Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models.

Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska Byråns Vattenbalansavdelning, and NRECA: Non Recorded Catchment Areas) and seven hybrid models based on three artificial intelligence (AI) methods (adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and group method of data handling (GMDH)) in simulating streamflow in four river basins in Indonesia. HBV and NRECA were developed based on precipitation data. Various combinations of 1-month lagged precipitation data together with outputs of HBV and NRECA were used for developing ANFIS and SVM models, and the best results of ANFIS and SVM formed the inputs to GMDH. Results showed that AI-based hybrid models have generally led to more accurate streamflow estimates compared with HBV and NRECA, and the GMDH model had the best performance at Cipero, Kedungdowo, Notog, and Sukowati stations, with RMSEs of 12.21, 6.07, 20.35, and 24.2 m3 s-1, respectively. More accurate estimation of peak values in training set at Cipero and Sukowati stations, and in both training and testing sets at Kedungdowo station was another advantage of GMDH. Hybrid models based on AI methods can be suitable alternatives to hydrological models, particularly in watersheds where there is a lack of measured data (e.g. climatic parameters, land cover-plant growth data, soil data, stream conditions, and properties of groundwater aquifers), provided that appropriate inputs are used.

Human immunodeficiency virus and mortality from coronavirus disease 2019: A systematic review and meta-analysis.

BACKGROUND: Persons living with human immunodeficiency virus (PLWH) constitute a vulnerable population in view of their impaired immune status. At this time, the full interaction between HIV and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been incompletely described. OBJECTIVE: The purpose of this study was to explore the impact of HIV and SARS-CoV-2 co-infection on mortality. METHOD: We systematically searched PubMed and the Europe PMC databases up to 19 January 2021, using specific keywords related to our aims. All published articles on coronavirus disease 2019 (COVID-19) and HIV were retrieved. The quality of the studies was evaluated using the Newcastle-Ottawa Scale for observational studies. Statistical analysis was performed with Review Manager version 5.4 and Comprehensive Meta-Analysis version 3 software. RESULTS: A total of 28 studies including 18 255 040 COVID-19 patients were assessed in this meta-analysis. Overall, HIV was associated with a higher mortality from COVID-19 on random-effects modelling {odds ratio [OR] = 1.19 [95% confidence interval (CI) = 1.01-1.39], p = 0.03; I 2 = 72%}. Meta-regression confirmed that this association was not influenced by age (p = 0.208), CD4 cell count (p = 0.353) or the presence of antiretroviral therapy (ART) (p = 0.647). Further subgroup analysis indicated that the association was only statistically significant in studies from Africa (OR = 1.13, p = 0.004) and the United States (OR = 1.30, p = 0.006). CONCLUSION: Whilst all persons ought to receive a SARS-CoV-2 vaccine, PLWH should be prioritised to minimise the risk of death because of COVID-19. The presence of HIV should be regarded as an important risk factor for future risk stratification of COVID-19.

Parkinson's disease may worsen outcomes from coronavirus disease 2019 (COVID-19) pneumonia in hospitalized patients: A systematic review, meta-analysis, and meta-regression.

BACKGROUND: Parkinson's Disease (PD) is among one of the common comorbidities in older patients. People with PD may be more vulnerable to severe pneumonia, due to the impairment of pulmonary function. Currently, the association between PD and COVID-19 is not yet established. This study aims to analyze the relationship between PD and in-hospital outcomes of COVID-19. MATERIALS AND METHODS: We systematically searched the PubMed and Europe PMC database using specific keywords related to our aims until December 25th, 2020. All articles published on COVID-19 and Parkinson's Disease were retrieved. The quality of the study was assessed using the Newcastle Ottawa Scale (NOS) tool for observational studies and Joanna Briggs Institute (JBI) Critical Appraisal Tools for cross-sectional studies. Statistical analysis was done using Review Manager 5.4 software. RESULTS: A total of 12 studies with 103,874 COVID-19 patients were included in this meta-analysis. This meta-analysis showed that Parkinson's Disease was associated with poor in-hospital outcomes [[OR 2.64 (95% CI 1.75-3.99), p < 0.00001, I2 = 81%] and its subgroup which comprised of severe COVID-19 [OR 2.61 (95% CI 1.98-3.43), p < 0.00001, I2 = 0%] and mortality from COVID-19 [RR 2.63 (95% CI 1.50-4.60), p = 0.0007, I2 = 91%]. Meta-regression showed that the association was influenced by age (p = 0.05), but not by gender (p = 0.46) and dementia (p = 0.23). CONCLUSIONS: Extra care and close monitoring should be provided to Parkinson's Disease patients to minimize the risk of infections, preventing the development of severe and mortality outcomes.

Predictors and outcomes of prolonged ventilation after coronary artery bypass graft surgery.

This study investigated and compared the risk factors and outcomes of patients undergoing coronary artery bypass graft surgery with and without the occurrence of prolonged mechanical ventilation. Data in a cardiac surgery database were examined retrospectively. Data selected included any isolated coronary artery bypass graft surgery performed by the surgical group from August 2005 to June 2009. The resulting cohort included a total of 2933 patients which was comprised of 116 patients with a ventilation time of greater than 72 hours (prolonged ventilation) and 2817 patients with a ventilation time of 72 hours or less (no prolonged ventilation). Patients with a prolonged ventilation time were matched (1:3 ratio) to patients not requiring a prolonged ventilation time by year of surgery resulting in our study cohort of 464 patients. To generate the unadjusted risks of each factor, χ(2) and t test analysis were performed. Logistic regression analysis was then used to investigate the adjusted risk between cases and controls and each of the significant variables. χ(2) and t tests were conducted comparing cases and controls with the outcome variables. Patients undergoing coronary artery bypass graft that experienced a prolonged ventilation time (cases) were more likely female, had a New York Hospital Association functional class of III or IV, and had a longer perfusion time. There was no significant difference between cases and controls with diabetes, chronic obstructive pulmonary disease, left ventricular ejection fraction, or body mass index while controlling for all significant risk factors. Careful patient selection and preparation during preoperative evaluation may help identify patients at risk for prolonged mechanical ventilation and thus help prevent the added morbidity and mortality associated with it.

Thin-film passive microcomponents for improved circuit performance.

Today's electromedical applications demand miniaturisation, integration and improved signal integrity. This article describes techniques to help designers meet these objectives.