Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine: Brussels, Belgium. 15-18 March 2016.

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Optimized data collection and analysis process for studying solar-thermal desalination by machine learning

An effective interdisciplinary study between machine learning and solar-thermal desalination requires a sufficiently large and well-analyzed experimental datasets. This study develops a modified dataset collection and analysis process for studying solar-thermal desalination by machine learning. Based on the optimized water condensation and collection process, the proposed experimental method collects over one thousand datasets, which is ten times more than the average number of datasets in previous works, by accelerating data collection and reducing the time by 83.3%. On the other hand, the effects of dataset features are investigated by using three different algorithms, including artificial neural networks, multiple linear regressions, and random forests. The investigation focuses on the effects of dataset size and range on prediction accuracy, factor importance ranking, and the model's generalization ability. The results demonstrate that a larger dataset can significantly improve prediction accuracy when using artificial neural networks and random forests. Additionally, the study highlights the significant impact of dataset size and range on ranking the importance of influence factors. Furthermore, the study reveals that the extrapolation data range significantly affects the extrapolation accuracy of artificial neural networks. Based on the results, massive dataset collection and analysis of dataset feature effects are important steps in an effective and consistent machine learning process flow for solar-thermal desalination, which can promote machine learning as a more general tool in the field of solar-thermal desalination

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

COVID-19 in New Orleans: A Nephrology Clinical and Education Perspective and Lessons Learned

Medicine and Health Sciences: Medical Specialties: Nephrolog

Experimental investigation of the effect of TDS on the thermal performance of the different types of solar stills

In regions with a restricted availability of drinkable water, solar stills offer a solution for a passive solar desalination method. In this study, three designs for solar stills are examined: the conventional solar still (CSS), the hemispheric solar still (HSS), and the pyramidal solar still (PSS). The experimental investigations were conducted over three consecutive days, thereby varying the total dissolved solids (TDS) concentrations in the basin water at 1000, 2000, and 3000 ppm. A comparative analysis focused on the performance of the various solar stills and the impact of TDS variation. The results revealed that the PSS consistently outperformed the CSS and HSS across all TDS levels. Notably, the PSS exhibited superior daily energy and exergy efficiencies. Furthermore, the daily productivity and energy efficiencies displayed an inverse relationship with the TDS concentration. A cost analysis indicated that the PSS achieved the lowest distilled water price, reaching a value of 0.0151 ＄/L. Specifically, the PSS exhibited a 17.3% and 3.5% higher accumulated daily productivity compared to CSS and HSS, respectively, at TDS = 1000. However, at TDS = 3000, the daily productivity of the PSS decreased by 3.5% and 7.5% compared to TDS = 2000 and 1000, respectively. Similarly, the energy efficiency of the PSS decreased by 5.27% and 8.67% as the TDS increased from 1000 to 3000. Notably, across all solar still types, the lowest cost values were consistently associated with the lowest TDS concentrations, with the PSS yielding the lowest cost overall

Increases in Heart Rate Variability Signal Improved Outcomes in Rapid Response Team Consultations: A Cohort Study

Background. Reduced heart rate variability (HRV) indicates dominance of the sympathetic system and a state of “physiologic stress.” We postulated that, in patients with critical illness, increases in HRV might signal successful resuscitation and improved prognosis. Methods. We carried out a prospective observational study of HRV on all patients referred to the rapid response team (RRT) and correlated with serial vital signs, lactate clearance, ICU admission, and mortality. Results. Ninety-one patients were studied. Significantly higher HRV was observed in patients who achieved physiological stability and did not need ICU admission: ASDNN 19 versus 34.5, p=0.032; rMSSD 13.5 versus 25, p=0.046; mean VLF 9.4 versus 17, p=0.021; mean LF 5.8 versus 12.4, p=0.018; and mean HF 4.7 versus 10.5, p=0.017. ROC curves confirmed the change in very low frequencies at 2 hours as a strong predictor for ICU admission with an AUC of 0.772 (95% CI 0.633, 0.911, p=0.001) and a cutoff value of −0.65 associated with a sensitivity of 78.6% and a specificity of 61%. Conclusions. Reduced HRV, specifically VLF, appears closely related to greater severity of critical illness, identifies unsuccessful resuscitation, and can be used to identify consultations that need early ICU admission