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Background: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds.Methods: We conducted a single-centre prospective before-and-after mixed-method trial in a 35 bed medical and surgical ICU. Daily ICU ward rounds were observed during two periods of 8 weeks. We compared paper checklists (control) with a dynamic (digital) clinical checklist (DCC, intervention). The primary outcome was compliance with best clinical practice, measured as the percentages of checked items and unchecked critical items. Secondary outcomes included ICU stay and the usability of digital checklists. Data are presented as median (interquartile range).Results: Clinical characteristics and severity of critical illness were similar during both control and intervention periods of study. A total of 36 clinicians visited 197 patients during 352 ward rounds using the paper checklist, compared with 211 patients during 366 ward rounds using the DCC. Per ICU round, a median of 100% of items (94.4-100.0) were completed by DCC, compared with 75.1% (66.7-86.4) by paper checklist (P=0.03). No critical items remained unchecked by the DCC, compared with 15.4% (8.3-27.3) by the paper checklist (P=0.01). The DCC was associated with reduced ICU stay (1 day [1-3]), compared with the paper checklist (2 days [1-4]; P=0.05). Usability of the DCC was judged by clinicians to require further improvement.Conclusions: A digital checklist improved compliance with best clinical practice, compared with a paper checklist, during ward rounds on a mixed ICU.Clinical Trial Registration: NCT03599856. [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components. [ABSTRACT FROM AUTHOR]

Several cities in the developing world, of which the capital city of India, New Delhi, is an example, often experience air quality in which pollutant levels go way above the levels considered hazardous for human health. To bring down the air quality to within permissible limits quickly, the measures typically taken involve shutting down certain high-polluting activities for some time to enable the air quality to recover temporarily. This paper presents a first-ever model based on artificial neural networks to forecast the extent of reduction in air quality parameters that can be achieved and the time period within which a change can be experienced when the source of the emissions is cut off temporarily. The model is based on the extensive data on the extent of reduction in air quality parameters that occurred during the lockdown that was imposed during the COVID-19 pandemic. The non-linear autoregressive exogenous network-based model chosen for the purpose employs the hour since stopping of emissions, relative humidity, wind speed, wind direction, and ambient temperature as input parameters to predict the rate of change of PM 2.5 with respect to the concentration at the start of the stopping of the emissions. Air quality data from a key monitoring station in New Delhi was used to develop the model. The model predicted the rate of drop in PM 2.5 with an R and MSE of 0.0044 and 0.9736, respectively, while training and 0.0095 and 0.9583 while testing. The model was then tested with data from 19 other stations in New Delhi, and accuracy of the model was found to be exceptionally accurate, with the correlation between the measured and the predicted PM 2.5 levels ranging from 0.74 to 0.94 and the MSE ranging from 0.0110 to 1.0746. Thus, the model can be employed to determine the number of hours of temporary stoppage of emissions required for the PM 2.5 concentration to reach safe levels. The methodology of development of the model can be extrapolated to construct models tailored for use in other parts of the world as well. • A novel ANN -based model is reported to predict rate of drop of PM 2.5 • Model has been trained on extensive dataset from the covid lockdown in Delhi. • Duration and efficacy of emergency measures to reduce emissions can be estimated. • The model uses easy-to-measure meteorological parameters as inputs. • Model has been validated for 19 stations of various land-use types in Delhi. [ABSTRACT FROM AUTHOR]