No abstract

Background Despite women undergoing primary percutaneous coronary intervention (PPCI) having a higher rate of adverse outcomes than men, data evaluating prognostic risk scores, especially in elderly women, remains scarce. This study was conducted to validate the predictive value of Thrombolysis in Myocardial Infarction (TIMI) risk score in elderly female patients. Materials and methods This was a retrospective analysis of elderly (>65 years) female patients who underwent PPCI for ST-elevated myocardial infarction (STEMI) from October 2016 to September 2018. Patients’ demographic details and elements of TIMI risk score including age, co-morbidities, Killip classification; weight, anterior MI and total ischemic time were extracted from hospital records. The primary outcome was in-hospital mortality and post-discharge mortality reported on telephonic follow-up. Results A total of 404 elderly women with a median age of 70 years were included. The mean TIMI score was 5.25±1.45 with 40.3% (163) patients of TIMI score > 5. In-hospital mortality rate was 6.4% (26) and was found to be associated with TIMI score (p<0.001). The in-hospital mortality rate increased from 3.1% at TIMI score of 0–4 to 34.6% at the score of 8. On follow-up (16.43±7.40 months) of 211 (55.8%) patients, the overall mortality rate was 20.3%, and this was also associated with TIMI score (p<0.001). The mortality rate increased from 5.6% at the score of 0–4 to 54.5% at the score of 8. The predictive values (area under the curve) of TIMI risk score for in-hospital and post-discharge mortality were 0.709 (95% CI 0.591–0.827; p <0.001) and 0.689 (95% CI 0.608–0.770; p <0.001), respectively. Conclusion Increased adverse outcomes were observed with higher TIMI risk score for in hospital and post-discharge follow-up. Therefore, the prognostic TIMI risk score is a robust tool in predicting both in-hospital as well as post-discharge mortality in elderly females.

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.

Standardized case definitions are needed in decision-making regarding respiratory syncytial virus(RSV) control strategies, including vaccine evaluation. A syndromic case definition comprising of "wheeze or apnea or cyanosis" could be useful for community-based surveillance of moderate RSV infection among young infants particularly in resource-limited settings. However, this definition showed modest specificity (29.2-49.6%) indicating that community-based surveillance may need augmentation with other data.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO 2 in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user comfort improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in terms of average delay, indoor air quality, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy bill costs, reduce carbon emissions, mitigate peak loads, and improve user comfort.INDEX TERMS energy management controller, user-comfort, demand shifting, load scheduling, battery storage systems, demand response, solar energy, smart grid. NOMENCLATURE