Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Online survey data of public subjective well-being on high occupancy vehicle lane in China

The data presented in this article are related to the research article entitled âOut-of-home activities, daily travel, and SWBâ (Ettema et al., 2010) [1]. The paper provides an online survey questionnaire and data about the public subjective well-being of high occupancy vehicle lanes in China. The survey data are made publicly available to extended analysis

Performance Evaluation of a Hydrogen-Based Clean Energy Hub with Electrolyzers as a Self-Regulating Demand Response Management Mechanism

Energy management of hybrid resources has become a critical issue in integrated energy system analysis. In this study, as a self-regulating demand response (DR) management mechanism, deferrable electrolyzers are used as a main controlled resource in a hydrogen-based clean energy hub (CEH), which includes a traditional generation plant (TGP), a low-carbon generation plant (LGP), and wind energy. Based on the hysteresis control model for aggregated electrolyzers, a comfort-constrained optimal energy state regulation (OESR) control strategy is implemented to model the deregulation feature of aggregated electrolyzers. The electrolyzers’ population can be integrated as a controlled efficient power plant (EPP) to provide the virtual spinning reserve for CEH. As a flexible and self-regulating participant, the electrolyzer-based EPP is integrated into the hybrid resource constrained optimization model; this reduces the total cost of CEH and carbon emissions and improves the integration of wind energy. Combined with TGP, LGP, and wind energy, the simulation results show that the deployment of aggregated electrolyzers on both the supply and demand sides of the CEH contributes to significant amounts of low-carbon hydrogen. The simulation also illustrates that the DR control strategy has a positive effect on active power and reserve re-dispatch

An energy-constrained state priority list model using deferrable electrolyzers as a load management mechanism

To reduce the consumption of fossil fuel and greenhouse gas (GHG) emissions, incentive-based policies are used to encourage end-users to utilize more clean energy. Hydrogen energy is an ideal clean energy that can be integrated into the next generation power grid. Deferrable electrolyzers (DEs), as a typical electricity-to-hydrogen conversion devices and capable of modulating power consumption, can convert excessive power to store electricity as hydrogen. Therefore it can be used as a method for load management. The main contribution of this paper is to propose an energy-constrained state priority list (ECSPL) model, for analyzing the charging response of aggregated loads consisting of DE units. The typical hysteresis control of DEs as a load management mechanism is first discussed. A characteristic parameter, i.e. the energy state of DE charging load, is used to group and prioritize the DE units. The proposed ECSPL model optimally determines the operating status of DE charging and standby process, and it maintains the user-desired DE charging trajectory considering customer-constraints. The proposed model maintains the diversity of operating status of DE charging and standby process to prevent unexpected synchronization phenomenon for operating status. To evaluate the performance of the proposed method, an estimated baseline of the aggregated DE charging loads is obtained based on natural hysteresis control. The ECSPL control method of DE units for intra-hour load balancing is then evaluated. The effects of different energy-constraints, deadbands of sampled end-use state comparison, error associated with the charging-trajectory measurements are modeled to evaluate the performance of controlled DE group. The ECSPL model is described and demonstrated by the modeling results of investigated DE units