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- Ruilope, LM, et al.
(författare)
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Design and Baseline Characteristics of the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease Trial
- 2019
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Ingår i: American journal of nephrology. - : S. Karger AG. - 1421-9670 .- 0250-8095. ; 50:5, s. 345-356
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Tidskriftsartikel (refereegranskat)abstract
- <b><i>Background:</i></b> 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. <b><i>Patients and</i></b> <b><i>Methods:</i></b> 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<sup>2</sup> 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 α = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. <b><i>Conclusions:</i></b> 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.
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| 3. |
- Liu, Q., et al.
(författare)
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Modeling interpretable social interactions for pedestrian trajectory
- 2024
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Ingår i: Transportation Research Part C. - : Elsevier Ltd. - 0968-090X .- 1879-2359. ; 162
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Tidskriftsartikel (refereegranskat)abstract
- The abilities to understand pedestrian social interaction behaviors and to predict their future trajectories are critical for road safety, traffic management and more broadly autonomous vehicles and robots. Social interactions are intuitively heterogeneous and dynamic over time and circumstances, making them hard to explain. In this paper, we creatively investigate modeling interpretable social interactions for pedestrian trajectory, which is not considered by the existing trajectory prediction research. Moreover, we propose a two-stage methodology for interaction modeling - “mode extraction” and “mode aggregation”, and develop a long short-term memory (LSTM)-based model for long-term trajectory prediction, which naturally takes into account multi-types of social interactions. Different from previous models that do not explain how pedestrians interact socially, we extract latent modes that represent social interaction types which scales to an arbitrary number of neighbors. Extensive experiments over two public datasets have been conducted. The quantitative and qualitative results demonstrate that our method is able to capture the multi-modality of human motion and achieve better performance under specific conditions. Its performance is also verified by the interpretation of predicted modes, of which the results are in accordance with common sense. Besides, we have performed sensitivity analysis on the crucial hyperparameters in our model. Code is available at: https://github.com/xiaoluban/Modeling-Interpretable-Social-Interactions-for-Pedestrian-Trajectory.
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| 7. |
- Wu, Y., et al.
(författare)
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A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network
- 2022
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Ingår i: Renewable energy. - : Elsevier Ltd. - 0960-1481 .- 1879-0682. ; 185, s. 302-320
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Tidskriftsartikel (refereegranskat)abstract
- Offshore wind farms are increasingly becoming the focus of clean sources market because of the huge energy potential and fast-maturing technology. The existing researches normally optimize the wind turbine layout and two-dimensional cable routing independently. This work focuses on the synchronization optimization of site selection of the offshore wind farm, three-dimensional wind turbine layout and three-dimensional cable network routing based on meta-heuristic algorithms and geographic information systems. Several practical issues, i.e., restricted areas, power generation, cable network and energy loss, are taken into consideration. A two-layer model is proposed. The outer layer model is for the site selection and the wind turbine layout optimization. The inner layer model is for the obstacle-avoiding cable routing optimization. In this stage, the seabed terrain is considered for the first time. The proposed integrated model is complex and non-convex. Thus, a hybrid method including an improved ant colony optimization combined with genetic algorithm, dual-simplex method and Kruskal algorithm is proposed to search the solution more efficiently. The initialization stage of the hybrid method is improved from random assignment to directional assignment. The directional solution is obtained by the widely used genetic algorithm. A case study based on a real offshore wind farm is established to prove the effectiveness of the proposed methodology. The results show an over one million dollars increase in annual benefit compared with conventional methods.
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