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Aims: The study sought to investigate the association between admission systolic blood pressure (SBP) and 1-year clinical outcomes in patients hospitalized for heart failure (HF) and in subgroups. Methods: This study was based on the China Patient-centred Evaluative Assessment of Cardiac Events Prospective Heart Failure Study, which prospectively enrolled patients hospitalized for HF in 52 hospitals from 20 provinces in China between August 2016 and May 2018. Patients were divided into four groups according to the quartiles of SBP at admission. The multivariable Cox proportional hazards regression models were fitted to examine the association between admission SBP and all-cause death and HF readmission within 1 year after the index hospitalization. Restricted cubic splines were used to explore the non-linear association between SBP and the clinical outcomes. Results: Among 4896 patients, those with lower admission SBP were younger, more likely to be male, have left ventricular ejection fraction <40%, and receive β-blockers, aldosterone antagonists, and diuretics. After adjustment for potential confounders, lower admission SBP was significantly associated with higher all-cause death and there is no threshold, while we only observed such an association with HF readmission when admission SBP was lower than 120 mmHg. Compared with the 4th SBP quartile, patients in the 1st SBP quartile had higher risk of all-cause death (hazard ratio, 1.85; 95% confidence interval 1.48–2.33; P < 0.001) and HF readmission (hazard ratio, 1.40; 95% confidence interval 1.19–1.65, P < 0.001). These associations were consistent in most subgroups, such as age, sex, and left ventricular ejection fraction. Conclusions: In patients hospitalized for HF, lower admission SBP portends an increased risk of 1 year all-cause death and HF readmission, and these associations were consistent among subgroups. [ABSTRACT FROM AUTHOR]

SmNiO 3 is a representative quantum material exhibiting the antidoping behavior, where the conductivity of the material is reduced rather than increased by electron doping. Recent experimental and theoretical works have demonstrated a phase transition of SmNiO 3 with large conductance changes via chemical methods. However, the effect of electron doping via interfacial charge transfer in SmNiO 3 is much less studied. In this work, the first-principles density functional theory (DFT)+ U method is employed to investigate the SmNiO 3 /YTiO 3 superlattice, in which the YTiO 3 layer acts as the electron donor. Compared with the chemical doping in SmNiO 3 , several interesting physical phenomena have been predicted in SmNiO 3 /YTiO 3 superlattices due to the lattice and electronic reconstructions. First, at a doping concentration of 1 e − per Ni, i.e., (SmNiO 3) 1 /(YTiO 3) 1 superlattice, all Ni 3 + are converted to Ni 2 + , resulting in a Mott-insulating phase, similar to the chemical doping in the pristine material. Interestingly, such a Mott gap can be efficiently modulated by tuning the stacking orientation. Second, at a doping concentration of 1 2 e − per Ni, i.e., [ 001 ]-orientated (SmNiO 3) 2 /(YTiO 3) 1 superlattice, the electronic structure associated with charge ordering depends on the concrete magnetic order, giving rise to magnetism-dependent electronic behavior. In addition, as the doping concentration further decreases (i.e., a doping concentration of 1 3 e − /Ni), a metallic state is predicted in a [ 001 ]-orientated (SmNiO 3) 3 /(YTiO 3) 1 superlattice, which is quite different from the case of chemical doping. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to investigate the prevalence, risk factors and prognostic implications of cognitive impairment in young and middle‐aged patients with acute heart failure (HF). Methods: In a prospective cohort of patients with acute HF, we assessed cognitive function by the Mini‐Cog, predictors of the cognitive impairment and its associations with 30 day and 1 year cardiovascular death or HF rehospitalization among young and middle‐aged patients (<65 years old). Results: Among 1958 young and middle‐aged patients, the prevalence of cognitive impairment was 19.6%. Predictors of cognitive impairment included older age, females, lower education levels and prior strokes. Compared with patients having normal cognitive function, cognitive impairment was associated with a higher risk of 30 day cardiovascular death or HF rehospitalization [hazard ratio (HR), 1.52, 95% confidence interval (CI), 1.07–2.17, P = 0.02], but not for 1 year cardiovascular death or HF rehospitalization (HR, 1.06, 95% CI, 0.87–1.30, P = 0.55). Conclusions: Cognitive impairment is present in a notable proportion of young and middle‐aged patients with acute HF and is associated with an increased risk of short‐term adverse outcomes. Strategies for screening and intervention for cognitive impairment at a younger age are necessary, particularly for those at high risk. [ABSTRACT FROM AUTHOR]

In recent years, graph similarity algorithms have been extensively developed based on neural networks. However, with an increase in the node count in graphs, these models either suffer from a reduced representation ability or face a significant increase in the computational cost. To address this issue, a graph similarity algorithm based on graph partitioning and attention mechanisms was proposed in this study. Our method first divided each input graph into the subgraphs to directly extract the local structural features. The residual graph convolution and multihead self-attention mechanisms were employed to generate node embeddings for each subgraph, extract the feature information from the nodes, and regenerate the subgraph embeddings using varying attention weights. Initially, rough cosine similarity calculations were performed on all subgraph pairs from the two sets of subgraphs, with highly similar pairs selected for precise similarity computation. These results were then integrated into the similarity score for the input graph. The experimental results indicated that the proposed learning algorithm outperformed the traditional algorithms and similar computing models in terms of graph similarity computation performance. [ABSTRACT FROM AUTHOR]

Based on the first‐principles calculation and grand canonical Monte Carlo (GCMC) simulation, the reversible hydrogen storage of Na‐decorated silicon boron (SiB) and C‐doped SiB monolayers have been investigated. For double side 4Na‐decorated SiB monolayer, each Na atom can adsorb five H2 molecules with the average adsorption energy of −0.18 eV/H2, yielding an H2 gravimetric density of 9.09 wt% and desorption temperature TD of 227 K. To further improve the H2 gravimetric density and desorption temperature, doping effect was employed to enhance the internal electric field between Na atom and SiB substrate. When two Si atoms in the SiB monolayer were replaced by C atoms, the adsorption energy for H2 was significantly improved. The H2 gravimetric density reaches to 10.63 wt% with the average adsorption energy of −0.22 eV/H2. Meanwhile, the desorption temperature TD was raised from 227 to 282 K, reaching the ideal condition near room temperature. In addition, the GCMC simulations further confirm that the H2 gravimetric density can fully meet the latest hydrogen storage target (5.5 wt%) for both Na‐decorated SiB monolayer and Na‐decorated C‐doped SiB monolayer. Our results indicate that both Na‐modified SiB and C‐doped SiB monolayers can be used as promising materials for reversible hydrogen storage. [ABSTRACT FROM AUTHOR]

Aiming to aid Electric-Power Industry to accurately understand users, hybrid learning model based ensemble learning algorithms for recognizing user to be sensitive to electric charge is proposed in this paper. On the basis of big data presented by CCF competition sponsor in China, with some excellent technology or algorithm such as JieBa, SFFS, etc., we extract many key features from data set and successfully draw a portrait for users who pay close attention to electric charge. Furthermore, machine learning algorithms and the strategy selection model related to them are investigated. The feasibility that hybrid learning model combining several ensemble learning algorithms can substantially improve classification accuracy are proved from theoretical level. Then the details of implementing hybrid learning model are described in the paper. Lastly, the hybrid learning model named Stacking is achieved, which yields better performance in contrast to the state-of-the-art competitors. The experimental results indicate that Stacking has both high precision and recall with 0.8 and 0.85 respectively. Furthermore the F1 score of Stacking evaluation is 0.823. [ABSTRACT FROM AUTHOR]