The rising death burden of atrial fibrillation and flutter in low-income regions and younger populations

ObjectiveThe aim of the study was to depict the global death burden of atrial fibrillation and/or flutter (AFF) between 1990 and 2019 and predict this burden in the next decade.MethodsWe retrieved annual death data on cases and rates of AFF between 1990 and 2019 from the Global Burden of Disease (GBD) Study 2019 and projected the trends for 2020–2029 by developing the Bayesian age-period-cohort model.ResultsThe global number of deaths from AFF increased from 117,038.00 in 1990 to 315,336.80 in 2019. This number is projected to reach 404,593.40 by 2029. The age-standardized mortality rates (ASMRs) of AFF have increased significantly in low- to middle-sociodemographic index (SDI) regions, which will surpass that in high SDI regions and reach above 4.60 per 100,000 by 2029. Globally, women have a higher ASMR than men, which is largely attributed to disproportionately higher mortality in women than men in lower SDI regions. Notably, AFF-related premature mortality continues to worsen worldwide. A pandemic of high systolic blood pressure and high body mass index (BMI) largely contributes to AFF-associated death. In particular, low- to middle-SDI regions and younger populations are increasingly affected by the rapidly growing current and future risk of high BMI.ConclusionThe global death burden of AFF in low-income countries and younger generations have not been sufficiently controlled in the past and will continue growing in the future, which is largely attributed to metabolic risks, particularly for high BMI. There is an urgent need to implement effective measures to control AFF-related mortality

GM(1,<i>N</i>)model-based prediction of carbon steel corrosion rate

[Objectives] The corrosion rate prediction of carbon steel in marine environment is very complicated and uncertain. [Methods] To improve the accuracy of prediction model in view of the low precision of grey prediction model for corrosion rate of carbon steel at present stage, the key factors which affect the corrosion rate can be concluded from the grey theory analysis of marine environment and corrosion rate of carbon steel, and then the GM(1，N） model which can predict the corrosion rate of carbon steel is established. [Results] According to the case analysis, the main factors that affect the corrosion rate in seaareas of Qindao, Xiamen, Zhousan, Yulin coastal region are seawater temperature, biofouling, pH value and salinity, and based on the above, the establishment of GM(1,5) model possesses higher precision and less computational costs. [Conclusions] The research shows that the GM(1，N） model can predict the corrosion rate of carbon steel effectively, and also provide a theoretical basis for the prediction of residual life of carbon steel

Transformer graded fault diagnosis based on neighborhood rough set and XGBoost

Aiming at the uncertainty of fault type reasoning based on fault data in transformer fault diagnosis model, this paper proposed a hierarchical diagnosis model based on neighborhood rough set and XGBoost. The model used arctangent transformation to preprocess the DGA data, which could reduce the distribution span of data features and the complexity of model training. Using 5 characteristic gases and 16 gas ratios as the input characteristic parameters of the XGBoost model at all levels, reduction was performed on these 21 input feature attributes, features that had a high contribution to fault classification were retained, and redundant features were removed to improve the accuracy and efficiency of model prediction. Taking advantage of XGBoost's strong ability to extract a few features, the output of the model was the superposition of leaf node scores for each type of fault, the maximum score was the type of failure the sample belonged to, and its value was also the probability value. The obtained probability was used as one of the evidence sources to use D-S evidence theory for information fusion to verify the reliability of the model. Experiments have proved that the XGBoost graded diagnosis model proposed in this article has the highest overall accuracy rate comparing with the traditional model, reaching 93.01%, the accuracy of XGBoost models at all levels has reached more than 90%, the average accuracy rate is higher than that of the traditional model by an average of more than 2.7%, and the average time-consuming is only 0.0695 s. After D-S multi-source information fusion, the reliability of the prediction results of the model proposed in this paper has been improved

Auction-based profit maximization offloading in mobile edge computing

Offloading Mobile Devices (MDs) computation tasks to Edge Nodes (ENs) is a promising solution to overcome computation and energy resources limitations of MDs. However, there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD profit. In this paper, we propose an auction-based computation offloading algorithm, inspiring ENs to provide high-quality service by maximizing the profit of ENs. Firstly, a novel cooperation auction framework is designed to avoid overall profit damage of ENs, which is derived from the high computation delay at the overloaded ENs. Thereafter, the bidding willingness of each MD in every round of auction is determined to ensure MD rationality. Furthermore, we put forward a payment rule for the pre-selected winner to effectively guarantee auction truthfulness. Finally, the auction-based profit maximization offloading algorithm is proposed, and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the auction. Numerical results verify the performance of the proposed algorithm. Compared with the VA algorithm, the ENs profit is increased by 23.8%, and the task discard ratio is decreased by 7.5%

Grape seed proanthocyanidin improves lipopolysaccharide-induced myocardial toxicity in mice

Lipopolysaccharide (LPS) is an important pathogenic factor for sepsis which results in cardiovascular diseases and even mortality. Proanthocyanidin, one of the main components in grape seed, has a wide range of biological activities in various diseases. The mouse model was established by intraperitoneal injection with LPS. Grape seed proanthocyanidin (GSP) was administered continuously for 8 days. Our results showed that pre-treatment of GSP dramatically ameliorated the level of creatine kinase (CK) and lactate dehydrogenase (LDH) to defend against LPS-induced myocardial toxicity (p<0.01). Moreover, pre-treatment of GSP significantly mitigated the expression of matrix metallopeptidase 2 (MMP-2) and matrix metallopeptidase 9 (MMP-9) to inhibit LPS-induced myocardial fibrosis (p<0.01). In addition, pre-treatment of GSP significantly increased superoxide dismutase (SOD) and catalase (CAT) activities to improve the level of malondialdehyde (MDA) and hydrogen peroxide (H2 O2 ) (p<0.01). Lastly, pre-treatment of GSP relieved tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) activities to prevent inflammatory responses (p<0.01)

Investigation into electrochemical performance of NiO/graphene composite nanofibers synthesized by a simple method as anode materials for high-performance lithium ion batteries

The NiO/graphene (NiO/G) composite nanofibers were successfully synthesized by simple electrospinning followed by heat treatment. They as anode materials for lithium ion batteries demonstrated the more outstanding electrochemical performance when compared with the NiO + Ni composite nanofibers as the reference. NiO/G exhibited a higher discharging/charging capacity (about 712 mAh·g ^−1 at the third cycle) with a coulombic efficiency of nearly 100% than NiO + Ni (547 mAh·g ^−1 ). NiO/G also demonstrated the excellent cycling stability due to its higher discharging capacity of 571 mAh·g ^−1 and retention rate of 78% than NiO + Ni (184 mAh·g ^−1 and 33%) when subject to 50 cycles at 100 mA·g ^−1 . Moreover, its rate performance was also greatly improved when compared with NiO + Ni owing to its higher discharging capacity (305 mAh·g ^−1 , 556 mAh·g ^−1 ) and retention rate (44%, 80%) at the current density increased from 100 mA·g ^−1 to 2000 mA·g ^−1 , and then recovered to 100 mA·g ^−1 . The outstanding electrochemical performance of the NiO/G electrode is closely related to its lower ohmic resistance (2.1 Ω)/charge transfer resistance (86.5 Ω), and stronger diffusion capability of Li ^+ resulting from the high specific surface area, excellent conductivity and a certain charge storage capacity of graphene

Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.</p

In Vitro Digestion and Fecal Fermentation of Polysaccharides from Hawthorn and Its Impacts on Human Gut Microbiota

Polysaccharides are biological macromolecules that are difficult to absorb into intestinal epithelial cells for exerting activities, whereas the interaction between polysaccharides and gut microbiota might be an alternative method. This study aimed to explore the in vitro digestion of hawthorn polysaccharides (HPS) and their interaction with the gut microbiota. Results showed that the content of reducing sugars increased slightly during gastric digestion. However, no free monosaccharide was detected during the whole simulated digestion process, indicating that HPS was indigestible. The total carbohydrate residue decreased during in vitro fermentation. This result was due to the utilization by the gut microbiota. Meanwhile, short-chain fatty acids were produced due to the utilization of HPS. Notably, HPS could significantly modulate the composition of human gut microbiota; in particular, the relative abundances of Megasphaera, Acidaminococcus and Mitsuokella increased, whereas the relative abundances of Escherichia Shigella and Fusobacterium decreased. It was suggested that HPS could decrease the abundances of harmful intestinal microbiota and regulate the proportion of beneficial bacteria in the intestinal tract. Overall, the beneficial effects of HPS were believed to be related to the gut microbiota and could be used as a potential dietary supplement

CO2 derived nanoporous carbons for carbon capture

Nanoporous carbons (NPCs) were synthesized from CO2 with magnesiothermic reduction for CO2 capture applications. The yield of NPCs was enhanced by a modified magnesiothermic reduction process, in which CO2 instead of Ar was applied to the reactor during heating Mg powders from 500 °C to a given reaction temperature (800–900 °C). The yields, microstructures, pore structures and CO2 adsorption properties were investigated for a variety of NPCs synthesized under various conditions of reaction temperature and duration (15–60 min). The results show that the synthesized NPCs are mainly mesoporous and composed of well crystalline carbon nanosheets interwoven with amorphous carbon. The yield, BET surface area, total pore volume, narrow microporosity (pore size < 1 nm) and CO2 adsorption capacity were decreased with an increase of reaction temperature and duration. The highest CO2 uptake of 39.7 mg g−1 at 273 K and 1 bar was obtained in the NPC that was synthesized at 800 °C for 15 min and with flowing CO2 during heating at above 500 °C. The high CO2 adsorption capacity is resulted from its large surface area and volume of narrow micropores smaller than 1 nm, being of 34.9 m2 g−1 and 0.011 cm³ g−1, respectively