Search

This paper studies a generalized distributed optimization problem for second-order multi-agent systems (MASs) over directed networks. Firstly, an improved distributed continuous-time algorithm is proposed. By using the linear transformation method and Lyapunov stability theory, some conditions are obtained to guarantee all agents' states asymptotically reach the optimal solution. Secondly, to reduce unnecessary communication transmission and control cost, an event-triggered algorithm is designed. Moreover, the convergence of the algorithm is proved, and the Zeno behavior can be avoided based on strict theoretic analysis. Finally, one example is given to verify the good performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time distributed optimisation problem for multi-agent systems (MASs) is investigated by proposing a kind of new event-based aperiodically intermittent communication strategy. Firstly, the distributed optimisation problem with the sum of local objective functions is considered. A novel finite-time event-triggered intermittent control protocol is proposed over undirected networks, and some sufficient conditions are obtained to ensure the finite-time consensus of MASs and the asymptotical solvability of the optimisation problem. Secondly, a more general distributed optimisation problem, in which the optimisation objective is the convex combination of local objective functions, is considered and we also prove that MASs can achieve consensus in finite-time and asymptotically reach the optimal solution under the proposed protocol over directed networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This study explores the disparities in older adults' self-rated health within the urban landscape of China. Drawing on the 1% national population survey of China in 2015, it highlights how variations in city development contribute to geographical health disparities among older residents. In the era of the decentralized fiscal system, a crucial mechanism identified is the role of cities' local fiscal revenue in connecting their socioeconomic development and the health status among older adults. Despite efforts by cities in lower socioeconomic positions to increase fiscal expenditure and address deficits through central transfer payments, they prove inadequate in effectively mitigating population health disparities. The prioritization of economic growth and neglect of public service provision responsibilities are fundamental causes within this fiscal framework. The findings underscore the urgent need for increased central transfer payments in public services to address the growing disparities in older adults' health. • Revealing health disparity among urban older adults amidst fiscal decentralization. • Local fiscal revenue connecting cities' development and individual health. • Ineffectiveness of local fiscal expenditure and deficit in alleviating disparities. • Urgency for leveraging public transfer payments toward public health services. [ABSTRACT FROM AUTHOR]

Rechargeable lithium‐oxygen (Li‐O2) batteries with high theoretical energy density are regarded as a promising candidate for the booming electric vehicle manufacturing industry, whereas their commercial application is hindered due to poor cyclic stability caused by the sluggish kinetics and inevitable cathode passivation. Herein, a synergistic working mechanism of Li‐O2 battery is initiated by implementing well‐designed hollow carbon shells with hierarchically porous hollow structure (HCS) assisted by dual redox mediators (RMs) simultaneously, so as to systemically address issues of diminished discharge capacity and reaction kinetics. The hierarchically porous structure of HCS constructs more space to accommodate Li2O2, increases the accessibility of active sites, and enhances discharge capacity. More importantly, the dual RMs regulate the growth mechanism of Li2O2 in surface‐mediated mode, resulting in an easily decomposed film‐like Li2O2 with higher electron transport capacity and improving the reaction kinetics. As a result of the synergistic effort, an HCS‐based Li‐O2 battery with dual RMs manifests a total discharge capacity of 24580 mA h g−1 and a prolonged cycle life of 2500 h at the current density of 100 mA g−1. This design philosophy will open up a new way to optimize and upgrade the cathode materials of Li‐O2 batteries. [ABSTRACT FROM AUTHOR]

The classification of diabetic retinopathy (DR) aims to utilize the implicit information in images for early diagnosis, to prevent and mitigate the further worsening of the condition. However, existing methods are often limited by the need to operate within large, annotated datasets to show significant advantages. Additionally, the number of samples for different categories within the dataset needs to be evenly distributed, because the characteristic of sample imbalance distribution can lead to an excessive focus on high-frequency disease categories, while neglecting the less common but equally important disease categories. Therefore, there is an urgent need to develop a new classification method that can effectively alleviate the issue of sample distribution imbalance, thereby enhancing the accuracy of diabetic retinopathy classification. In this work, we propose MediDRNet, a dual-branch network model based on prototypical contrastive learning. This model adopts prototype contrastive learning, creating prototypes for different levels of lesions, ensuring they represent the core features of each lesion level. It classifies by comparing the similarity between data points and their category prototypes. Our dual-branch network structure effectively resolves the issue of category imbalance and improves classification accuracy by emphasizing subtle differences in retinal lesions. Moreover, our approach combines a dual-branch network with specific lesion-level prototypes for core feature representation and incorporates the convolutional block attention module for enhanced lesion feature identification. Our experiments using both the Kaggle and UWF classification datasets have demonstrated that MediDRNet exhibits exceptional performance compared to other advanced models in the industry, especially on the UWF DR classification dataset where it achieved state-of-the-art performance across all metrics. On the Kaggle DR classification dataset, it achieved the highest average classification accuracy (0.6327) and Macro-F1 score (0.6361). Particularly in the classification tasks for minority categories of diabetic retinopathy on the Kaggle dataset (Grades 1, 2, 3, and 4), the model reached high classification accuracies of 58.08%, 55.32%, 69.73%, and 90.21%, respectively. In the ablation study, the MediDRNet model proved to be more effective in feature extraction from diabetic retinal fundus images compared to other feature extraction methods. This study employed prototype contrastive learning and bidirectional branch learning strategies, successfully constructing a grading system for diabetic retinopathy lesions within imbalanced diabetic retinopathy datasets. Through a dual-branch network, the feature learning branch effectively facilitated a smooth transition of features from the grading network to the classification learning branch, accurately identifying minority sample categories. This method not only effectively resolved the issue of sample imbalance but also provided strong support for the precise grading and early diagnosis of diabetic retinopathy in clinical applications, showcasing exceptional performance in handling complex diabetic retinopathy datasets. Moreover, this research significantly improved the efficiency of prevention and management of disease progression in diabetic retinopathy patients within medical practice. We encourage the use and modification of our code, which is publicly accessible on GitHub: https://github.com/ReinforceLove/MediDRNet. • Addresses class imbalance in the DR dataset. • Optimized vascular feature extraction for DR. • Achieved top results in ACA and Macro-F1 for DR classification. [ABSTRACT FROM AUTHOR]

Highlights • ZmUBP15 , ZmUBP16 and ZmUBP19 are specifically in response to Cd and salt stress in maize. • ZmUBP15, ZmUBP16 and ZmUBP19 are conserved in maize and localized in cell membrane. • Over-expression of ZmUBP15 , ZmUBP16 or ZmUBP19 partially rescue ub p16 -1 tolerance to Cd and salt stress in Arabidopsis. • Over-expression of ZmUBP15 , ZmUBP16 or ZmUBP19 enhance Arabidopsis tolerance to Cd stress and salt stress. Abstract Ubiquitin-Specific Protease16 (UBP16) has been described involved in cadmium stress and salt stress in Arabidopsis , however nothing is known about the functions of its homologs in maize. In this study, we investigate the functions of ZmUBP15 , ZmUBP16 and ZmUBP19 , three Arabidopsis UBP16 homologs in maize. Our results indicate that ZmUBP15 , ZmUBP16 and ZmUBP19 are ubiquitously expressed throughout plant development, and ZmUBP15, ZmUBP16 and ZmUBP19 proteins are mainly localized in plasma membrane. Complementation analyses show that over-expression of ZmUBP15 or ZmUBP16 can rescue the defective phenotype of ub p16 -1 in cadmium stress. In addition, over-expression of ZmUBP15 , ZmUBP16 or ZmUBP19 can increase the plant tolerance to cadmium stress. These results indicate that ZmUBP15 , ZmUBP16 and ZmUBP19 are required for plant to tolerance the cadmium stress. Consistent with this point, cadmium-related genes are markedly up-regulated in seedlings over-expressing ZmUBP15 , ZmUBP16 or ZmUBP19. Furthermore, our data indicate that ZmUBP15 , ZmUBP16 and ZmUBP19 partially rescue the salt-stress phenotype of ub p16 -1. Thus, our research uncover the functions of three novel maize proteins, ZmUBP15, ZmUBP16 and ZmUBP19, which are required for plants in response to cadmium stress and salt stress. [ABSTRACT FROM AUTHOR]

The inactive lithium generated by the formation of solid electrolyte interface (SEI) and "dead lithium", and the side reaction of Li with the reactive 3D framework, lead to the low coulombic efficiency (CE) and short cycle life of metal Li anode. In this work, a 3D porous biochar modified by silver nanoparticles (PB-Ag) with high lithiophilic and chemically stable surface is designed as a host for Li anode, achieving no dead lithium generation, small SEI area and minimized side reaction between lithium and the frame surface. Since the paths of inactive lithium production are systematically blocked, this composite lithium anode with 3D frame exhibits a high cycle CE. Characterizations of the PB-Ag show that selectively oxidative decomposition of the active group (-COOH) on the surface and loading of highly dispersed silver nanoparticles are achieved simultaneously through a simple and mild one-step liquid phase reaction. Electrochemical tests demonstrate that the PB-Ag possesses an excellent lithiophilicity, and maintains almost 100% CE and very low mass-transfer polarization potential (<20 mV) after 200 cycles. Moreover, the deposition process of lithium on PB-Ag is investigated in detail by ex-situ SEM. [ABSTRACT FROM AUTHOR]