Exploring the Interrelationships between Public Health, Fiscal Decentralization, and Local Government Debt in China

This paper investigates the interrelationships among local government debt, fiscal decentralization, and public health. The investigation begins by constructing a theoretical model to analyze the inherent connections between these variables. Subsequently, an empirical analysis is conducted using data from China between 2015 and 2021. The findings demonstrate a bidirectional relationship between fiscal decentralization, local government debt, and public health. Specifically, it is observed that an increase in local government debt has adverse effects on both fiscal decentralization and public health, while fiscal decentralization has a positive impact on public health. These insights are consistently validated through rigorous regression methodologies, affirming the robustness and significance of these relationships

TKwinFormer: Top k Window Attention in Vision Transformers for Feature Matching

Local feature matching remains a challenging task, primarily due to difficulties in matching sparse keypoints and low-texture regions. The key to solving this problem lies in effectively and accurately integrating global and local information. To achieve this goal, we introduce an innovative local feature matching method called TKwinFormer. Our approach employs a multi-stage matching strategy to optimize the efficiency of information interaction. Furthermore, we propose a novel attention mechanism called Top K Window Attention, which facilitates global information interaction through window tokens prior to patch-level matching, resulting in improved matching accuracy. Additionally, we design an attention block to enhance attention between channels. Experimental results demonstrate that TKwinFormer outperforms state-of-the-art methods on various benchmarks. Code is available at: https://github.com/LiaoYun0x0/TKwinFormer.Comment: 11 pages, 7 figure

Earth: Atmospheric Evolution of a Habitable Planet

Our present-day atmosphere is often used as an analog for potentially habitable exoplanets, but Earth's atmosphere has changed dramatically throughout its 4.5 billion year history. For example, molecular oxygen is abundant in the atmosphere today but was absent on the early Earth. Meanwhile, the physical and chemical evolution of Earth's atmosphere has also resulted in major swings in surface temperature, at times resulting in extreme glaciation or warm greenhouse climates. Despite this dynamic and occasionally dramatic history, the Earth has been persistently habitable--and, in fact, inhabited--for roughly 4 billion years. Understanding Earth's momentous changes and its enduring habitability is essential as a guide to the diversity of habitable planetary environments that may exist beyond our solar system and for ultimately recognizing spectroscopic fingerprints of life elsewhere in the Universe. Here, we review long-term trends in the composition of Earth's atmosphere as it relates to both planetary habitability and inhabitation. We focus on gases that may serve as habitability markers (CO2, N2) or biosignatures (CH4, O2), especially as related to the redox evolution of the atmosphere and the coupled evolution of Earth's climate system. We emphasize that in the search for Earth-like planets we must be mindful that the example provided by the modern atmosphere merely represents a single snapshot of Earth's long-term evolution. In exploring the many former states of our own planet, we emphasize Earth's atmospheric evolution during the Archean, Proterozoic, and Phanerozoic eons, but we conclude with a brief discussion of potential atmospheric trajectories into the distant future, many millions to billions of years from now. All of these 'Alternative Earth' scenarios provide insight to the potential diversity of Earth-like, habitable, and inhabited worlds.Comment: 34 pages, 4 figures, 4 tables. Review chapter to appear in Handbook of Exoplanet

A novel optimized grey model and its application in forecasting CO2 emissions

Carbon dioxide emissions are the main cause of global warming. At present, how to reduce carbon dioxide emissions while promoting energy savings and emission reduction is a hot research topic. Hence, China’s carbon dioxide emissions must be reasonably and accurately predicted because it is very important for the Chinese government to formulate energy and environmental policies. In this study, the classical optimization theory of the Fibonacci sequence and golden ratio were applied to the grey prediction model of an approximately inhomogeneous exponential series. Then, a new optimization model was established, and the properties of the optimization model were studied. The purpose is to reduce the parameter estimation errors of the model and improve the simulation and prediction accuracy of the model. Next, the novel model was applied to the simulation and prediction of CO2 emissions in China. The experimental results show that the effectiveness of the novel model was much better than that of the other models, which confirms the effectiveness of the new model. Based on this, China’s carbon dioxide emissions were predicted and analysed. The results show that China’s carbon dioxide emissions will still be on the rise over the next five years, and carbon dioxide emissions remain a serious problem

Towards Circular Fashion: Design for Community-Based Clothing Reuse and Upcycling Services under a Social Innovation Perspective

With the rise of the circular economy, recycling, and upcycling is an emerging sustainable system in the fashion industry, emphasising a closed loop of “design, produce, use, and recycle”. In this context, this paper will explore community-based approaches to scale up clothing reuse and upcycling under a social innovation perspective. This study aims to establish community-based practice models, which contribute toward promoting a greater understanding of sustainable fashion and achieving collaborative cocreation frameworks for community stakeholders. This paper, therefore, takes a social innovation perspective to conduct design studies helping with the technical (problem-solving) and cultural (sense-making) barriers that clothing reuse and upcycling face. The research was conducted in the context of the Shanghai community, and a large amount of first-hand research data were obtained through field research, expert and user interviews, and participatory workshops. Finally, this research establishes a platform proposal which combines strategic service design and practical toolkit design. It is a new community-based service model highlighting a significant advancement in the degree of collaboration and cocreation in traditional community service models. Additionally, it dramatically demonstrates the potential of socially innovative design thinking in promoting circular fashion and the closed-loop fashion system

VNF Chain Placement for Large Scale IoT of Intelligent Transportation

With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation

Optimization of Shearer Drum Based on Multi-Objective Bat Algorithm with Grid (MOBA/G)

The shearer drum undertakes the main function of coal falling and loading, and picks distributed on it have a great impact on the performance of the drum. However, few studies have optimized the pick and drum at the same time. In this paper, parameters of pick and drum are considered as design variables, and the response functions of design variables are established based on the central composite experiment method. The optimal structural and working parameters of the pick and the drum of MG500/1130-WD shearer are obtained by using the multi-objective bat algorithm and multi-objective bat algorithm with grid, respectively. Comparing results of the two algorithms, the multi-objective bat algorithm with grid is more effective in improving the comprehensive performance of the drum. According to the optimized design variables, a coal mining test is carried out to verify the optimization effect of the algorithm. The result provides some theoretical references for the design and production of the drum and has some engineering application value

Optimization of Shearer Drum Based on Multi-Objective Bat Algorithm with Grid (MOBA/G)

The shearer drum undertakes the main function of coal falling and loading, and picks distributed on it have a great impact on the performance of the drum. However, few studies have optimized the pick and drum at the same time. In this paper, parameters of pick and drum are considered as design variables, and the response functions of design variables are established based on the central composite experiment method. The optimal structural and working parameters of the pick and the drum of MG500/1130-WD shearer are obtained by using the multi-objective bat algorithm and multi-objective bat algorithm with grid, respectively. Comparing results of the two algorithms, the multi-objective bat algorithm with grid is more effective in improving the comprehensive performance of the drum. According to the optimized design variables, a coal mining test is carried out to verify the optimization effect of the algorithm. The result provides some theoretical references for the design and production of the drum and has some engineering application value

Peak Cutting Force Estimation of Improved Projection Profile Method for Rock Fracturing Capacity Prediction with High Lithological Tolerance

Prediction of rock fracturing capacity demands particular requirements for the exploitation of mineral resources, especially for the parameter design of conical pick performance for hard rock fragmentation, which must take into account differences in rock mechanical properties. Among these parameters, the peak cutting force (PCF) is important in designing, selecting, and optimizing the cutting head of mining equipment and a cutability index of rocks. Taking high lithological tolerance as demand traction, this study proposes a theoretical model for estimating the peak cutting force of conical picks based on the improved projection profile method for which the influence of alloy head, pick body structure, and installation parameters are taken into consideration. Besides, experimental results corresponding to different numbers of rock samples are used to verify the accuracy and stability of the theoretical model. Meanwhile, the comparison of performance in cutting force estimation between this model and four other existing theoretical models is conducted. The results found that the new method has the highest correlation coefficient with the experimental results and the lowest root mean square error comparing with other models, i.e., the estimation performance of this method has high lithological tolerance when the rock type increases and the lithology changes. Consequently, the proposed peak cutting force estimation of improved projection profile method will provide a more valid and accurate prediction for rock fracturing capacity with large differences in rock mechanical properties

Feature Matching and Position Matching Between Optical and SAR With Local Deep Feature Descriptor

Image matching between the optical and synthetic aperture radar (SAR) is one of the most fundamental problems for earth observation. In recent years, many researchers have used hand-made descriptors with their expertise to find matches between optical and SAR images. However, due to the large nonlinear radiation difference between optical images and SAR images, the image matching becomes very difficult. To deal with the problems, the article proposes an efficient feature matching and position matching algorithm (MatchosNet) based on local deep feature descriptor. First, A new dataset is presented by collecting a large number of corresponding SAR images and optical images. Then a deep convolutional network with dense blocks and cross stage partial networks is designed to generate deep feature descriptors. Next, the hard L2 loss function and ARCpatch loss function are designed to improve matching effect. In addition, on the basis of feature matching, the two-dimensional (2-D) Gaussian function voting algorithm is designed to further match the position of optical images and SAR images of different sizes. Finally, a large number of quantitative experiments show that MatchosNet has a excellent matching effect in feature matching and position matching. The code will be released at: https://github.com/LiaoYun0x0/Feature-Matching-and-Position-Matching-between-Optical-and-SAR