DETERMINANTS OF BANK CAPITAL: EVIDENCE FROM THE U.S.

This paper analyses factors that affect bank capital. We use a sample of U.S. banks over the period 1996 to 2012. According to bank size, we separate the whole sample into small banks, medium banks and large banks. These three groups have different abilities to manage risks and access capital markets. To see the impact of the recent financial crisis, we further separate the whole sample into two subsamples: 1996 to 2006 and 2007 to 2012. Making use of an advanced estimation method (GMM), we find that bank capital is influenced by risk, profitability, deposits, loan loss provision, and size

Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional Network (GCN) to model and process multi-targets appearing in sentences at the same time based on the positional relationship, and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target words. In addition to the standard target-dependent sentiment classification task, an auxiliary node relation classification task is constructed. Experiments demonstrate that our model achieves new comparable performance on the benchmark datasets: SemEval-2014 Task 4, i.e., reviews for restaurants and laptops. Furthermore, the method of dividing the target words into isolated individuals has disadvantages, and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model

Based on the difference of Newton’s method integrated energy system distributed collaborative optimization

With the integration of renewable energy into the grid, the traditional power system stability faced by huge challenges, and the development of integrated energy system, it is of essence to improve the coupling of multiple integrated energy systems of different types, management in the integrated energy system and reduce the pressure of communication and computing, in this paper, we construct a distributed Newton algorithm based on Newton’s method to accelerate the solving speed, which decreases the times of iterations to reduce the pressure of communication and calculation, saving the cost of operation. Besides, privacy protection is particularly important for a distributed control system, under the premise that calculation speed is guaranteed, meanwhile, privacy protection of all agents in an integrated energy system is also critical. This study uses annular directed distributed algorithm to enhance the privacy of integrated distributed energy systems in the intelligent body, so as to fully ensure the privacy safety of all agents in the system. Moreover, the forementioned difference Newton algorithm in this study avoid the behavior of Zeno, greatly accelerating the speed of iteration and finding the best energy market price,. At the same time, the privacy safety of all agentsin the distributed energy system are ensured. Finally, a distributed integrated energy system based on the algorithm proposed by this study has went through theoretical proof and simulation experiment, whose result shows the validity of the algorithm

Solubility of CO2 in Ionic Liquids with Additional Water and Methanol: Modeling with PC-SAFT Equation of State

The superior properties of specific ionic liquids (ILs), such as negligible volatility, high thermal stability, flexible designability, and their affinity to capture CO2, make them an attractive alternative to chemical and physical solvents that are currently used in CO2 capture processes. However, a limitation to use ILs for industrial CO2 capture is their high viscosity compared to conventional solvents, which leads to a lower CO2 capture rate and higher pumping cost. The viscosity of ILs can be reduced by adding a co-solvent, such as water or methanol. In this work, solubility, vapor–liquid equilibria (VLE), and liquid–liquid equilibria (LLE) for binary and ternary mixtures involving CO2, ILs, water, and methanol have been systematically investigated by employing perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state with two different strategies. ILs were considered as self-associating chain molecules with two association sites in the first strategy. As a comparison, they were regarded as strong electrolytes that dissociate into anions and cations in the second strategy. It was found that both strategies provide accurate correlations in modeling CO2 solubilities in ILs and LLE of binary ILs/water systems. Four ternary systems were selected to verify the predictive capability of the two strategies. For water-containing systems, both strategies performed excellently when binary interaction parameters (BIPs) can be obtained by fitting to experimental data, while they performed poorly for system with few experimental data. For cases where methanol acted as a co-solvent, accurate predictions were obtained with both strategies, even without any BIPs. PC-SAFT was found to be a potential practical tool to develop CO2 capture processes with new alternative solvents when there are sufficient experimental data for binary mixtures

Global and local carbon footprints of city of Hong Kong and Macao from 2000 to 2015

Hong Kong and Macao are featured with their urban metabolism as they heavily rely on the energy and resource supply from other regions. However, a comprehensive perspective is lacked to depict their CO2 emissions due to the independence of statistical data. Here we analyze the carbon footprints of Hong Kong and Macao. The direct energy-related emissions (Scope 1), the emissions of cross-boundary electricity (Scope 2), and the embodied emissions associated with trade (Scope 3) are examined. Scope 1 carbon footprints of the two areas were stabilized at 50 Mt, accounting for 0.6% of those from Mainland China in 2018. Their global footprints were approximately three times of their Scope 1 emissions, accompanied by a continuous growth between 2000 and 2015, and the contribution of their local footprints has doubled on average. Their Scope 3 emissions were mainly due to the enormous unfavorable balance of trade. Meanwhile, the increasing impact of imports' higher emission intensity on their Scope 3 emissions should not be ignored. We suggest that Hong Kong and Macao should adjust their mitigation policies that focus only on Scope 1 emissions as developed cities outsourcing production through supply chains

Finding flaws in the spatial distribution of health workforce and its influential factors: An empirical analysis based on Chinese provincial panel data, 2010–2019

BackgroundThe maldistributions of the health workforce showed great inconsistency when singly measured by population quantity or geographic area in China. Meanwhile, earlier studies mainly employed traditional econometric approaches to investigate determinants for the health workforce, which ignored spillover effects of influential factors on neighboring regions. Therefore, we aimed to analyze health workforce allocation in China from demographic and geographic perspectives simultaneously and then explore the spatial pattern and determinants for health workforce allocation taking account of the spillover effect.MethodsThe health resource density index (HRDI) equals the geometric mean of health resources per 1,000 persons and per square kilometer. First, the HRDI of licensed physicians (HRDI_P) and registered nurses (HRDI_N) was calculated for descriptive analysis. Then, global and local Moran's I indices were employed to explore the spatial features and aggregation clusters of the health workforce. Finally, four types of independent variables were selected: supportive resources (bed density and government health expenditure), healthcare need (proportion of the elderly population), socioeconomic factors (urbanization rate and GDP per capita), and sociocultural factors (education expenditure per pupil and park green area per capita), and then the spatial panel econometric model was used to assess direct associations and intra-region spillover effects between independent variables and HRDI_P and HRDI_N.ResultsGlobal Moran's I index of HRDI_P and HRDI_N increased from 0.2136 (P = 0.0070) to 0.2316 (P = 0.0050), and from 0.1645 (P = 0.0120) to 0.2022 (P = 0.0080), respectively. Local Moran's I suggested spatial aggregation clusters of HRDI_P and HRDI_N. For HRDI_P, bed density, government health expenditure, and GDP had significantly positive associations with local HRDI_P, while the proportion of the elderly population and education expenditure showed opposite spillover effects. More precisely, a 1% increase in the proportion of the elderly population would lead to a 0.4098% increase in HRDI_P of neighboring provinces, while a 1% increase in education expenditure leads to a 0.2688% decline in neighboring HRDI_P. For HRDI_N, the urbanization rate, bed density, and government health expenditure exerted significantly positive impacted local HRDI_N. In addition, the spillover effect was more evident in the urbanization rate, with a 1% increase in the urbanization rate relating to 0.9080% growth of HRDI_N of surrounding provinces. Negative spillover effects of education expenditure, government health expenditure, and elderly proportion were observed in neighboring HRDI_N.ConclusionThere were substantial spatial disparities in health workforce distribution in China; moreover, the health workforce showed positive spatial agglomeration with a strengthening tendency in the last decade. In addition, supportive resources, healthcare needs, and socioeconomic and sociocultural factors would affect the health labor configuration not only in a given province but also in its nearby provinces

Inhibition of Cathepsin S Produces Neuroprotective Effects after Traumatic Brain Injury in Mice

Cathepsin S (CatS) is a cysteine protease normally present in lysosomes. It has long been regarded as an enzyme that is primarily involved in general protein degradation. More recently, mounting evidence has shown that it is involved in Alzheimer disease, seizures, age-related inflammatory processes, and neuropathic pain. In this study, we investigated the time course of CatS protein and mRNA expression and the cellular distribution of CatS in a mouse model of traumatic brain injury (TBI). To clarify the roles of CatS in TBI, we injected the mice intraventricularly with LHVS, a nonbrain penetrant, irreversible CatS inhibitor, and examined the effect on inflammation and neurobehavioral function. We found that expression of CatS was increased as early as 1 h after TBI at both protein and mRNA levels. The increased expression was detected in microglia and neurons. Inhibition of CatS significantly reduced the level of TBI-induced inflammatory factors in brain tissue and alleviated brain edema. Additionally, administration of LHVS led to a decrease in neuronal degeneration and improved neurobehavioral function. These results imply that CatS is involved in the secondary injury after TBI and provide a new perspective for preventing secondary injury after TBI

A review on the usability,flexibility, affinity, and affordability of virtual technology for rehabilitation training of upper limb amputees

(1) Background: Prosthetic rehabilitation is essential for upper limb amputees to regain their ability to work. However, the abandonment rate of prosthetics is higher than 50% due to the high cost of rehabilitation. Virtual technology shows potential for improving the availability and cost-effectiveness of prosthetic rehabilitation. This article systematically reviews the application of virtual technology for the prosthetic rehabilitation of upper limb amputees.(2) Methods: We followed PRISMA review guidance, STROBE, and CASP to evaluate the included articles. Finally, 17 articles were screened from 22,609 articles.(3) Results: This study reviews the possible benefits of using virtual technology from four aspects: usability, flexibility, psychological affinity, and long-term affordability. Three significant challenges are also discussed: realism, closed-loop control, and multi-modality integration.(4) Conclusions: Virtual technology allows for flexible and configurable control rehabilitation, both during hospital admissions and after discharge, at a relatively low cost. The technology shows promise in addressing the critical barrier of current prosthetic training issues, potentially improving the practical availability of prosthesis techniques for upper limb amputees