Government Ownership, Capital Allocation and Bank Risk

In this paper, we study the relationship between government ownership and bank risk. Two different variables are generated using the Chinese commercial banks\u27 data from the year 2000 to 2011. One variable is z-risk which indicates the risk of insolvency based on the banks\u27 operating performance, and the other one is Moody\u27s financial strength ratings which measures the operation risk of individual bank. Data support that government ownership increases commercial banks\u27 operation risk, either in terms of solvency margin or financial strength ratings. The results also indicate that larger banks have lower operation risk than smaller commercial banks. However, our empirical evidence shows that economic growth has negative impact on the operation risk of commercial banks even after controlling year-specific effect. It is surprising that foreign-owned banks have higher operation risk than local banks

Solving specified-time distributed optimization problem via sampled-data-based algorithm

Despite significant advances on distributed continuous-time optimization of multi-agent networks, there is still lack of an efficient algorithm to achieve the goal of distributed optimization at a pre-specified time. Herein, we design a specified-time distributed optimization algorithm for connected agents with directed topologies to collectively minimize the sum of individual objective functions subject to an equality constraint. With the designed algorithm, the settling time of distributed optimization can be exactly predefined. The specified selection of such a settling time is independent of not only the initial conditions of agents, but also the algorithm parameters and the communication topologies. Furthermore, the proposed algorithm can realize specified-time optimization by exchanging information among neighbours only at discrete sampling instants and thus reduces the communication burden. In addition, the equality constraint is always satisfied during the whole process, which makes the proposed algorithm applicable to online solving distributed optimization problems such as economic dispatch. For the special case of undirected communication topologies, a reduced-order algorithm is also designed. Finally, the effectiveness of the theoretical analysis is justified by numerical simulations

On 3D simultaneous attack against manoeuvring target with communication delays

This article investigates the simultaneous attack problem of multiple missiles against a manoeuvring target with delayed information transmission in three-dimensional space. Based on the kinetic model of the missiles, the problem is divided into three demands: the velocity components normal to line-of-sight converge to zero in finite time, the component of motion states along line-of-sight should achieve consensus and converge to zero. The guidance law is designed for each demand and by theoretical proof, the upper bound of delay which can tolerate is presented and the consensus error of the relative distances can converge to a small neighbourhood of zero. And simulation example presented also demonstrates the validity of the theoretical result

Aboveground and Belowground Plant Traits Explain Latitudinal Patterns in Topsoil Fungal Communities From Tropical to Cold Temperate Forests

Soil fungi predominate the forest topsoil microbial biomass and participate in biogeochemical cycling as decomposers, symbionts, and pathogens. They are intimately associated with plants but their interactions with aboveground and belowground plant traits are unclear. Here, we evaluated soil fungal communities and their relationships with leaf and root traits in nine forest ecosystems ranging from tropical to cold temperate along a 3,700-km transect in eastern China. Basidiomycota was the most abundant phylum, followed by Ascomycota, Zygomycota, Glomeromycota, and Chytridiomycota. There was no latitudinal trend in total, saprotrophic, and pathotrophic fungal richness. However, ectomycorrhizal fungal abundance and richness increased with latitude significantly and reached maxima in temperate forests. Saprotrophic and pathotrophic fungi were most abundant in tropical and subtropical forests and their abundance decreased with latitude. Spatial and climatic factors, soil properties, and plant traits collectively explained 45% of the variance in soil fungal richness. Specific root length and root biomass had the greatest direct effects on total fungal richness. Specific root length was the key determinant of saprotrophic and pathotrophic fungal richness while root phosphorus content was the main biotic factor determining ectomycorrhizal fungal richness. In contrast, spatial and climatic features, soil properties, total leaf nitrogen and phosphorus, specific root length, and root biomass collectively explained >60% of the variance in fungal community composition. Soil fungal richness and composition are strongly controlled by both aboveground and belowground plant traits. The findings of this study provide new evidence that plant traits predict soil fungal diversity distribution at the continental scale

Assessment of dynamic characteristics of fluidized beds via numerical simulations

Euler–Lagrange simulations coupled with the multiphase particle-in-cell (MP-PIC) approach for considering inter-particulate collisions have been performed to simulate a non-reacting fluidized bed at laboratory-scale. The objective of this work is to assess dynamic properties of the fluidized bed in terms of the specific kinetic energy of the bed material $k_S$ in J/kg and the bubble frequency $f_B$ in Hz, which represent suitable measures for the efficiency of the multiphase momentum exchange and the characteristic timescale of the fluidized bed system. The simulations have reproduced the bubbling fluidization regime observed in the experiments, and the calculated pressure drop $\Delta p$ in Pa has shown a reasonably good agreement with measured data. While varying the bed inventory $m_S$ in kg and the superficial gas velocity $u_G$ in m/s, $k_S$ increases with $u_G$ due to the increased momentum of the gas flow, which leads to a reinforced gas-to-solid momentum transfer. In contrast, $f_B$ decreases with $m_S$, which is attributed to the increased bed height $h_B$ in m at larger $m_S$. An increased gas temperature $T_G$ from 20 to 500 °C has led to an increase in $k_S$ by approximately 50%, whereas $\Delta p$, $h_B$, and $f_B$ are not sensitive to $T_G$. This is due to the increased gas viscosity with $T_G$, which results in an increased drag force exerted by the gas on the solid phase. While up-scaling the reactor to increase the bed inventory, bubble formation is enhanced significantly. This has led to an increased $f_B$, whereas $k_S$, $h_B$, and $\Delta p$ remain almost unchanged during the scale-up process. The results reveal that the general parameters such as $h_B$ and $\Delta p$ are not sufficient for assessing the hydrodynamic behavior of a fluidized bed while varying the operating temperatures and up-scaling the reactor dimension. In these cases, the dynamic properties $k_S$ and $f_B$ can be used as more suitable parameters for characterizing the hydrodynamics of fluidized beds

A novel target detection method for SAR images based on shadow proposal and saliency analysis

Conventional synthetic aperture radar (SAR) based target detection methods generally use high intensity pixels in the pre-screening stage while ignoring shadow information. Furthermore, they cannot accurately extract the target area and also have poor performance in cluttered environments. To solve this problem, a novel SAR target detection method which combines shadow proposal and saliency analysis is presented in this paper. The detection process is divided into shadow proposal, saliency detection and One-Class Support Vector Machine (OC-SVM) screening stages. In the shadow proposal stage, localizing targets is performed rst with the detected shadow regions to generate proposal chips that may contain potential targets. Then saliency detection is conducted to extract salient regions of the proposal chips using local spatial autocorrelation and signicance tests. Afterwards, in the last stage, the OC-SVM is employed to identify the real targets from the salient regions. Experimental results show that the proposed saliency detection method possesses higher detection accuracy than several state of the art methods on SAR images. Furthermore, the proposed SAR target detection method is demonstrated to be robust under dierent imaging environments. to extract salient regions of the proposal chips using local spatial autocorrelation and signicance tests. Afterwards, in the last stage, the OC-SVM is employe

Robust Hybrid Algorithm of PSO and SOCP for Grating Lobe Suppression and against Array Manifold Mismatch

Based on Particle Swarm Optimization (PSO) and Second-Order Cone Programming (SOCP) algorithm, this paper proposes a hybrid optimization method to suppress the grating lobes of sparse arrays and improve the robustness of array layout. With the peak side-lobe level (PSLL) as the objective function, the paper adopts the particle swarm optimization as a global optimization algorithm to optimize the elements’ positions, the convex optimization as a local optimization algorithm to optimize the elements’ weights. The effectiveness of the grating lobes suppression (as low as -32.13 dB) by this method is illustrated through its application to the sparse linear array when the actual steering vector is known. To enhance the robustness of the optimized array, a rebuilt robust convex optimization model is adopted in the optimization of both array excitations and layout. When the array manifold mismatch error is 1cm, the PSLL by the robust algorithm can be compressed to -27dB, compared to that of -24dB by the ordinary optimization. Results of a set of representative numerical experiments show that the algorithm proposed in this paper can obtain a more robust array layout and matched elements’ weight coefficients to avoid the huge degradation of the array pattern performance in the presence of array manifold mismatch errors. The good performance of pattern synthesis demonstrates the effectiveness of the proposed robust algorithm

Didymin improves UV irradiation resistance in C. elegans

Didymin, a type of flavono-o-glycoside compound naturally present in citrus fruits, has been reported to be an effective anticancer agent. However, its effects on stress resistance are unclear. In this study, we treated Caenorhabditis elegans with didymin at several concentrations. We found that didymin reduced the effects of UV stressor on nematodes by decreasing reactive oxygen species levels and increasing superoxide dismutase (SOD) activity. Furthermore, we found that specific didymin-treated mutant nematodes daf-16(mu86) & daf-2(e1370), daf-16(mu86), akt-1(ok525), akt-2(ok393), and age-1(hx546) were susceptible to UV irradiation, whereas daf-2(e1371) was resistant to UV irradiation. In addition, we found that didymin not only promoted DAF-16 to transfer from cytoplasm to nucleus, but also increased both protein and mRNA expression levels of SOD-3 and HSP-16.2 after UV irradiation. Our results show that didymin affects UV irradiation resistance and it may act on daf-2 to regulate downstream genes through the insulin/IGF-1-like signaling pathway

Network analysis of cold cognition and depression in middle-aged and elder population: the moderation of grandparenting

BackgroundCognitive decline and negative emotions are common in aging, especially decline in cold cognition which often co-occurred with depression in middle-aged and older adults. This study analyzed the interactions between cold cognition and depression in the middle-aged and elder populations using network analysis and explored the effects of grandparenting on the cold cognition-depression network.MethodsThe data of 6,900 individuals (≥ 45 years) from the China Health and Retirement Longitudinal Study (CHARLS) were used. The Minimum Mental State Examination (MMSE) and the Epidemiology Research Center Depression Scale-10 (CESD-10) were used to assess cold cognition and depressive symptoms, respectively. Centrality indices and bridge centrality indices were used to identify central nodes and bridge nodes, respectively.ResultsNetwork analysis showed that nodes “language ability” and “depressed mood” were more central nodes in the network of cold cognition and depression in all participants. Meantime, nodes “attention,” “language ability” and “hopeless” were three key bridge nodes connecting cold cognition and depressive symptoms. Additionally, the global connectivity of the cold cognition and depression network was stronger in the non-grandparenting than the grandparenting.ConclusionThe findings shed a light on the complex interactions between cold cognition and depression in the middle-aged and elder populations. Decline in language ability and depressed mood can serve as predictors for the emergence of cold cognitive dysfunction and depression in individuals during aging. Attention, language ability and hopelessness are potential targets for psychosocial interventions. Furthermore, grandparenting is effective in alleviating cold cognitive dysfunction and depression that occur during individual aging