Granger Causality in Risk and Detection of Extreme Risk Spillover Between Financial Markets

Controlling and monitoring extreme downside market risk is important for financial risk management and portfolio/investment diversification. In this paper, we introduce a new concept of Granger causality in risk and propose a class of kernel-based tests to detect extreme downside risk spillover between financial markets, where risk is measured by the left tail of the distribution or equivalently by the Value at Risk (VaR). The proposed tests have a convenient asymptotic standard normal distribution under the null hypothesis of no Granger causality in risk. They check a large number of lags and thus can detect risk spillover that occurs with a time lag or that has weak spillover at each lag but carries over a very long distributional lag. Usually, tests using a large number of lags may have low power against alternatives of practical importance, due to the loss of a large number of degrees of freedom. Such power loss is fortunately alleviated for our tests because our kernel approach naturally discounts higher order lags, which is consistent with the stylized fact that today’s financial markets are often more influenced by the recent events than the remote past events. A simulation study shows that the proposed tests have reasonable size and power against a variety of empirically plausible alternatives in nite samples, including the spillover from the dynamics in mean, variance, skewness and kurtosis respectively. In particular, nonuniform weighting delivers better power than uniform weighting and a Granger type regression procedure. The proposed tests are useful in investigating large comovements between financial markets such as financial contagions. An application to the Eurodollar and Japanese Yen highlights the merits of our approach

Cyclic performance of viscoelastic dielectric elastomers with solid hydrogel electrodes

Hydrogels containing electrolyte can work as ionic conductors to actuate dielectric elastomer (DE) artificial muscles. Based on a popular design of a circular actuator, we study theoretically and experimentally the cyclic performance of acrylic DE actuators with solid hydrogel electrodes. The viscoelasticity of solid electrodes constrains the maximum strain that is attainable for one cycle of triangular voltage, and it also diminishes the accumulated increment of the maximum strain after many cycles of loadings.Engineering and Applied Science

The Impact of L2 Learning on Cognitive Aging

It has become a multidisciplinary research area to overcome cognitive decline caused by aging. Many factors can affect cognitive aging and the influence of second language learning (L2 learning) cannot be ignored. The recent decade has witnessed much pathological, behavior and neuroimaging research that L2 experience may help maintain the cognitive function in the elderly, resist cognitive decline, and even delay the onset of Alzheimer\u27s disease (AD). This work is to review available literature concerned and elucidate the neural mechanisms under which L2 learning (training) may modify or sculpt the brain from perspectives of cognitive reserve, plasticity and overlapping networks. Future directions concerning length of learning, frequency of use, comparison with other cognitively stimulating activities are put forward so as to clarify the relationship between language experience and cognitive aging

Single-image based deep learning for precise atomic defects identification

Defect engineering has been profoundly employed to confer desirable functionality to materials that pristine lattices inherently lack. Although single atomic-resolution scanning transmission electron microscopy (STEM) images are widely accessible for defect engineering, harnessing atomic-scale images containing various defects through traditional image analysis methods is hindered by random noise and human bias. Yet the rise of deep learning (DL) offering an alternative approach, its widespread application is primarily restricted by the need for large amounts of training data with labeled ground truth. In this study, we propose a two-stage method to address the problems of high annotation cost and image noise in the detection of atomic defects in monolayer 2D materials. In the first stage, to tackle the issue of data scarcity, we employ a two-state transformation network based on U-GAT-IT for adding realistic noise to simulated images with pre-located ground truth labels, thereby infinitely expanding the training dataset. In the second stage, atomic defects in monolayer 2D materials are effectively detected with high accuracy using U-Net models trained with the data generated in the first stage, avoiding random noise and human bias issues. In both stages, we utilize segmented unit-cell-level images to simplify the model's task and enhance its accuracy. Our results demonstrate that not only sulfur vacancies, we are also able to visualize oxygen dopants in monolayer MoS2, which are usually overwhelmed by random background noise. As the training was based on a few segmented unit-cell-level realistic images, this method can be readily extended to other 2D materials. Therefore, our results outline novel ways to train the model with minimized datasets, offering great opportunities to fully exploit the power of machine learning (ML) applicable to a broad materials science community

EFFECTS OF CIJI HUA’AI BAOSHENG GRANULE FORMULA (CHBGF) ON LIFE TIME, PATHOLOGY, PERIPHERAL BLOOD CELLS OF TUMOR CHEMOTHERAPY MODEL MOUSE WITH H22 HEPATOMA CARCINOMA CELLS

Background: Ciji Hua’ai Baosheng Granule Formula (CHBGF) is a traditional Chinese empirical formula that can help the tumor patients who have received chemotherapy antagonize the toxin and side-effects so as to improve and prolong the life. This study is to evaluate the effects of CHBGF on improving life quality in terms of survival time, pathology of tumor tissue and ameliorating peripheral blood cells in mouse chemotherapy model with subcutaneous transplanted tumor or ascitic tumor of H22 hepatoma carcinoma cells at an overall level. Materials and Methods: 71 mice among the 92 Kunming mice were injected subcutaneously into the right anterior armpit with H22 hepatoma carcinoma cells, after 7 days, which had formed tumors and were used peritoneal injection of Cytoxan (CTX) (200mg/kg) to establish the mouse chemotherapy model with transplanted tumor, and then which were commensurately divided into 8 groups by random digits table. 21 mice were injected into peritoneal cavity to use CTX and the same method to establish the model. The groups for evaluating the effects on the survival time were the model, CHBGF and positive control group respectively with 7 mice in each group. The groups for evaluating the effects on anti-cancer were the model group, three treatment groups and positive control group with 10 mice in each group. The survival-time-observing groups were given intragastric administration of normal saline, CHBGF (64g/kg) once a day, and peritoneal injection of 5-Fluorouracil (25mg/kg) once every other day respectively. The survival time of each group was observed. The five anti-cancer-observing groups were given intragastric administration of normal saline, CHBGF (64g/kg, 32g/kg and 16g/kg) once a day, and peritoneal injection of 5-Fluorouracil (25mg/kg) once every other day respectively. After treatment for 21 days, the transplanted tumors were peeled off. Blood was collected through pricking eyeball and analyzed by hematology analyzer. And postchemotherapy transplanted tumor inhibition ratios were calculated. Pathological changes of tumor tissues and blood smears were observed with light microscope. Results: The life prolonging rate of CHBGF (64g/kg) group with transplanted tumor is 20.14%, and their survival time was longer than that of the 5-Fluorouracil group (

Boosting Lithium-Ion Storage Capability in CuO Nanosheets via Synergistic Engineering of Defects and Pores

CuO is a promising anode material for lithium-ion batteries due to its high theoretical capacity, low cost, and non-toxicity. However, its practical application has been plagued by low conductivity and poor cyclability. Herein, we report the facile synthesis of porous defective CuO nanosheets by a simple wet-chemical route paired with controlled annealing. The sample obtained after mild heat treatment (300°C) exhibits an improved crystallinity with low dislocation density and preserved porous structure, manifesting superior Li-ion storage capability with high capacity (~500 mAh/g at 0.2 C), excellent rate (175 mAh/g at 2 C), and cyclability (258 mAh/g after 500 cycles at 0.5 C). The enhanced electrochemical performance can be ascribed to the synergy of porous nanosheet morphology and improved crystallinity: (1) porous morphology endows the material a large contact interface for electrolyte impregnation, enriched active sites for Li-ion uptake/release, more room for accommodation of repeated volume variation during lithiation/de-lithiation. (2) the improved crystallinity with reduced edge dislocations can boost the electrical conduction, reducing polarization during charge/discharge. The proposed strategy based on synergic pore and defect engineering can pave the way for development of advanced metal oxides-based electrodes for (beyond) Li-ion batteries