Specification Analysis of Option Pricing Models Based on Time- Changed Levy Processes

We analyze the specifications of option pricing models based on time- changed Levy processes. We classify option pricing models based on the structure of the jump component in the underlying return process, the source of stochastic volatility, and the specification of the volatility process itself. Our estimation of a variety of model specifications indicates that to better capture the behavior of the S&P 500 index options, we need to incorporate a high frequency jump component in the return process and generate stochastic volatilities from two different sources, the jump component and the diffusion component.Option pricing; Levy processes; time change; jumps; Diffusion; stochastic volatility; finite activity; infinite activity; infinite variation.

Donor–Acceptor Fluorophores for Energy-Transfer-Mediated Photocatalysis

Triplet–triplet energy transfer (EnT) is a fundamental activation pathway in photocatalysis. In this work, we report the mechanistic origins of the triplet excited state of carbazole-cyanobenzene donor–acceptor (D–A) fluorophores in EnT-based photocatalytic reactions and demonstrate the key factors that control the accessibility of the 3LE (locally excited triplet state) and 3CT (charge-transfer triplet state) via a combined photochemical and transient absorption spectroscopic study. We found that the energy order between 1CT (charge transfer singlet state) and 3LE dictates the accessibility of 3LE/3CT for EnT, which can be effectively engineered by varying solvent polarity and D–A character to depopulate 3LE and facilitate EnT from the chemically more tunable 3CT state for photosensitization. Following the above design principle, a new D–A fluorophore with strong D–A character and weak redox potential is identified, which exhibits high efficiency for Ni(II)-catalyzed cross-coupling of carboxylic acids and aryl halides with a wide substrate scope and high selectivity. Our results not only provide key fundamental insight on the EnT mechanism of D–A fluorophores but also establish its wide utility in EnT-mediated photocatalytic reactions

Spectrophotometric determinationof trace nitrite with a novel self-coupling diazotizing reagent: J-acid

A simple and sensitive method for the spectrophotometric determination of nitrite was described and optimum reaction conditions along with other important analytical parameters were established. In the presence of potassium bromide at 25°C, nitrite reacted with J-acid in hydrochloric acid producing diazonium salt and then coupled with excess J-acid in the sodium carbonate solution yielding red colored azo compounds. At wavelength of 500 nm, Beer’s law was obeyed over the concentration range of 0,02 – 0,60 mg∙L⁻¹. The molar absorptivity was 3,92∙10⁴ L∙mol⁻¹∙cm⁻¹. This method was easily applied to the determination of trace nitrite in environmental water with recoveries of 9₈,7 – 101,2%

Biological functions of endophytic bacteria in Robinia pseudoacacia ‘Hongsen’

IntroductionEndophytes and their host plants have co-evolved for a very long time. This relationship has led to the general recognition of endophytes as a particular class of microbial resources. R. pseudoacacia ‘Hongsen’ is drought- and barren-resistant species that can be grown in both the north and south of China, efficiently addresses the ecological issues caused by China’s ‘southern eucalyptus and northern poplar. Up to date, cultured-dependent studies are available for the R. pseudoacacia nitrogen-fixing and other endophytes. Therefore, the present research studied the R. pseudoacacia ‘Hongsen,’ microbiome in detail by high-throughput sequencing and culture dependant.MethodsThis study examined microbial species and functional diversity in Robinia pseudoacacia ‘Hongsen’ using culture-dependent (isolation) and culture-independent techniques.ResultsA total of 210 isolates were isolated from R. pseudoacacia ‘Hongsen.’ These isolates were clustered into 16 groups by the In Situ PCR (IS-PCR) fingerprinting patterns. 16S rRNA gene sequence analysis of the representative strain of each group revealed that these groups belonged to 16 species of 8 genera, demonstrating the diversity of endophytes in R. pseudoacacia ‘Hongsen’. ’Bacillus is the most prevalent genus among all the endophytic bacteria. High-throughput sequencing of endophytic bacteria from R. pseudoacacia ‘Hongsen’ of the plant and the rhizosphere soil bacteria showed that the bacterial populations of soil near the root, leaf, and rhizosphere differed significantly. The microbial abundance decreased in the endophytes as compared to the rhizosphere. We observed a similar community structure of roots and leaves. With and without root nodules, Mesorhizobium sp. was significantly different in R. pseudoacacia ‘Hongsen’ plant.DiscussionIt was predicted that R. pseudoacacia ‘Hongsen’ plant endophytic bacteria would play a significant role in the metabolic process, such as carbohydrate metabolism, amino acid metabolism, membrane transport, and energy metabolism

CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems

All-Trans-Retinoic Acid Suppresses Neointimal Hyperplasia and Inhibits Vascular Smooth Muscle Cell Proliferation and Migration via Activation of AMPK Signaling Pathway

The proliferation and migration of vascular smooth muscle cells (VSMC) is extensively involved in pathogenesis of neointimal hyperplasia. All-trans-retinoic acid (ATRA) is a natural metabolite of vitamin A. Here, we investigated the involvement of AMP-activated protein kinase (AMPK) in the anti-neointimal hyperplasia effects of ATRA. We found that treatment with ATRA significantly reduced neointimal hyperplasia in the left common carotid artery ligation mouse model. ATRA reduced the proliferation and migration of VSMC, A7r5 and HASMC cell lines. Our results also demonstrated that ATRA altered the expression of proliferation-related proteins, including CyclinD1, CyclinD3, CyclinA2, CDK2, CDK4, and CDK6 in VSMC. ATRA dose-dependently enhanced the phosphorylation level of AMPKα (Thr172) in the left common carotid artery of experimental mice. Also, the phosphorylation level of AMPKα in A7r5 and HASMC was significantly increased. In addition, ATRA dose-dependently reduced the phosphorylation levels of mTOR and mTOR target proteins p70 S6 kinase (p70S6K) and 4E-binding protein 1 (4EBP1) in A7r5 and HASMC. Notably, the inhibition of AMPKα by AMPK inhibitor (compound C) negated the protective effect of ATRA on VSMC proliferation in A7r5. Also, knockdown of AMPKα by siRNA partly abolished the anti-proliferative and anti-migratory effects of ATRA in HASMC. Molecular docking analysis showed that ATRA could dock to the agonist binding site of AMPK, and the binding energy between AMPK and ATRA was -7.91 kcal/mol. Molecular dynamics simulations showed that the binding of AMPK-ATRA was stable. These data demonstrated that ATRA might inhibit neointimal hyperplasia and suppress VSMC proliferation and migration by direct activation of AMPK and inhibition of mTOR signaling

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol–1 Å–1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost

A review on modelling methods, tools and service of integrated energy systems in China

An integrated energy system (IES) is responsible for aggregating various energy carriers, such as electricity, gas, heating, and cooling, with a focus on integrating these components to provide an efficient, low-carbon, and reliable energy supply. This paper aims to review the modeling methods, tools, and service modes of IES in China to evaluate opportunities for improving current practices. The models reviewed in this paper are classified as demand forecasting or energy system optimization models based on their modeling progress. Additionally, the main components involved in the IES modeling process are presented, and typical domestic tools utilized in the modeling processes are discussed. Finally, based on a review of several demonstration projects of IES, future development directions of IES are summarized as the integration of data-driven and engineering models, improvements in policies and mechanisms, the establishment of regional energy management centers, and the promotion of new energy equipment

Non-default Component of Sovereign Emerging Market Yield Spreads and its Determinants: Evidence from Credit Default Swap Market

This article shows that a sizable component of emerging market sovereign yield spreads is due to factors other than default risk, such as liquidity. The author estimates the non-default component of the yield spreads as the basis between the actual credit default swap (CDS) premium and the hypothetical CDS premium implied by emerging market bond yields. On average, the basis is large and positive for speculative-grade bonds and slightly negative for investment-grade bonds. The large positive basis for speculative-grade bonds supports the existence of speculation in the CDS market when the underlying's credit quality is bad. The author studies the effects of bond liquidity, liquidity in the CDS market, equity market performance, and macroeconomic variables on the non-default component of the emerging market yield spreads. The results show that bond liquidity has a significant and positive effect on the CDS–bond basis of investment-grade bonds. The results suggest that the liquid bonds of investment-grade bonds are more expensive relative to the prices implied their CDS premiums. However, the results are somewhat mixed and even contrary for the speculative-grade bond sample