Estimating option prices and implied volatilities are critical for option risk management and trading. Common strategies in previous studies have relied on parametric models, including the stochastic ...volatility model (SV), jump-diffusion model (JD), and Black-Scholes model (BS). However, these models are built on several strict and idealistic assumptions, including lognormality and sample-path continuity. In addition, previous studies on option pricing mainly relied on its own market-level indicators without considering the effect of other concurrent options. To address these challenges, we propose an intelligent learning and ensembling framework based on convolutional neural network (CNN). Specifically, the customized nonparametric learning approach is first utilized to estimate option prices. Second, several traditional parametric models are also applied to estimate these prices. The estimated prices are combined by a CNN to obtain the final estimations. Our experiments based on Chinese SSE 50 ETF options demonstrate that the proposed intelligent framework outperforms the traditional SV model, JD model, and BS model with at least 41.52% performance enhancement in terms of RMSE.
Developing natural nano-platforms with high biocompatibility and natural targeting ability represents great significance for drug delivery. High-density lipoprotein (HDL), a natural lipid-protein ...complex, plays important roles in physiological activities, particularly in reverse cholesterol transport (RCT) and be closely associated with atherosclerotic cardiovascular diseases. Recent studies have demonstrated that HDLs have the potential to serve as ideal drug carriers. Recombinant HDLs (rHDLs) have been used to encapsulate substances such as small interfering RNA (siRNA), drugs, and contrast agents, fully utilizing the biocompatibility and targeting ability of rHDL in the body and providing new strategies for drug delivery and disease treatment. In this review, we discussed in detail the basic principles of HDL as a drug delivery system, the mechanisms of targeted drug delivery, and several methods for preparing HDL nanoparticles. Afterward, we comprehensively reviewed the applications of HDL as a drug carrier in cardiovascular diseases, cancer treatment (such as glioblastoma, breast cancer, hepatocellular carcinoma and urologic cancers) and some other fields. Finally, we reviewed the therapeutic effects and safety of HDL nanoparticles in clinical studies. Through a review and summary of these research advances, we aim to fully understand the potential of HDL as a drug carrier in clinical applications, providing valuable references and guidance for future research and expedites the translational application of HDL as drug carriers.
In this review, we comprehensively summarized the applications of nanomaterial rHDL as a drug carrier in cardiovascular diseases, cancer treatment (such as glioblastoma, breast cancer, hepatocellular carcinoma and urologic cancers) and some other fields. Display omitted
Objective. To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics ...technology, with the aim of providing a reference for clinical treatment. Methods. Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan–Meier online plotter tool. Results. CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. Conclusion. CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.
The Three Gorges Reservoir is the largest freshwater resource reservoir in China. The water environment security in the Three Gorges Reservoir area has a prominent position in the major national ...strategy for the protection of the Yangtze River. Based on the pressure–state–response (PSR) model, this study comprehensively considers the dangerousness of risk source, the sensitivity of risk receptors, and the acceptable level of regional environmental risk to construct the grading evaluation index system of water environment pollution risk. By using spatial statistical methods, including the variation coefficient method and cold–hot spot pattern analysis, the risk distribution of water environment pollution in the Chongqing section of the Three Gorges Reservoir area was comprehensively identified and evaluated by administrative units. The results showed that: (1) the number of risk sources was largest in Yunyang County and the number of risk receptors was largest in Wanzhou District. However, the distribution of high-risk pollution sources and high-sensitivity receptors was most intensive in the main urban area and surrounding areas of Chongqing, and the regional environmental risk acceptance level was the lowest. (2) The statistical results of risk source dangerousness and the risk receptor sensitivity index at the county level in the study area showed an aggregated distribution pattern, with hotspot areas concentrated in the main urban area of Chongqing and the surrounding area in the upper reaches of the reservoir area. Moreover, the acceptable level of risk in this area showed a cold spot area, while other regions basically showed a balanced distribution pattern without forming significant hot spot or cold spot areas. (3) The high-risk river section of water pollution in the reservoir area comprised five counties, including Jiulongpo District, Yubei District, Shapingba District, Yuzhong District and Nanan District; the middle-risk river section comprised six counties, including Changshou District, Beipei District, Jiangbei District, Dadukou District, Fuling District and Shizhu County; and the low-risk river sections were mainly distributed in the Jiangjin District in the upper reaches of the reservoir area and the middle and lower reaches of the northeast ecological area of Chongqing. Therefore, the acceptable levels of water pollution risk sources, receptors and regional environmental risks in the Chongqing section of the Three Gorges Reservoir area are unevenly distributed, showing an aggregated distribution pattern. The spatial distribution of water environment pollution risk is uneven, and the significant potential risk area is the functional core area of Chongqing, which is the critical area of water environment risk management in the future.
Stock volatility is influenced by the release, dissemination, and acceptance of information. Rumor clarification is expected to reduce asymmetric information and abnormal stock returns by increasing ...information transparency. However, investors are irrational, and modern behavioral finance studies attribute non-random stock movements to investors' cognitive and emotional biases. The verification of rumor authenticity may cause fluctuations in investor sentiment, which increases impulsive investing behaviors and stock movements. Due to the widespread and fast accessibility of social media, many electronic information platforms have been established to clarify rumors. It is critical to understand the effects of digitalized rumor clarification on stock markets. In this study, we extracted 12,663 rumor-clarification pairs from 1,804,520 social media posts. We quantified the language used in these messages via sentiment analysis, along with online firm behaviors, to study the effect of clarifications on stock markets. Our findings are as follows: (1) Digitalized rumor-clarification messages affect the abnormal returns of relevant stocks. (2) This influence can be quantified and measured by the emotion polarity of rumor clarification. (3) Firms' online clarification behaviors, including information disclosure frequency, response time, and wording, have limited to no influence on abnormal returns.
Two methods are described and illustrated for the measurement of organo−cobalt bond homolysis energies through reactions of tetra(p-anisyl)porphyrinato cobalt(II), (TAP)CoII•, with organic radicals ...of the form •C(CH3)(R)CN in the presence of olefins. Thermodynamic values for bond homolysis have been determined directly for (TAP)Co-C(CH3)2CN (ΔH° = 17.8±0.5 kcal mol-1, ΔS° = 23.1 ± 1.0 cal K-1 mol-1) and (TAP)Co-CH(CH3)C6H5 (ΔH° = 19.5 ± 0.6 kcal mol-1, ΔS° = 24.5 ± 1.1 cal K-1 mol-1) from evaluation of the equilibrium constants for the dissociation process (Co−R ⇌ CoII• + R•) in chloroform. The bond homolysis enthalpy for (TAP)Co-C5H9 (ΔH° = 30.9 kcal mol-1) was determined indirectly by measuring the thermodynamic values for the competition reaction (TAP)Co-C(CH3)2CN + C5H8 ⇌ (TAP)Co-C5H9 + CH2C(CH3)CN (ΔH° = 0.9 ± 0.3 kcal mol-1) in conjunction with a thermochemical cycle. This indirect approach was also used to evaluate (TAP)Co-CH(CH3)C6H5 BDE (20.5 kcal mol-1) which agrees favorably with the value determined directly. When the Co−R bond homolysis enthalpies are known from independent evaluation, these equilibrium measurements provide a method for evaluating relative heats of formation of organic radicals. Application of this approach gives 40.8 kcal mol-1 for the heat of formation of •C(CH3)2CN in chloroform. Success of these methods is dependent on fast abstraction of H• from the organic radicals by (TAP)CoII• to form (TAP)Co-H and rapid addition of (TAP)Co-H with olefins to form organocobalt complexes. Kinetic-equilibrium simulations utilizing reaction schemes for these processes provide an accurate description of the kinetic profiles and the equilibrium concentrations of solution species when the organic radical species achieve steady state.
A highly enantioselective tandem Michael addition of tryptamine‐derived oxindoles to alkynones was developed by taking advantage of a chiral N,N′‐dioxide Sc(OTf)3 catalyst. The reaction enables the ...facile preparation of enantioenriched spiropyrrolidine‐3,3′‐oxindole compounds, which provides a novel strategy for the synthesis of monoterpenoid indole alkaloids. As a demonstration, the asymmetric synthesis of strychnos alkaloids (−)‐tubifoline, (−)‐tubifolidine, (−)‐dehydrotubifoline was achieved in 10–11 steps.
Komplizierter Ringschluss: Bei der Synthese von Strychnos‐Alkaloiden katalysiert ein Komplex aus einem chiralen N,N′‐Dioxid und Sc(OTf)3 die enantioselektive Tandem‐Michael‐Addition von Tryptamin abgeleiteter Oxindole an Alkinone. Die Reaktion liefert enantiomerenangereicherte Spiropyrrolidin‐3,3′‐oxindol‐Verbindungen und demonstriert eine neue Synthesestrategie für Monoterpenoid‐Indolalkaloide.
Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough in the two tasks. ...One of the promising solutions called BERT can jointly optimize the two tasks. We note that BERT-based models convert each complex token into multiple sub-tokens by wordpiece algorithm, which generates a mismatch between the lengths of the tokens and the labels. This leads to BERT-based models do not do well in label prediction which limits model performance improvement. Many existing models can be compatible with this issue but some hidden semantic information is discarded in the fine-tuning process. We address the problem by introducing a novel joint method on top of BERT which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby contributing to the two tasks. Our method can well extract the contextual features from complex tokens by the proposed sub-words attention adapter (SAA), which preserves overall utterance information. Additionally, we propose an intent attention adapter (IAA) to obtain the full sentence features to aid users to predict intent. Experimental results confirm that our proposed model is significantly improved on two public benchmark datasets. In particular, the slot filling F1 score is improved from 96.1 to 98.2 (2.1% absolute) on the Airline Travel Information Systems (ATIS) dataset.
Improving the performance of low-cost inertial sensors is meaningful for popularization of automated driving technologies. Researchers usually utilize thermal chamber to calibrate the temperature ...drift errors (TDEs) of expensive inertial sensors and compensate them before using them. However, it is unacceptable for low-cost inertial sensors considering the massive calibration efforts. This paper proposes a novel method to model TDE as a state so that it can be estimated online with other states together, which can adaptively compensate different TDEs without preparation. A multi-sensor fusion system for estimating yaw angle with this idea is studied. The observability of this system is analyzed and the result shows that TDE is independent from others states when there is change of temperature. Experiments are carried out and the results reveal that the performance of inertial sensor assisted with the proposed method is better than that in normal system.
Combined with the idea of Austrian school of economics, this paper proposes a new multi-agent model for artificial stock market based on genetic network programming. It focuses on applying the GNP ...approach to emulate investment behavior of agents and evolve their trading rules. Simultaneously, this model enhances the heterogeneity of agents, and searches for an optimal combination of parameter values based on GA. Simulation results confirm the effectiveness of this GNP-ASM model through comparison with empirical statistics
