Synthesizing high-resolution satellite salinity data based on multi-fractal fusion

The spaceborne platform has unprecedently provided the global eddy-permitting(typically about 0.25°)products of sea surface salinity(SSS),however the existing SSS products can hardly resolve mesoscale motions due to the heavy noises therein and the over-smoothing in denoising processes.By means of the multi-fractal fusion(MFF),the high-resolution SSS product is synthesized with the template of sea surface temperature(SST).Two low-resolution SSS products and four SST products are considered as the source data and the templates respectively to determine the best combination.The fused products are validated by the in situ observations and intercompared via SSS maps,Singularity Exponent maps and wavenumber spectra.The results demonstrate that the MFF can perform a good work in mitigating the noises and improving the resolution.The combination of the climate change initiative SSS and the remote sensing system SST can produce the 0.1°denoised product whose global mean standard derivation of salinity against Argo is 0.21 and the feature resolution can reach 30−40 km.

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

Application of Elzaki Transform Method to Market Volatility Using the Black-Scholes Model

Black-Scholes Model (B-SM) simulates the dynamics of financial market and contains instruments such as options and puts which are major indices requiring solution. B-SM is known to estimate the correct prices of European Stock options and establish the theoretical foundation for Option pricing. Therefore, this paper evaluates the Black-Schole model in simulating the European call in a cash flow in the dependent drift and focuses on obtaining analytic and then approximate solution for the model. The work also examines Fokker Planck Equation (FPE) and extracts the link between FPE and B-SM for non equilibrium systems. The B-SM is then solved via the Elzaki transform method (ETM). The computational procedures were obtained using MAPLE 18 with the solution provided in the form of convergent series.

Optimal Quota-Share and Excess-of-Loss Reinsurance and Investment with Heston’s Stochastic Volatility Model

An optimal quota-share and excess-of-loss reinsurance and investment problem is studied for an insurer who is allowed to invest in a risk-free asset and a risky asset.Especially the price process of the risky asset is governed by Heston's stochastic volatility(SV)model.With the objective of maximizing the expected index utility of the terminal wealth of the insurance company,by using the classical tools of stochastic optimal control,the explicit expressions for optimal strategies and optimal value functions are derived.An interesting conclusion is found that it is better to buy one reinsurance than two under the assumption of this paper.Moreover,some numerical simulations and sensitivity analysis are provided.

Comparative evaluation of commercial Douchi by different molds:biogenic amines,non-volatile and volatile compounds

To provide new insights into the development and utilization of Douchi artificial starters,three common strains(Aspergillus oryzae,Mucor racemosus,and Rhizopus oligosporus)were used to study their influence on the fermentation of Douchi.The results showed that the biogenic amine contents of the three types of Douchi were all within the safe range and far lower than those of traditional fermented Douchi.Aspergillus-type Douchi produced more free amino acids than the other two types of Douchi,and its umami taste was more prominent in sensory evaluation(P<0.01),while Mucor-type and Rhizopus-type Douchi produced more esters and pyrazines,making the aroma,sauce,and Douchi flavor more abundant.According to the Pearson and PLS analyses results,sweetness was significantly negatively correlated with phenylalanine,cysteine,and acetic acid(P<0.05),bitterness was significantly negatively correlated with malic acid(P<0.05),the sour taste was significantly positively correlated with citric acid and most free amino acids(P<0.05),while astringency was significantly negatively correlated with glucose(P<0.001).Thirteen volatile compounds such as furfuryl alcohol,phenethyl alcohol,and benzaldehyde caused the flavor difference of three types of Douchi.This study provides theoretical basis for the selection of starting strains for commercial Douchi production.

Effect of different drying methods on the amino acids,α-dicarbonyls and volatile compounds of rape bee pollen

The significant demand for high quality food has motivated us to adopt appropriate processing methods to improve the food nutritional quality and flavors.In this study,the effects of five drying methods,namely,pulsed vacuum drying(PVD),freeze drying(FD),infrared drying(IRD),hot-air drying(HAD)and sun drying(SD)on free amino acids(FAAs),α-dicarbonyl compounds(α-DCs)and volatile compounds(VOCs)in rape bee pollen(RBP)were determined.The results showed that FD significantly released the essential amino acids(EAAs)compared with fresh samples while SD caused the highest loss.Glucosone was the dominantα-DCs in RBP and the highest loss was observed after PVD.Aldehydes were the dominant volatiles of RBP and SD samples contained more new volatile substances(especially aldehydes)than the other four drying methods.Comprehensively,FD and PVD would be potential methods to effectively reduce the quality deterioration of RBP in the drying process.

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

In this study,we proposed a new model to improve the accuracy of fore-casting the stock market volatility pattern.The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange(Tada-wul).The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations.The proposed forecasting model combines the best maximum overlapping discrete wavelet transform(MODWT)function(Bl14)and exponential generalized autoregressive conditional heteroscedasticity(EGARCH)model.The results show the model's ability to analyze stock market data,highlight important events that contain the most volatile data,and improve forecast accuracy.The results were compared from a number of mathematical mod-els,which are the non-linear spectral model,autoregressive integrated moving aver-age(ARIMA)model and EGARCH model.The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error(MAPE),Mean absolute scaled error(MASE)and Root means squared error(RMSE).

Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria:Current state of the art

Pathogenic microorganisms produce numerous metabolites,including volatile organic compounds(VOCs).Monitoring these metabolites in biological matrices(e.g.,urine,blood,or breath)can reveal the presence of specific microorganisms,enabling the early diagnosis of infections and the timely implementation of tar-geted therapy.However,complex matrices only contain trace levels of VOCs,and their constituent com-ponents can hinder determination of these compounds.Therefore,modern analytical techniques enabling the non-invasive identification and precise quantification of microbial VOCs are needed.In this paper,we discuss bacterial VOC analysis under in vitro conditions,in animal models and disease diagnosis in humans,including techniques for offline and online analysis in clinical settings.We also consider the advantages and limitations of novel microextraction techniques used to prepare biological samples for VOC analysis,in addition to reviewing current clinical studies on bacterial volatilomes that address inter-species in-teractions,the kinetics of VOC metabolism,and species-and drug-resistance specificity.

An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.

Impact of trading hours extensions on foreign exchange volatility:intraday evidence from the Moscow exchange

Using transaction-level tick-by-tick data of same-and next-day settlement of the Russian Ruble versus the US Dollar exchange rate(RUB/USD)traded on the Moscow Exchange Market during the period 2005–2013,we analyze the impact of trading hours extensions on volatility.During the sample period,the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate.To analyze the effect of the implementations,various measures of historical and realized volatility are calculated for 5-and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions.Besides historical volatility measures,we also examine volume and spread.We apply an autoregressive moving average-autoregressive conditional heteroscedasticity(ARMA-GARCH)model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading.The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes.Volatility changes mostly occur after the opening of the market.The length of the extension has a significant positive effect on realized volatility.The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement,whereas this is not observed for next-day settlement.Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets,they may also lead to higher volatility in the market.Furthermore,this distortion is more significant at opening and midday.A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

Exploring Nutritional Compositions,Volatile Compounds,Health Benefits,Emerging Processing Technologies,and Potential Food Products of Glutinous Rice:A Review

Glutinous rice(Oryza sativa var.glutinosa)stands out as one of the most popular rice varieties globally,amidst thousands of rice cultivars.Its increasing popularity is attributed to its rich nutritional compositions and health benefits.This review aims to summarize the nutritional compositions,volatile compounds,and health benefits of glutinous rice.Further,in-depth studies are necessary to explore the utilization of glutinous rice in enhancing processing technologies and developing new food products.Glutinous rice has been shown to possess numerous health benefits,including antioxidant activity,bioactive compounds,anti-cancer properties,anti-inflammatory effects,anti-diabetic potential,and cholesterol-lowering effects.Besides its nutritional compositions,the major volatile compounds identified in glutinous rice could serve as a functional food for human consumption.Emerging processing technologies related to glutinous rice are elaborated to improve the latest developments for incorporating them into various food products.

Identification of breath volatile organic compounds to distinguish pancreatic adenocarcinoma,pancreatic cystic neoplasm,and patients without pancreatic lesions

BACKGROUND Volatile organic compounds(VOCs)are a promising potential biomarker that may be able to identify the presence of cancers.AIM To identify exhaled breath VOCs that distinguish pancreatic ductal adenocar-cinoma(PDAC)from intraductal papillary mucinous neoplasm(IPMN)and healthy volunteers.METHODS We collected exhaled breath from histologically proven PDAC patients,radiological diagnosis IPMN,and healthy volunteers using the ReCIVA®device between 10/2021-11/2022.VOCs were identified by thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry and compared between groups.RESULTS A total of 156 participants(44%male,mean age 62.6±10.6)were enrolled(54 PDAC,42 IPMN,and 60 controls).Among the nine VOCs identified,two VOCs that showed differences between groups were dimethyl sulfide[0.73 vs 0.74 vs 0.94 arbitrary units(AU),respectively;P=0.008]and acetone dimers(3.95 vs 4.49 vs 5.19 AU,respectively;P<0.001).After adjusting for the imbalance parameters,PDAC showed higher dimethyl sulfide levels than the control and IPMN groups,with adjusted odds ratio(aOR)of 6.98(95%CI:1.15-42.17)and 4.56(1.03-20.20),respectively(P<0.05 both).Acetone dimer levels were also higher in PDAC compared to controls and IPMN(aOR:5.12(1.80-14.57)and aOR:3.35(1.47-7.63),respectively(P<0.05 both).Acetone dimer,but not dimethyl sulfide,performed better than CA19-9 in PDAC diagnosis(AUROC 0.910 vs 0.796).The AUROC of acetone dimer increased to 0.936 when combined with CA19-9,which was better than CA19-9 alone(P<0.05).CONCLUSION Dimethyl sulfide and acetone dimer are VOCs that potentially distinguish PDAC from IPMN and healthy participants.Additional prospective studies are required to validate these findings.

Pharmacological effects of volatile oil from chrysanthemum and its associated mechanisms:a review

Volatile oil(VO)is the main chemical component of common plants in Chrysanthemum genus,and it possesses several beneficial pharmacological properties,including bacteriostatic,antioxidant,anti-tumor,anti-inflammatory,antipyretic,analgesic,antiosteoporotic,antihypertensive,sedative,and hypnotic effects.To date,research on the effective components of Chrysanthemum extract has mainly focused on flavonoids,whereas limited data are available on the chemical constituents and underlying mechanisms of action of the VO components.In this review,the pharmacological activities and mechanisms of VO are comprehensively reviewed with the aim of providing a foundation for further development for medicinal,aromatherapy,and diet therapy applications.

Effects of ultrasonic-assisted extraction on bioactive compounds,volatile flavors and antioxidant activities of vine tea water extracts

Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigates the influence of different extraction methods(room temperature water extraction,boiling water extraction,ultrasonic-assisted room temperature water extraction,and ultrasonic-assisted boiling water extraction,referred to as room temperature water extraction(RE),boiling water extraction(BE),ultrasonic assistance at room temperature water extraction(URE),and ultrasonic assistance in boiling water extraction(UBE))on the yield,dihydromyricetin(DMY)content,free amino acid composition,volatile aroma components,and antioxidant properties of vine tea extracts.Results:A notable influence of extraction temperature on the yield of vine tea extracts(P<0.05),with BE yielding the highest at 43.13±0.26%,higher than that of RE(34.29±0.81%).Ultrasound-assisted extraction significantly increased the DMY content of the extracts(P<0.05),whereas DMY content in the RE extracts was 59.94±1.70%,that of URE reached 66.14±2.78%.Analysis revealed 17 amino acids,with L-serine and aspartic acid being the most abundant in the extracts,nevertheless ultrasound-assisted extraction reduced total free amino acid content.Gas chromatography-mass spectrometry analysis demonstrated an increase in the diversity and quantity of compounds in the vine tea water extracts obtained through ultrasonic-assisted extraction.Specifically,69 and 68 volatile compounds were found in URE and UBE extracts,which were higher than the number found in RE and BE extracts.In vitro,antioxidant activity assessments revealed varying antioxidant capacities among different extraction methods,with RE exhibiting the highest DPPH scavenging rate,URE leading in ABTS•+free radical scavenging,and BE demonstrating superior ferric ion reducing antioxidant activity.Conclusion:The findings suggest that extraction methods significantly influence the chemical composition and antioxidant properties of vine tea extracts.Ultrasonic-assisted extraction proved instrumental in elevating the DMY content in vine tea extracts,thereby enriching its flavor profile while maintaining its antioxidant properties.

Research Progress of Volatile Substances Analysis and Sensory Evaluation of Soymilk

Soybean is one of the important crops in China. Soymilk, a traditional neutral plant-based protein drink, is rich in high quality proteins. Although soybean milk is rich in nutrients, its marketing among consumers, especially those in Western countries who are used to peaceful flavor, has been limited due to the adverse flavor impact brought by its special composition. In recent years, with the increasing attention to the nutritional value of soymilk, the flavor of soymilk has become a popular research object for scholars at home and abroad. The flavor components of soymilk are mainly volatile small molecular compounds produced by enzymatic reactions catalyzed by lipoxygenase(LOX). After formation, they interact with protein macromolecules to form the overall flavor of soymilk. At present, there are many methods to control the off-odor of soymilk at home and abroad, including physical heating methods, chemical methods, biological enzymatic digestion methods, mask methods, and a variety of breeding methods. These methods effectively reduce the off-odor of soymilk, but all of them have shortcomings. Currently, the sensory characteristics of the beany odor in soymilk are evaluated mainly by traditional human sensory scoring along with the assistance of modern instrument analysis of volatile flavor substances using headspace solid phase microextraction(SPME) gas chromatography coupled with-mass spectrometry(GC-MS). This paper summarized the research results of volatile flavor substances in soymilk in recent years and the sensory evaluation methods of soymilk at home and abroad, and looked forward to the future development direction, hoping to provide some theoretical bases and reference detection methods for solving the problem of soymilk flavor in the future.

Devolatilization of high viscous fluids with high gravity technology

Volatile organic compounds(VOCs)are generally toxic and harmful substances that can cause health and environmental problems.The removal of VOCs from polymers has become a key problem.The effective devolatilization to remove VOCs from high viscous fluids such as polymer is necessary and is of great importance.In this study,the devolatilization effect of a rotating packed bed(RPB)was studied by using polydimethylsiloxane as the viscous fluid and acetone as the VOC.The devolatilization rate and liquid phase volume(KLa)have been evaluated.The results indicated that the optimum conditions were the high-gravity factor of 60,liquid flow rate of 10 L·h^(-1),and vacuum degree of 0.077 MPa.The dimensionless correlation of KLa was established,and the deviations between predicted and experimental values were less than±28%.The high-gravity technology will result in lower mass transfer resistance in the devolatilization process,enhance the mass transfer process of acetone,and improve the removal effect of acetone.This work provides a promising path for the removal of volatiles from polymers in combination with high-gravity technology.It can provide the basis for the application of RPB in viscous fluids.

GC-MS/MS Method for Determination and Pharmacokinetics of Main Components of Cang-Ai Volatile Oil in Rat Plasma after Intravenous Administration

[Objectives]To establish a gas chromatography-triple quadrupole mass spectrometry(GC-MS/MS)method based on multiple reaction monitoring(MRM)mode for the analysis of the major components in Cang-ai volatile oil(CAVO).[Methods]An ultrasensitive gas chromatography-tandem mass spectrometry(GC-MS/MS)method was developed and validated for the determination of three highly abundant components in rat plasma samples.Paeonol was used as an internal standard.A multiple reaction monitoring(MRM)model was employed for the quantification of the three major components of CAVO.[Results]The method demonstrated linearity over the range of 0.25 to 50μg/mL with a correlation coefficient(R 2)greater than 0.9998.The lower limit of quantification was 0.25μg/mL.Intra-day and inter-day accuracy and precision were within 15%.Extraction recovery and matrix effect values ranged from 90.1%to 110.6%and 0.1%to 2.1%,respectively.[Conclusions]This method was successfully applied to the simultaneous determination of the three components in high-level CAVO plasma samples,providing a basis for subsequent studies of CAVO.