Anxiety disorder is a psychiatric condition characterized by excessive fear and anxiety as its core features,often accompanied by behavioral abnormalities.Severe anxiety and depression can trigger suicidal attempts or behaviors,which has attracted extensive public attention.As a safe and effective complementary therapy,aromatherapy demonstrates clear potential in alleviating mild to moderate anxiety.To investigate the role of aromatherapy in emotional regulation,this study examined the effects and underlying mechanisms of vetiver volatile extracts on anxiety-like behaviors induced by chronic unpredictable mild stress (CUMS) in mice.Twenty-eight male C57BL/6J mice were randomly assigned to four groups (n=7 per group):control group,model group,vetiver volatile extract group,and positive control group (fluoxetine hydrochloride,3 mg/kg).An anxiety mouse model was established using CUMS.After 18 days of modeling,mice in the vetiver volatile extract group were exposed to vetiver volatile extracts for 14 days before the experiment concluded.Behavioral changes were evaluated using the sucrose preference test,open field test,and forced swimming test.Hippocampal levels of tryptophan (TRP) and serotonin (5-HT),as well as serum concentrations of TNF-α,IL-6 and IL-1β,were measured by enzyme-linked immunosorbent assay (ELISA).The results revealed that in behavioral tests,compared with the model group,both the vetiver volatile extract group and the positive control group showed significantly increased sucrose preference rates,greater distance traveled and time spent in the central area,and a notable rise in vertical rearing episodes.Regarding physiological indicators,the vetiver volatile extract group reduced serum levels of IL-6,IL-1β and TNF-α in model mice and upregulated the mass concentration of TRP and 5-HT in the hippocampus.In summary,vetiver volatile extracts effectively alleviated anxiety-like behaviors in mice by modulating neurotransmitter balance and inhibiting inflammatory responses.

Kluyveromyces marxianus is a thermotolerant,non-conventional yeast.Pyruvate decarboxylase(Pdc1) catalyzes the conversion of pyruvate to acetaldehyde and is a key factor for ethanol fermentation in K.marxianus.In this study,a K.marxianus strain,KM-502,was isolated from kefir grains.Genomic sequencing predicted 6 801 genes.Growth trait analysis revealed that this strain grew fastest at 37 ℃,had an optimal growth pH from 3 to 5,a NaCl tolerance threshold of 8%,and could utilize carbon sources including glucose,fructose,sucrose,lactose,galactose,xylose,and xylitol.Subsequently,a genetic manipulation system for K.marxianus strains was established based on CRISPR-Cas9 and Cre-loxP technologies.This system was used to construct the Pdc1 gene deletion strain KMΔPdc1 and a uracil auxotrophic strain KMΔUra3.The KMΔPdc1strain lost its ethanol production capability and exhibited significant growth lag.After continuous subculturing of KMΔPdc1,an adapted strain,KMΔPdc1D,was obtained.The growth lag in KMΔPdc1D was alleviated,recovering to a level close to that of the wild-type strain.Compared to KMΔPdc1,KMΔPdc1D showed increased production of acetate and glycerol.This study serves as a reference for the utilization and development of Kluyveromyces marxianus.

Appropriate cold acclimation treatment can improve the cold tolerance of cold-sensitive vegetables and extend their shelf life.This study used green peppers as the research subjects.First,the peppers were cold-conditioned at 0 ℃ for 15 or 45 minutes and then stored at 10 ℃ for 30 days.The effects of cold acclimation treatment on the redox system,membrane lipid composition were analyzed to provide a basis for identifying cold-resistance targets.Results showed that cold acclimation treatment at 0 ℃ for 45 minutes delayed the loss of titratable acid,chlorophyll,total sugar,and reducing sugar during storage;effectively maintained flavonoid and reduced glutathione levels;and significantly inhibited malondialdehyde accumulation.At the end of storage,malondialdehyde levels in the treated groups were only 19.4% of those in the control group.Cold acclimation treatment also significantly delayed the inactivation of CAT and POD.At the end of storage,CAT and POD activities in the 0 ℃ for 45 minutes group were 47.06% and 40.05% higher than those in the control group,respectively.After one day of storage,the total unsaturated fatty acids(UFAs) content of green peppers reached 14.20% after 45 minutes of cold acclimation treatment at 0 ℃,a 2.40% increase compared to the control group.These results suggest that appropriate cold acclimation treatment can improve the nutritional quality of green peppers during storage,increase antioxidant capacity,regulate physiological metabolism,and effectively extend their storage life.

A high-quality agricultural product supply chain can effectively synchronize supply and demand across urban and rural markets,while rural e-commerce generates new impetus for rural revitalization.Using provincial panel data from China for 2011 to 2022,this paper applies fixed-effects estimations and heterogeneity analysis to empirically assess the influence of rural e-commerce on the development level of agricultural product supply chains.Findings indicate that the expansion of rural e-commerce substantially enhances the development level of these supply chains,a result that withstands robustness checks.Heterogeneity analysis further shows that the positive impact of rural e-commerce is more pronounced in eastern regions and in provinces where agriculture is not the dominant sector.Consequently,all provinces should capitalize on the opportunities afforded by rural e-commerce,leverage supply-chain mechanisms to meet agricultural product demand,and devise differentiated development strategies.

Based on a cubic nonlinear memristor,a novel complex chaotic system is proposed.Through dynamical analysis including dissipativity,sensitivity to initial conditions,stability of equilibrium points,bifurcation diagrams,Lyapunov exponent spectra,time series,phase portraits,and 0-1 tests,it is demonstrated that superior chaotic characteristics are exhibited by the memristive complex Lorenz system,which provides enhanced security for cryptographic applications.The encryptionof grayscale images isperformedusing a novel scrambling-diffusionalgorithmthat harnesses the chaotic time series producedby aninnovative system.Through security analysis of the image encryption scheme,the following conclusions are obtained:the key space designadoptedinthis scheme isdesignedtomeet cryptographic securityrequirements;the histogram of the encrypted image exhibits a uniform distribution;the correlation coefficients between adjacent pixels are significantly reduced and approach zero;the information entropy is close to the theoretical maximum of 8;and the system demonstrates strong robustness against differential attacks.

Wheat is an important cereal crop,and accurate detection of its growth period and other growth information is an important foundation for ensuring quality and yield.In recent years,artificial intelligence technologies such as computer vision have been widely applied in the field of agriculture,but traditional convolutional neural networks (CNNs) have limitations in extracting global features under complex lighting and background variations.This article takes the four growth stages of wheat,namely “germination,” “tillering,” “heading,” and “maturity,” as the research objects.A new state space modeling network VMamba is introduced into the wheat growth stage detection task,and a wheat growth stage detection model based on VMamba is constructed.Comparative experiments are conducted with mainstream models such as ResNet50,MobileNetV3,and ViT.The experimental results show that the VMamba model outperforms the comparison model in terms of accuracy and F1 score indicators,and also has advantages in inference speed and parameter quantity.Overall,it exhibits better classification and detection performance.In addition,the feasibility and effectiveness of the VMamba model in agricultural visual inspection were verified through actual deployment on the Nvidia Jetson Orin platform,providing an implementation approach for intelligent crop monitoring.

The current new energy vehicle industry is developing rapidly.Driven by policies and the economy,the recycling and utilization of lithium iron phosphate batteries have been moving towards a large-scale direction.According to statistics,currently,lithium iron phosphate batteries account for over 60% of the total retired batteries in China,but the recycling rate is less than 50%,and the poor economic performance is a bottleneck that needs to be urgently broken through.Therefore,analyzing the economic critical point of lithium iron phosphate battery recycling is of great significance for improving the recycling rate of lithium iron phosphate batteries and building a standardized,safe and efficient recycling and utilization system.This study integrates multi-dimensional data including policy texts and market prices from 2018 to 2025,and constructs a dynamic analysis framework.It adopts an integrated analytical framework of game theory-system dynamics-Bayesian hierarchical hybrid model.First,it defines the game subjects and objectives of the government and recycling enterprises (represented by GEM and CATL),designs strategy sets for subsidy intensity and production capacity strategies,and solves the subgame perfect Nash equilibrium in different metal price ranges through payoff functions.Based on this,a hybrid model is constructed to quantify the dynamic interaction between policy subsidies and metal prices,and describe the extreme scenarios through Monte Carlo simulation.After empirical calibration and convergence validation,it is clarified that the economic threshold fluctuates in the range of 79 000 to 115 000 yuan/ton.For every 100 yuan/ton increase in subsidy intensity,the threshold decreases by 5 000 to 8 000 yuan/ton;while for every 10% increase in lithium price volatility,the threshold rises by 3 000 to 5 000 yuan/ton.The research results can provide a quantitative basis for industrial policy optimization and enterprise operations,play an important role in improving the recycling rate of lithium iron phosphate batteries,and help achieve the dual goals of resource security and economic sustainable development.

In semi-parametric models,estimation robustness is often compromised by nonlinear trends or model misspecification.Traditional doubly robust estimators,which rely heavily on mean-oriented assumptions and propensity score models,frequently fail to deliver reliable inference.This paper develops an enhanced semi-parametric quantile doubly robust estimation model that integrates local linear quantile regression to simultaneously optimize both the nonparametric component g(X) and the parametric component β(τ),thereby obtaining efficient estimators.Numerical simulations—conducted under scenarios where at least one of g(X) or β(τ) is misspecified—show that the proposed model achieves significantly lower bias and root mean square error compared to conventional linear quantile regression.Specifically,bias is reduced by approximately 45%,highlighting the models superior robustness.By adopting a joint optimization strategy for both components,the proposed framework exhibits stronger robustness than existing mean-based models,making it particularly suitable for applications involving model misspecification or nonlinear trends