Multi-Objective Multi-Dimensional Transportation: A Case Study to the Flow of the Commodities of the Main Roads to Main Nodes in the North Western Coastal Strip of Egypt

The distribution of merchandises and commodities from source towns to final destinations is a vital issue. The job of transporter’s decisions can be optimized by reformulating the transportation problem as generalization of the classical transportation problems. Multiobjective multi-dimensional transportation network is considered the extension of conventional two-dimensional transportation network and is convenient for dealing with transportation systems with multiple supply nodes, multiple demand nodes, as well as diverse modes of transportation demands or delivering multiple kinds of merchandises. In this study, we implement an improved Biogeography based optimization IBBO to the flow of the commodities of the main roads to main nodes in the North Western Coastal Strip of Egypt, where there are four main roads and three nodes. The proposed algorithm incorporates the dominance criteria to handle multiple objective functions which enable the decision maker to cover all the Pareto frontier of the problem which have a large-scale size. Numerical results were reported in order to establish the real computational burden of the proposed algorithm and to assess its convergence performances for solving real geographical problem.

Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Envelope Method and More General New Global Structures of Solutions for Multi-dimensional Conservation Law

For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.

Investigating the elliptic anisotropy of identified particles in p-Pb collisions with a multi-phase transport model

The elliptic azimuthal anisotropy coefficient(v_(2))of the identified particles at midrapidity(|η|<0.8)was investigated in p-Pb collisions at√s_(NN)=5.02 TeV using a multi-phase transport model(AMPT).The calculations of differential v_(2)based on the advanced flow extraction method of light flavor hadrons(pions,kaons,protons,andΛ)in small collision systems were extended to a wider transverse momentum(p_(T))range of up to 8 GeV/c for the first time.The string-melting version of the AMPT model provides a good description of the measured p_(T)-differential v_(2)of the mesons but exhibits a slight deviation from the baryon v_(2).In addition,we observed the features of mass ordering at low p_(T)and the approximate number-of-constituentquark(NCQ)scaling at intermediate p_(T).Moreover,we demonstrate that hadronic rescattering does not have a significant impact on v_(2)in p-Pb collisions for different centrality selections,whereas partonic scattering dominates in generating the elliptic anisotropy of the final particles.This study provides further insight into the origin of collective-like behavior in small collision systems and has referential value for future measurements of azimuthal anisotropy.

Research and Practice on the Multi-dimensional Course Goal Achievement Evaluation of Software Engineering

According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.

Host-induced gene silencing of the Verticillium dahliae thiamine transporter protein gene(VdThit)confers resistance to Verticillium wilt in cotton

Verticillium wilt(VW),induced by the soil-borne fungus Verticillium dahliae(Vd),poses a substantial threat to a diverse array of plant species.Employing molecular breeding technology for the development of cotton varieties with heightened resistance to VW stands out as one of the most efficacious protective measures.In this study,we successfully generated two stable transgenic lines of cotton(Gossypium hirsutum L.),VdThitRNAi-1 and VdThit-RNAi-2,using host-induced gene silencing(HIGS)technology to introduce double-stranded RNA(dsRNA)targeting the thiamine transporter protein gene(VdThit).Southern blot analysis confirmed the presence of a single-copy insertion in each line.Microscopic examination showed marked reductions in the colonization and spread of Vd-mCherry in the roots of VdThit-RNAi cotton compared to wild type(WT).The corresponding disease index and fungal biomass of VdThit-RNAi-1/2 also exhibited significant reductions.Real-time quantitative PCR(qRT-PCR)analysis demonstrated a substantial inhibition of VdThit expression following prolonged inoculation of VdThit-RNAi cotton.Small RNA sequencing(sRNA-Seq)analysis revealed the generation of a substantial number of VdThit-specific siRNAs in the VdThit-RNAi transgenic lines.Additionally,the silencing of VdThit by the siVdThit produced by VdThit-RNAi-1/2 resulted in the elevated expression of multiple genes involved in the thiamine biosynthesis pathway in Vd.Under field conditions,VdThit-RNAi transgenic cotton exhibited significantly enhanced disease resistance and yield compared with WT.In summary,our findings underscore the efficacy of HIGS targeting VdThit in restraining the infection and spread of Vd in cotton,thereby potentially enabling the development of cotton breeding as a promising strategy for managing VW.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

Identification of the lysine and histidine transporter family in Camellia sinensis and the characterizations in nitrogen utilization

In plants,the lysine and histidine transporter(LHT)family represent a class of proteins that mediate the uptake,translocation,and utilization of amino acids.The tea plant(Camellia sinensis)is a perennial evergreen with a relatively high level of amino acids.However,systematic identification and molecular characterization of the LHT gene family has rarely been reported in tea plants.In this study,22 CsLHTs were identified from the‘Shuchazao’genome and classified into two groups.The modeled three-dimensional structure and the conserved domains presented a high similarity among the LHTs proteins.Moreover,it was predicted that a few genes were conserved through the analysis of the physiochemical characters,structures and cis-elements in promoters.The expression patterns in tea plants revealed that CsLHT7 was mainly expressed in the roots,and CsLHT4 and CsLHT11 exhibited relatively high expression in both the roots and leaves.Moreover,the expression of all three genes could be induced by organic nitrogen.Additionally,heterogeneous expression of CsLHT4,CsLHT7 and CsLHT11 in Arabidopsis thaliana decreased the aerial parts biomass compared with that in WT plants while significantly increased the rosette biomass only for CsLHT11transgenic plants versus WT plants.Overall,our results provide fundamental information about CsLHTs and potential genes in N utilization for further analysis in tea plants.

Hydromechanical characterization of gas transport amidst uncertainty for underground nuclear explosion detection

Given the challenge of definitively discriminating between chemical and nuclear explosions using seismic methods alone,surface detection of signature noble gas radioisotopes is considered a positive identification of underground nuclear explosions(UNEs).However,the migration of signature radionuclide gases between the nuclear cavity and surface is not well understood because complex processes are involved,including the generation of complex fracture networks,reactivation of natural fractures and faults,and thermo-hydro-mechanical-chemical(THMC)coupling of radionuclide gas transport in the subsurface.In this study,we provide an experimental investigation of hydro-mechanical(HM)coupling among gas flow,stress states,rock deformation,and rock damage using a unique multi-physics triaxial direct shear rock testing system.The testing system also features redundant gas pressure and flow rate measurements,well suited for parameter uncertainty quantification.Using porous tuff and tight granite samples that are relevant to historic UNE tests,we measured the Biot effective stress coefficient,rock matrix gas permeability,and fracture gas permeability at a range of pore pressure and stress conditions.The Biot effective stress coefficient varies from 0.69 to 1 for the tuff,whose porosity averages 35.3%±0.7%,while this coefficient varies from 0.51 to 0.78 for the tight granite(porosity<1%,perhaps an underestimate).Matrix gas permeability is strongly correlated to effective stress for the granite,but not for the porous tuff.Our experiments reveal the following key engineering implications on transport of radionuclide gases post a UNE event:(1)The porous tuff shows apparent fracture dilation or compression upon stress changes,which does not necessarily change the gas permeability;(2)The granite fracture permeability shows strong stress sensitivity and is positively related to shear displacement;and(3)Hydromechanical coupling among stress states,rock damage,and gas flow appears to be stronger in tight granite than in porous tuff.

Global characterization of OsPIP aquaporins reveals that the H_(2)O_(2)transporter OsPIP2;6 increases resistance to rice blast

Plasma membrane intrinsic proteins(PIPs)are conserved plant aquaporins that transport small molecules across the plasma membrane to trigger instant stress responses and maintain cellular homeostasis under biotic and abiotic stress.To elucidate their roles in plant immunity to pathogen attack,we characterized the expression patterns,subcellular localizations,and H_(2)O_(2)-transport ability of 11 OsPIPs in rice(Oryza sativa),and identified OsPIP2;6 as necessary for rice disease resistance.OsPIP2;6 resides on the plasma membrane and facilitates cytoplasmic import of the immune signaling molecule H_(2)O_(2).Knockout of OsPIP2;6 increases rice susceptibility to Magnaporthe oryzae,indicating a positive function in plant immunity.OsPIP2;6 interacts with OsPIP2;2,which has been reported to increase rice resistance to pathogens via H_(2)O_(2)transport.Our findings suggest that OsPIP2;6 cooperates with OsPIP2;2 as a defense signal transporter complex during plant–pathogen interaction.

Sodium-dependent glucose transporter 2 inhibitors effects on myocardial function in patients with type 2 diabetes and asymptomatic heart failure

BACKGROUND Sodium-dependent glucose transporter 2 inhibitors(SGLT2i)have shown efficacy in reducing heart failure(HF)burden in a very heterogeneous groups of patients,raising doubts about some contemporary assumptions of their mechanism of action.We previously published a prospective observational study that evaluated mechanisms of action of SGLT2i in patients with type 2 diabetes who were in HF stages A and B on dual hypoglycemic therapy.Two groups of patients were included in the study:the ones receiving SGLT2i as an add-on agent to metformin and the others on dipeptidyl peptidase-4 inhibitors as an add-on to metformin due to suboptimal glycemic control.AIM To evaluate the outcomes regarding natriuretic peptide,oxidative stress,inflammation,blood pressure,heart rate,cardiac function,and body weight.METHODS The study outcomes were examined by dividing each treatment arm into two subgroups according to baseline parameters of global longitudinal strain(GLS),N-terminal pro-brain natriuretic peptide,myeloperoxidase(MPO),high-sensitivity C-reactive protein(hsCRP),and systolic and diastolic blood pressure.To evaluate the possible predictors of observed changes in the SGLT2i arm during follow-up,a rise in stroke volume index,body mass index(BMI)decrease,and lack of heart rate increase,linear regression analysis was performed.RESULTS There was a greater reduction of MPO,hsCRP,GLS,and blood pressure in the groups with higher baseline values of mentioned parameters irrespective of the therapeutic arm after 6 months of follow-up.Significant independent predictors of heart rate decrease were a reduction in early mitral inflow velocity to early diastolic mitral annular velocity at the interventricular septal annulus ratio and BMI,while the predictor of stroke volume index increase was SGLT2i therapy itself.CONCLUSION SGLT2i affect body composition,reduce cardiac load,improve diastolic/systolic function,and attenuate the sympathetic response.Glycemic control contributes to the improvement of heart function,blood pressure control,oxidative stress,and reduction in inflammation.

Inward particle transport driven by biased endplate in a cylindrical magnetized plasma

The inward particle transport is associated with the formation of peaked density profiles,which contributes to improve the fusion rate and the realization of steady-state discharge.The active control of inward particle transport is considered as one of the most critical issues of magnetic confinement fusion.Recently,it is realized preliminarily by adding a biased endplate in the Peking University Plasma Test(PPT)device.The results reveal that the inward particle flux increases with the bias voltage of the endplate.It is also found that the profile of radial electric field(Er)shear is flattened by the increased bias voltage.Radial velocity fluctuations affect the inward particle more than density fluctuations,and the frequency of the dominant mode driving inward particle flux increases with the biased voltage applied to the endplate.The experimental results in the PPT device provide a method to actively control the inward particle flux using a biased endplate and enrich the understanding of the relationship between E_(r)×B shear and turbulence transport.

Impact of T_(i)/T_(e )ratio on ion transport based on EAST H-mode plasmas

At the EAST tokamak, the ion temperature(T_(i)) is observed to be clamped around 1.25 keV in electron cyclotron resonance(ECR)-heated plasmas, even at core electron temperatures up to 10 keV(depending on the ECR heating power and the plasma density). This clamping results from the lack of direct ion heating and high levels of turbulence-driven transport. Turbulent transport analysis shows that trapped electron mode and electron temperature gradient-driven modes are the most unstable modes in the core of ECR-heated H-mode plasmas. Nevertheless, recently it was found that the T_(i)/T_(e)ratio can increase further with the fraction of the neutral beam injection(NBI) power, which leads to a higher core ion temperature(Ti0). In NBI heating-dominant H-mode plasmas, the ion temperature gradient-driven modes become the most unstable modes.Furthermore, a strong and broad internal transport barrier(ITB) can form at the plasma core in high-power NBI-heated H-mode plasmas when the T_(i)/T_(e)ratio approaches ~1, which results in steep core Teand Tiprofiles, as well as a peaked neprofile. Power balance analysis shows a weaker Teprofile stiffness after the formation of ITBs in the core plasma region, where Ticlamping is broken,and the core Tican increase further above 2 keV, which is 80% higher than the value of Ticlamping in ECR-heated plasmas. This finding proposes a possible solution to the problem of Ticlamping on EAST and demonstrates an advanced operational regime with the formation of a strong and broad ITB for future fusion plasmas dominated by electron heating.

A new review of single-ion conducting polymer electrolytes in the light of ion transport mechanisms

With the depletion of fossil fuels and the demand for high-performance energy storage devices,solidstate lithium metal batteries have received widespread attention due to their high energy density and safety advantages.Among them,the earliest developed organic solid-state polymer electrolyte has a promising future due to its advantages such as good mechanical flexibility,but its poor ion transport performance dramatically limits its performance improvement.Therefore,single-ion conducting polymer electrolytes(SICPEs)with high lithium-ion transport number,capable of improving the concentration polarization and inhibiting the growth of lithium dendrites,have been proposed,which provide a new direction for the further development of high-performance organic polymer electrolytes.In view of this,lithium ions transport mechanisms and design principles in SICPEs are summarized and discussed in this paper.The modification principles currently used can be categorized into the following three types:enhancement of lithium salt anion-polymer interactions,weakening of lithium salt anion-cation interactions,and modulation of lithium ion-polymer interactions.In addition,the advances in single-ion conductors of conventional and novel polymer electrolytes are summarized,and several typical highperformance single-ion conductors are enumerated and analyzed in what way they improve ionic conductivity,lithium ions mobility,and the ability to inhibit lithium dendrites.Finally,the advantages and design methodology of SICPEs are summarized again and the future directions are outlined.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Effects of Rattling Behavior of K and Cd Atoms along Different Directions in Anisotropic KCdAs on Lattice Thermal Transport and Thermoelectric Properties

We employ advanced first principles methodology,merging self-consistent phonon theory and the Boltzmann transport equation,to comprehensively explore the thermal transport and thermoelectric properties of KCdAs.Notably,the study accounts for the impact of quartic anharmonicity on phonon group velocities in the pursuit of lattice thermal conductivity and investigates 3ph and 4ph scattering processes on phonon lifetimes.Through various methodologies,including examining atomic vibrational modes and analyzing 3ph and 4ph scattering processes,the article unveils microphysical mechanisms contributing to the lowκL within KCdAs.Key features include significant anisotropy in Cd atoms,pronounced anharmonicity in K atoms,and relative vibrations in non-equivalent As atomic layers.Cd atoms,situated between As layers,exhibit rattling modes and strong lattice anharmonicity,contributing to the observed lowκL.Remarkably flat bands near the valence band maximum translate into high PF,aligning with ultralowκL for exceptional thermoelectric performance.Under optimal temperature and carrier concentration doping,outstanding ZT values are achieved:4.25(a(b)-axis,p-type,3×10^(19)cm^(−3),500 K),0.90(c-axis,p-type,5×10^(20)cm^(−3),700 K),1.61(a(b)-axis,n-type,2×10^(18)cm^(−3),700 K),and 3.06(c-axis,n-type,9×10^(17)cm^(−3),700 K).

Increased excitatory amino acid transporter 2 levels in basolateral amygdala astrocytes mediate chronic stress–induced anxiety-like behavior

The conventional perception of astrocytes as mere supportive cells within the brain has recently been called into question by empirical evidence, which has revealed their active involvement in regulating brain function and encoding behaviors associated with emotions.Specifically, astrocytes in the basolateral amygdala have been found to play a role in the modulation of anxiety-like behaviors triggered by chronic stress. Nevertheless, the precise molecular mechanisms by which basolateral amygdala astrocytes regulate chronic stress–induced anxiety-like behaviors remain to be fully elucidated. In this study, we found that in a mouse model of anxiety triggered by unpredictable chronic mild stress, the expression of excitatory amino acid transporter 2 was upregulated in the basolateral amygdala. Interestingly, our findings indicate that the targeted knockdown of excitatory amino acid transporter 2 specifically within the basolateral amygdala astrocytes was able to rescue the anxiety-like behavior in mice subjected to stress. Furthermore, we found that the overexpression of excitatory amino acid transporter 2 in the basolateral amygdala, whether achieved through intracranial administration of excitatory amino acid transporter 2agonists or through injection of excitatory amino acid transporter 2-overexpressing viruses with GfaABC1D promoters, evoked anxiety-like behavior in mice. Our single-nucleus RNA sequencing analysis further confirmed that chronic stress induced an upregulation of excitatory amino acid transporter 2 specifically in astrocytes in the basolateral amygdala. Moreover, through in vivo calcium signal recordings, we found that the frequency of calcium activity in the basolateral amygdala of mice subjected to chronic stress was higher compared with normal mice.After knocking down the expression of excitatory amino acid transporter 2 in the basolateral amygdala, the frequency of calcium activity was not significantly increased, and anxiety-like behavior was obviously mitigated. Additionally, administration of an excitatory amino acid transporter 2 inhibitor in the basolateral amygdala yielded a notable reduction in anxiety level among mice subjected to stress. These results suggest that basolateral amygdala astrocytic excitatory amino acid transporter 2 plays a role in in the regulation of unpredictable chronic mild stress-induced anxiety-like behavior by impacting the activity of local glutamatergic neurons, and targeting excitatory amino acid transporter 2 in the basolateral amygdala holds therapeutic promise for addressing anxiety disorders.