基于Q-Learning的分簇无线传感网信任管理机制

针对无线传感器网络中存在的安全问题,提出了基于Q-Learning的分簇无线传感网信任管理机制(Q-learning based trust management mechanism for clustered wireless sensor networks,QLTMM-CWSN).该机制主要考虑通信信任、数据信任和能量信任3个方面.在网络运行过程中,基于节点的通信行为、数据分布和能量消耗,使用Q-Learning算法更新节点信任值,并选择簇内信任值最高的节点作为可信簇头节点.当簇中主簇头节点的信任值低于阈值时,可信簇头节点代替主簇头节点管理簇内成员节点,维护正常的数据传输.研究结果表明,QLTMM-CWSN机制能有效抵御通信攻击、伪造本地数据攻击、能量攻击和混合攻击.

Optimal Cyber Attack Strategy Using Reinforcement Learning Based onCommon Vulnerability Scoring System

Currently,cybersecurity threats such as data breaches and phishing have been on the rise due to the many differentattack strategies of cyber attackers,significantly increasing risks to individuals and organizations.Traditionalsecurity technologies such as intrusion detection have been developed to respond to these cyber threats.Recently,advanced integrated cybersecurity that incorporates Artificial Intelligence has been the focus.In this paper,wepropose a response strategy using a reinforcement-learning-based cyber-attack-defense simulation tool to addresscontinuously evolving cyber threats.Additionally,we have implemented an effective reinforcement-learning-basedcyber-attack scenario using Cyber Battle Simulation,which is a cyber-attack-defense simulator.This scenarioinvolves important security components such as node value,cost,firewalls,and services.Furthermore,we applieda new vulnerability assessment method based on the Common Vulnerability Scoring System.This approach candesign an optimal attack strategy by considering the importance of attack goals,which helps in developing moreeffective response strategies.These attack strategies are evaluated by comparing their performance using a variety ofReinforcement Learning methods.The experimental results show that RL models demonstrate improved learningperformance with the proposed attack strategy compared to the original strategies.In particular,the success rateof the Advantage Actor-Critic-based attack strategy improved by 5.04 percentage points,reaching 10.17%,whichrepresents an impressive 98.24%increase over the original scenario.Consequently,the proposed method canenhance security and risk management capabilities in cyber environments,improving the efficiency of securitymanagement and significantly contributing to the development of security systems.

Network Defense Decision-Making Based on Deep Reinforcement Learning and Dynamic Game Theory

Existing researches on cyber attackdefense analysis have typically adopted stochastic game theory to model the problem for solutions,but the assumption of complete rationality is used in modeling,ignoring the information opacity in practical attack and defense scenarios,and the model and method lack accuracy.To such problem,we investigate network defense policy methods under finite rationality constraints and propose network defense policy selection algorithm based on deep reinforcement learning.Based on graph theoretical methods,we transform the decision-making problem into a path optimization problem,and use a compression method based on service node to map the network state.On this basis,we improve the A3C algorithm and design the DefenseA3C defense policy selection algorithm with online learning capability.The experimental results show that the model and method proposed in this paper can stably converge to a better network state after training,which is faster and more stable than the original A3C algorithm.Compared with the existing typical approaches,Defense-A3C is verified its advancement.

Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets,and Future Directions

In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.

Monitoring and Early Warning of New Cyber-Telecom Crime Platform Based on BERT Migration Learning

The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.

Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.

Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their opinions.This increases the amount of material being shared on social media sites.There is a wealth of information about the threat that may be found in such open data sources.The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information.Despite the fact that several models for detecting cybersecurity events have been presented,it remains challenging to extract security events from the vast amounts of unstructured text present in public data sources.The majority of the currently available methods concentrate on detecting events that have a high number of dimensions.This is because the unstructured text in open data sources typically contains a large number of dimensions.However,to react to attacks quicker than they can be launched,security analysts and information technology operators need to be aware of critical security events as soon as possible,regardless of how often they are reported.This research provides a unique event detection method that can swiftly identify significant security events from open forums such as Twitter.The proposed work identified new threats and the revival of an attack or related event,independent of the volume of mentions relating to those events on Twitter.In this research work,deep learning has been used to extract predictive features from open-source text.The proposed model is composed of data collection,data transformation,feature extraction using deep learning,Latent Dirichlet Allocation(LDA)based medium-level cyber-event detection and final Google Trends-based high-level cyber-event detection.The proposed technique has been evaluated on numerous datasets.Experiment results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96% accuracy.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Effect of Panax notoginseng saponins on the expression of beta-amyloid protein in the cortex of the parietal lobe and hippocampus, and spatial learning and memory in a mouse model of senile dementia

BACKGROUND： The pharmacological actions of Panax notoginseng saponins （PNS） lie in removing free radicals, anti-inflammation and anti-oxygenation. It can also improve memory and behavior in rat models of Alzheimer＇s disease. OBJECTIVE： Using the Morris water maze, immunohistochemistry, real-time PCR and RT-PCR, this study aimed to measure improvement in spatial learning, memory, expression of amyloid precursor protein （App） and β -amyloid （A β ）, to investigate the mechanism of action of PNS in the treatment of AD in the senescence accelerated mouse-prone 8 （SAMP8） and compare the effects with huperzine A. DESIGN, TIME AND SETTING： A completely randomized grouping design, controlled animal experiment was performed in the Center for Research ＆ Development of New Drugs, Guangxi Traditional Chinese Medical University from July 2005 to April 2007. MATERIALS： Sixty male SAMP8 mice, aged 3 months, purchased from Tianjin Chinese Traditional Medical University of China, were divided into four groups： PNS high-dosage group, PNS low-dosage group, huperzine A group and control group. PNS was provided by Weihe Pharmaceutical Co., Ltd. （batch No.： Z53021485, Yuxi, Yunan Province, China）. Huperzine A was provided by Zhenyuan Pharmaceutical Co., Ltd. （batch No.： 20040801, Zhejiang, China）. METHODS： The high-dosage group and low-dosage group were treated with 93.50 and 23.38 mg/kg PNS respectively per day and the huperzine A group was treated with 0.038 6 mg/kg huperzine A per day, all by intragastric administration, for 8 consecutive weeks. The same volume of double distilled water was given to the control group. MAIN OUTCOME MEASURES： After drug administration, learning and memory abilities were assessed by place navigation and spatial probe tests. The recording indices consisted of escape latency （time-to-platform）, and the percentage of swimming time spent in each quadrant. The number of A β 1-40, A β 1-42 and App immunopositive neurons in the brains of SAMP8 mice was analyzed by immunohistochemistry. The mRNA content ofApp, tau, acetylcholinesterase, and synaptophysin （Syp） was tested by real time PCR and RT-PCR. RESULTS： The PCR results show that PNS can downregulate the expression of the App gene and upregulate the expression of the Syp gene in the parietal cortex and hippocampus of SAMP8 mice. The therapeutic effects of the PNS high-dosage group were greater than those of the PNS low-dosage group and the huperzine A group （P 〈 0.05）. The results of the Morris water maze and immunohistochemistry indicated that PNS can improve the capacity for spatial learning and memory in SAMP8 mice, and reduce the content of A β 1-40, A β 1-42 and expression of App in the brains of SAMP8 mice. The therapeutic effects of the PNS high-dosage group were greater than that of the PNS low-dosage group and the huperzine A group （P 〈 0.05）. CONCLUSION： These results support the hypothesis that PNS plays a therapeutic and protective role on the pathological lesions and learning dysfunction of Alzheimer＇s disease. The therapeutic effects of PNS for Alzheimer＇s disease are possibly achieved through downregulating the expression of the App gene and upregulating the expression of the Syp gene. The therapeutic effects of PNS are dose-dependent and are greater than the effect of huperzine A.

医学生-病友参与式健康管理对合并2型糖尿病的脑梗死患者生活质量的影响

目的探讨医学生-病友参与式健康管理对合并2型糖尿病的脑梗死患者生活质量的影响。方法选取首次发作、美国国立卫生研究院卒中量表(National Institute of Health stroke scale,NIHSS)评分为1~4分且合并2型糖尿病的脑梗死患者随机分为对照组(49例)和研究组(47例),对照组行常规健康管理,研究组在此基础上,结合Learns模式,引入医学生和病友参与。对比管理前后2组患者血糖水平、生活质量、Barthel指数(Barthel Index,BI)等变化。采用Spearman相关性分析探讨SF-36评分与BI评分的相关性。结果干预前,2组FBG、2 h PG、HbA1c、BI评分比较差异无统计学意义(P>0.05);干预后,研究组FBG、2 h PG、HbA1c、BI评分均优于对照组(P<0.05)。研究组SF-36评分的8个维度评分均优于对照组(P<0.05)。Spearman相关性分析结果显示:干预后研究组精力、社会活动、情感职能与BI正相关(r分别为0.469、0.758、0.453,P<0.01)。结论医学生-病友参与式健康管理能够改善合并2型糖尿病的脑梗死患者的血糖水平、改善生活质量、日常生活活动能力;关注患者的精力、社会活动、情感职能有助于提高其生活质量。

Cyberspace Security Using Adversarial Learning and Conformal Prediction

This paper advances new directions for cyber security using adversarial learning and conformal prediction in order to enhance network and computing services defenses against adaptive, malicious, persistent, and tactical offensive threats. Conformal prediction is the principled and unified adaptive and learning framework used to design, develop, and deploy a multi-faceted?self-managing defensive shield to detect, disrupt, and deny intrusive attacks, hostile and malicious behavior, and subterfuge. Conformal prediction leverages apparent relationships between immunity and intrusion detection using non-conformity measures characteristic of affinity, a typicality, and surprise, to recognize patterns and messages as friend or foe and to respond to them accordingly. The solutions proffered throughout are built around active learning, meta-reasoning, randomness, distributed semantics and stratification, and most important and above all around adaptive Oracles. The motivation for using conformal prediction and its immediate off-spring, those of semi-supervised learning and transduction, comes from them first and foremost supporting discriminative and non-parametric methods characteristic of principled demarcation using cohorts and sensitivity analysis to hedge on the prediction outcomes including negative selection, on one side, and providing credibility and confidence indices that assist meta-reasoning and information fusion.

Optimizing Network Security via Ensemble Learning: A Nexus with Intrusion Detection

Network intrusion detection systems need to be updated due to the rise in cyber threats. In order to improve detection accuracy, this research presents a strong strategy that makes use of a stacked ensemble method, which combines the advantages of several machine learning models. The ensemble is made up of various base models, such as Decision Trees, K-Nearest Neighbors (KNN), Multi-Layer Perceptrons (MLP), and Naive Bayes, each of which offers a distinct perspective on the properties of the data. The research adheres to a methodical workflow that begins with thorough data preprocessing to guarantee the accuracy and applicability of the data. In order to extract useful attributes from network traffic data—which are essential for efficient model training—feature engineering is used. The ensemble approach combines these models by training a Logistic Regression model meta-learner on base model predictions. In addition to increasing prediction accuracy, this tiered approach helps get around the drawbacks that come with using individual models. High accuracy, precision, and recall are shown in the model’s evaluation of a network intrusion dataset, indicating the model’s efficacy in identifying malicious activity. Cross-validation is used to make sure the models are reliable and well-generalized to new, untested data. In addition to advancing cybersecurity, the research establishes a foundation for the implementation of flexible and scalable intrusion detection systems. This hybrid, stacked ensemble model has a lot of potential for improving cyberattack prevention, lowering the likelihood of cyberattacks, and offering a scalable solution that can be adjusted to meet new threats and technological advancements.

Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

Cybersecurity threats are increasing rapidly as hackers use advanced techniques.As a result,cybersecurity has now a significant factor in protecting organizational limits.Intrusion detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a challenge.It remains an open contest over how to learn features in IDS since current approaches use deep learning methods.Hybrid learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber threats.In this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 dataset.To achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

Cybersecurity and Cyber Forensics: Machine Learning Approach Systematic Review

The proliferation of cloud computing and internet of things has led to the connectivity of states and nations(developed and developing countries)worldwide in which global network provide platform for the connection.Digital forensics is a field of computer security that uses software applications and standard guidelines which support the extraction of evidences from any computer appliances which is perfectly enough for the court of law to use and make a judgment based on the comprehensiveness,authenticity and objectivity of the information obtained.Cybersecurity is of major concerned to the internet users worldwide due to the recent form of attacks,threat,viruses,intrusion among others going on every day among internet of things.However,it is noted that cybersecurity is based on confidentiality,integrity and validity of data.The aim of this work is make a systematic review on the application of machine learning algorithms to cybersecurity and cyber forensics and pave away for further research directions on the application of deep learning,computational intelligence,soft computing to cybersecurity and cyber forensics.

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

Machine Learning Models for Heterogenous Network Security Anomaly Detection

The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptions. Conventional detection approaches face challenges in keeping up with the ever-changing strategies of cyber-attacks, resulting in heightened susceptibility and significant harm to network infrastructures. In order to tackle this urgent issue, this project focused on developing an effective anomaly detection system that utilizes Machine Learning technology. The suggested model utilizes contemporary machine learning algorithms and frameworks to autonomously detect deviations from typical network behaviour. It promptly identifies anomalous activities that may indicate security breaches or performance difficulties. The solution entails a multi-faceted approach encompassing data collection, preprocessing, feature engineering, model training, and evaluation. By utilizing machine learning methods, the model is trained on a wide range of datasets that include both regular and abnormal network traffic patterns. This training ensures that the model can adapt to numerous scenarios. The main priority is to ensure that the system is functional and efficient, with a particular emphasis on reducing false positives to avoid unwanted alerts. Additionally, efforts are directed on improving anomaly detection accuracy so that the model can consistently distinguish between potentially harmful and benign activity. This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.

Machine Learning Approach to Mobile Forensics Framework for Cyber Crime Detection in Nigeria

The mobile Cyber Crime detection is challenged by number of mobiledevices (internet of things), large and complex data, the size, the velocity,the nature and the complexity of the data and devices has become sohigh that data mining techniques are no more efficient since they cannothandle Big Data and internet of things. The aim of this research work wasto develop a mobile forensics framework for cybercrime detection usingmachine learning approach. It started when call was detected and thisdetection is made by machine learning algorithm furthermore intelligentmass media towers and satellite that was proposed in this work has theability to classified calls whether is a threat or not and send signal directlyto Nigerian communication commission (NCC) forensic lab for necessaryaction.

Imperatorin alleviates Aβ-induced spatial learning memory impairment and neuroinflam⁃mation in model mice of Alzheimer disease

OBJECTIVE To investigate the effects of imperatorin on the spatial learning memory impairment and neuroinflammation in model mice of Alzheimer disease(AD)induced by intracerebroventricular injection of Aβ1-42.METHODS Mouse model of AD was established by injection of Aβ1-42 into the lateral ventricles.Im⁃peratorin(2.5 and 5.0 mg·kg-1,daily)was inject⁃ed by intraperitoneally 1 h after intracerebroven⁃tricular injection for 13 d.The effect of imperato⁃rin on the spatial learning and memory impair⁃ment was assessed by eight arm maze tests.The levels of cytokines TNF-α,IL-1β,IL-6,IL-18 and chemokines MCP-1 in mouse cortex and hip⁃pocampus were detected by ELISA.The protein expression of NF-κB P65,TLR4,MyD88,p-P38,p-ERK,and p-JNK were detected by Western blotting.RESULTS As compared with the AD model group,imperatorin treatment significantly attenuated Aβ1-42-induced spatial learning and memory impairment assessed by eight arm maze tests.In addition,imperatorin significantly reduced the levels of cytokines TNF-α,IL-1β,IL-6,IL-18 and chemokines MCP-1 in the cerebral cortex and hippocampus.Meanwhile,Western blotting results showed that imperatorin treat⁃ment significantly down-regulated the protein expression of NF-κB P65,TLR4,MyD88,p-P38,p-ERK,and p-JNK.CONCLUSION Imperatorin has neuroprotective effects in the Aβ1-42 induced AD model mice and its mechanism may be partially associated with the inhibition of inflam⁃matory response in the cortex and hippocampus.

Adolescent Exposure of JWH-018 “Spice” Produces Subtle Effects on Learning and Memory Performance in Adulthood

The active components associated with the bio-designer drugs known variously as “Spice” or “K2” have rapidly gained in popularity among recreational users, forcing the United States Drug Enforcement Administration to classify these compounds as Schedule I drugs in the Spring of 2011. However, although there is some information about many of the synthetic cannabinoids used in Spice products, little is known about the consequences of the main constituent, (1-pentyl-3-(1-naphthoyl)indole;JWH-018), on neuropsychological development or behavior. In the present experiment, adolescent rats were given repeated injections of either saline or 100 μg/kg of JWH-018. Once the animals were 75 days of age, they were trained using tasks with spatial components of various levels of difficulty and a spatial learning set task. On early trials with water maze tasks of varying difficulty, the JWH-018 treated rats were impaired relative to controls. However, by the end of each phase of testing, drug and control animals were comparable, although on probe trials the drug-treated animals spent significantly less time in the target quadrant. In addition, the performance of the drug-treated rats was inferior to that of the control animals on a learning set task, suggesting some difficulty in adapting their responses to changing task demands. The results suggest that chronic exposure to this potent cannabinoid CB1 receptor agonist during adolescence is capable of producing a variety of subtle changes affecting spatial learning and memory performance in adulthood, well after the drug exposure period.

NP-14 Effects of Osthole on the Improvement of Learning and Memory Impairment in A Mouse Model Injected with Aβ25-35

Objective:We aimed to investigate the effects of osthole on learning and memory impairment of AD mice induced by injection of Aβ25-35 and the content of Ca2+、GLU、Ab1-42 in the brain tissue and peripheral blood.Methods:Mice were randomly assigned to sham operation,Aβ25-35,Aβ25-35+Ost-L,Aβ25-35+Ost-M,and Aβ25-35+Ost-H group.Water maze test was performed to assessing spatial learning ability of mice.It is determined that the MDA level and the activity of SOD in the brain tissue of mice in each group by colorimetry.The GLU kit and Ca2+kit were used to detect the GLU,Ca2+in tissue and serum.Elisa was used to detect the expression of Aβ1-42 in the hippocampus and serum of mice.HE staining and silver staining were used to detect neuron apoptosis and pathological changes in brain slices.Results:①Effects of osthole on learning and memory:With the increase of training day,the escape latencies continuously reduced in each experimental group,the escape latencies of the model group was longer on the 1st,2nd,3rd,and 5th days than the normal group,the difference was statistically significant(day 3,4:P<0.05,day 5:P<0.01);compared with the model group,the escaping latency on the fifth day of the OST low-medium high-dose group was significantly shortened,which was statistically significant(P<0.05).②Effects on oxidative stresspathway:the SOD activity of AD mice in the hippocampus model group was lower than that in the normal group,which was statistically significant(P<0.05);The SOD activity in the OST group was higher than that in the model group,which was statistically significant(P<0.05).The MDA content in the model group was significantly higher than that in the normal group(P<0.05).The MDA content in the OST high-dose group was lower than that in the model group,which was statistically significant(P<0.05).③Effects of GLU levels on neurotransmitters:the results of the detection of GLU in cortical area and GLU in serum of AD mice in OST dose groups showed that serum GLU levels in the model group were significantly lower than those in the sham group,which was statistically significant(P<0.05).GLU levels in the cortical area were also significantly higher than those in the sham group,which was statistically significant(P<0.05).Compared with the model group,GLU levels in the OST administration group were significantly downregulated.Among the serum,the effect of medium dose group was obvious.Although there was a trend of down-regulation in the cortical administration group,there was no statistical significance.④Changes in Ca2+concentration in the brain;Detection of intracellular Ca ion concentration in AD mice by OST doses showed that,compared with the sham group,the model group was significantly upregulated in cortical Ca2+levels.There was no statistical difference in the administration group.Compared with the model group,the concentration of Ca2+in the OST-H group significantly decreased.⑤Effect on levels of Ab1-42 in hippocampus and serum:model group had significantly higher Ab1-42 levels in hippocampus than in sham operation group,which was statistically significant(P<0.05).Ab1-42 in serum was also significantly upregulated compared to the sham group,which was statistically significant(P<0.05).Compared with the model group,the levels of Aβ1-42 in the OST administration group were significantly down-regulated,with the lower and middle doses in the hippocampus being more significant,while the serum was more pronounced at lower doses.⑥Silver staining to detect the tangles of hippocampal neurons:Neuron tangles in the hippocampal CA1 region showed a dark brown-yellow granule distribution in the nuclei of the model group(positive expression).Nerve cell body and dendrites,axons are black or black red,background light yellow.Compared with the model group,the administration group has improved significantly.Conclusion:OST improves spatial learning and memory of dementia model mice injected with Ab25-35 in both hippocampus.Experimental studies have shown that OST has different degrees of regulation on neuronal apoptosis,Ca2+/GLU/oxidative stress and other pathways,and it plays a role in improving multiple AD pathological changes and delaying the pathogenesis of neurodegenerative diseases.