Optimizing Bearing Fault Detection:CNN-LSTM with Attentive TabNet for Electric Motor Systems

Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing faults.Manual fault diagnosis poses safety risks and economic instability,necessitating an automated approach.This study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular Learning.The model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label encoding.Its architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss functions.Fine-tuning techniques prevent over-fitting.Evaluations were conducted on 10 fault classes from the CWRU dataset.FTCNNLSTM was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 instances.The FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other methods.This establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

The Analogy between the Immune System and Human Life

The immune system operates as a complex organization with distinct roles and functions. Excitingly we recognized the similarities between the cellular dynamics of the immune system and our lives, activities, and behaviors. Observing the immune system can guide how to respond to various daily situations, including when to react, tolerate, or ignore. Recognizing this analogy between our lives and the immune system should motivate us to adopt a wisdom-based approach when investigating the mechanisms and future discoveries related to this system and to deepen our understanding of this complex system with newfound respect. In this context, the present review examines several integral biological processes of the immune system by drawing parallels between them and human life, activities, and behaviors to learn how we must behave based on the insights offered by this complex organization. The literature search was conducted in international databases such as PubMed/MEDLINE and Google Scholar search engine using English equivalent keywords from 1998 up to April 2023. The search strategy used the following subject heading terms: Immune system, analogy, human life, cellular dynamics, memory, tolerance, and ignorance. In conclusion, the immune system is a complex organization comprising various cells interacting within specific sites and networks, communicating, drawing experiences, and learning how to tolerate certain conditions that make it share certain similarities with human life.

Hybrid scientific article recommendation system with COOT optimization

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.

A Novel MegaBAT Optimized Intelligent Intrusion Detection System in Wireless Sensor Networks

Wireless Sensor Network(WSN),whichfinds as one of the major components of modern electronic and wireless systems.A WSN consists of numerous sensor nodes for the discovery of sensor networks to leverage features like data sensing,data processing,and communication.In thefield of medical health care,these network plays a very vital role in transmitting highly sensitive data from different geographic regions and collecting this information by the respective network.But the fear of different attacks on health care data typically increases day by day.In a very short period,these attacks may cause adversarial effects to the WSN nodes.Furthermore,the existing Intrusion Detection System(IDS)suffers from the drawbacks of limited resources,low detection rate,and high computational overhead and also increases the false alarm rates in detecting the different attacks.Given the above-mentioned problems,this paper proposes the novel MegaBAT optimized Long Short Term Memory(MBOLT)-IDS for WSNs for the effective detection of different attacks.In the proposed framework,hyperpara-meters of deep Long Short-Term Memory(LSTM)were optimized by the meta-heuristic megabat algorithm to obtain a low computational overhead and high performance.The experimentations have been carried out using(Wireless Sensor NetworkDetection System)WSN-DS datasets and performance metrics such as accuracy,recall,precision,specificity,and F1-score are calculated and compared with the other existing intelligent IDS.The proposed framework provides outstanding results in detecting the black hole,gray hole,scheduling,flooding attacks and significantly reduces the time complexity,which makes this system suitable for resource-constraint WSNs.

A Shape-Memory Deployable Subsystem with a Large Folding Ratio in China’s Tianwen-1 Mars Exploration Mission

Once China’s Tianwen-1 Mars probe arrived in a Mars orbit after a seven-month flight in the deep cold space environment,it would be urgently necessary to monitor its state and the surrounding environment.To address this issue,we developed a flexible deployable subsystem based on shape memory polymer composites(SMPC-FDS)with a large folding ratio,which incorporates a camera and two temperature telemetry points for monitoring the local state of the Mars orbiter and the deep space environment.Here,we report on the development,testing,and successful application of the SMPC-FDS.Before reaching its Mars remote-sensing orbit,the SMPC-FDS is designed to be in a folded state with high stiffness;after reaching orbit,it is in a deployed state with a large envelope.The transition from the folded state to the deployed state is achieved by electrically heating the shape memory polymer composites(SMPCs);during this process,the camera on the SMPC-FDS can capture the local state of the orbiter from multiple angles.Moreover,temperature telemetry points on the SMPC-FDS provide feedback on the environment temperature and the temperature change of the SMPCs during the energization process.By simulating a Mars on-orbit space environment,the engineering reliability of the SMPC-FDS was comprehensively verified in terms of the material properties,structural dynamic performance,and thermal vacuum deployment feasibility.Since the launch of Tianwen-1 on 23 July 2020,scientific data on the temperature environment around Tianwen-1 has been successfully acquired from the telemetry points on the SMPCFDS,and the local state of the orbiter has been photographed in orbit,showing the national flag of China fixed on the orbiter.

A Scalable Interconnection Scheme in Many-Core Systems

Recent architectures of multi-core systems may have a relatively large number of cores that typically ranges from tens to hundreds;therefore called many-core systems.Such systems require an efficient interconnection network that tries to address two major problems.First,the overhead of power and area cost and its effect on scalability.Second,high access latency is caused by multiple cores’simultaneous accesses of the same shared module.This paper presents an interconnection scheme called N-conjugate Shuffle Clusters(NCSC)based on multi-core multicluster architecture to reduce the overhead of the just mentioned problems.NCSC eliminated the need for router devices and their complexity and hence reduced the power and area costs.It also resigned and distributed the shared caches across the interconnection network to increase the ability for simultaneous access and hence reduce the access latency.For intra-cluster communication,Multi-port Content Addressable Memory(MPCAM)is used.The experimental results using four clusters and four cores each indicated that the average access latency for a write process is 1.14785±0.04532 ns which is nearly equal to the latency of a write operation in MPCAM.Moreover,it was demonstrated that the average read latency within a cluster is 1.26226±0.090591 ns and around 1.92738±0.139588 ns for read access between cores from different clusters.

Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification

System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

Aquaculture has long been a critical economic sector in Taiwan.Since a key factor in aquaculture production efficiency is water quality,an effective means of monitoring the dissolved oxygen content(DOC)of aquaculture water is essential.This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality.Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality.Since aquaculture water quality depends on a continuous interaction among multiple factors,and the current state is correlated with the previous state,a model with time series is required.Therefore,this study used recurrent neural networks(RNNs)with sequential characteristics.Commonly used RNNs such as long short-term memory model and gated recurrent unit(GRU)model have a memory function that appropriately retains previous results for use in processing current results.To construct a suitable RNN model,this study used Taguchi method to optimize hyperparameters(including hidden layer neuron count,iteration count,batch size,learning rate,and dropout ratio).Additionally,optimization performance was also compared between 5-layer and 7-layer network architectures.The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study.The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%,root mean square error of 0.0638,and R-value of 0.9984.Therefore,thewater qualitymanagement system developed in this study can quickly provide practitioners with highly accurate data,which is essential for a timely response to water quality issues.This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company.Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value.

Anticonvulsant Effects of Chrysanthellum americanum L. (Vatke) Aqueous Extract in Mice Pilocarpine Model of Epilepsy and Associated Memory Impairment: Role of Antioxidant Defense System and Cholinergic Transmission

Chrysanthellum americanum (L.) Vatke is a medicinal plant used by the traditional healers to treat epilepsy and associated memory impairment. This work aims at evaluating the anticonvulsant effects of Chrysanthellum americanum aqueous extract in mice pilocarpine model of epilepsy and associated memory loss. Mice were administered orally Chrysanthellum americanum aqueous extract (27.69, 69.22, 138.45, 276.9 mg/kg, prepared from the whole part) for test groups, intraperitoneally 300 mg/kg sodium valproate for the positive control group or orally 10 mL/kg distilled water for the negative control group, respectively, during a period of seven consecutive days. On the first day, temporal lobe epilepsy was induced by intraperitoneal injection of 360 mg/kg pilocarpine one hour after the administration of different treatment to mice, and the occurrence of status epilepticus was evaluated. On the second day, the anticonvulsant property was measured after the intraperitoneal injection of a sub-convulsive dose of picrotoxin (1 mg/kg). On the seventh day, the anti-amnesic properties of the extract were evaluated in the epileptic mice using the T-maze and open field paradigms. The results show that Chrysanthellum americanum extract significantly (p Chrysanthellum americanum (276.9 mg/kg) likewise sodium valproate (300 mg/kg) significantly (p Chrysanthellum americanum aqueous extract has anticonvulsant effects against pilocarpine induced-epileptic seizures and memory impairment. These properties could be mediated by the amelioration of antioxidant defense system and cholinergic neurotransmission in epileptic mice, which could partly justify the use of Chrysanthellum americanum in the traditional medicine for the treatment of epilepsy.

RCache: A Read-Intensive Workload-Aware Page Cache for NVM Filesystem

Byte-addressable non-volatile memory(NVM),as a new participant in the storage hierarchy,gives extremely high performance in storage,which forces changes to be made on current filesystem designs.Page cache,once a significant mechanism filling the performance gap between Dynamic Random Access Memory(DRAM)and block devices,is now a liability that heavily hinders the writing performance of NVM filesystems.Therefore state-of-the-art NVM filesystems leverage the direct access(DAX)technology to bypass the page cache entirely.However,the DRAM still provides higher bandwidth than NVM,which prevents skewed read workloads from benefiting from a higher bandwidth of the DRAM and leads to sub-optimal performance for the system.In this paper,we propose RCache,a readintensive workload-aware page cache for NVM filesystems.Different from traditional caching mechanisms where all reads go through DRAM,RCache uses a tiered page cache design,including assigning DRAM and NVM to hot and cold data separately,and reading data from both sides.To avoid copying data to DRAM in a critical path,RCache migrates data from NVM to DRAM in a background thread.Additionally,RCache manages data in DRAM in a lock-free manner for better latency and scalability.Evaluations on Intel Optane Data Center(DC)Persistent Memory Modules show that,compared with NOVA,RCache achieves 3 times higher bandwidth for read-intensive workloads and introduces little performance loss for write operations.

A Brief Review of the Relationship between Addiction and Memory Systems

This essay will reexamine research on the relationship between human memory and addiction. This paper will review several studies that discussed how memory systems in the human brain are involved in the acquisition of behavior that is learned and is associated with the development of drug addiction and drug relapse. Additional information reveals that when individuals make the transition from recreational drug or impulsive use to compulsive drug abuse, which may result in a neuroanatomical change in areas of the brain from cognitive control guided by the hippocampus/dorsomedial striatum towards conditioned control of behavior managed by the dorsolateral striatum (DLS) [1]. This review also looked at studies that involved experiments with humans and lower animals, which suggested that the hippocampus mediates a cognitive/spatial type of memory, while the dorsal striatum manages stimulus-response (S-R) habit memory, and the amygdala governs the classical conditioning form of learning and stimulus-affective-associative relationships [1]. Overall, these studies utilize the hypothesis of the memory systems view of addiction, and the involvement of learning and memory in the context of drug addiction, which was proposed by them [2]. This theory has been proposed in response to drug addiction research and includes alcohol, amphetamine, and cocaine [1]. The research also explains how stress and anxiety can play a role in how strong emotional excitement can lead to dependent habit memory in rodents and humans [1]. .

1960—2020年安阳市气候生产潜力变化与未来趋势

基于安阳市气象站点资料,运用Thornthwaite Memorial模型、Morlet小波分析以及Mann-Kendall检验等方法针对安阳市气候生产潜力时空演变特征进行分析。研究结果表明,近61年安阳市气温以0.19℃/10 a的倾向率呈增加趋势,年降水量以-4.64 mm/10 a的速率呈减少趋势,近61年安阳市气候生产潜力年平均为1037.95 g/(m^(2)·a),并以5.04 g/(m^(2)·a)/10 a的倾向率呈上升趋势。Morlet小波分析表明,近61年安阳市气候生产潜力存在28年左右的主周期变化;1978—2020年安阳市粮食单产显著提高,气候资源利用率波动增加,21世纪10年代平均气候资源利用率已达59.40%。安阳市气候生产潜力对降水变化更敏感,气候越暖湿,越有利于气候生产潜力的提高。R/S分析表明未来安阳市气候生产潜力将呈下降趋势。

利用长短期记忆网络LSTM对赤道太平洋海表面温度短期预报

海表面温度作为海洋中一个最重要的变量,对全球气候、海洋生态等有很大的影响,因此十分有必要对海表面温度(SST)进行预报。深度学习具备高效的数据处理能力,但目前利用深度学习对整个赤道太平洋的SST短期预报及预报技巧的研究仍较少。本文基于最优插值海表面温度(OISST)的日平均SST数据,利用长短期记忆(LSTM)网络构建了未来10天赤道太平洋(10°S~10°N,120°E~80°W)SST的逐日预报模型。LSTM预报模型利用1982~2010年的观测数据进行训练,2011~2020年的观测数据作为初值进行预报和检验评估。结果表明:赤道太平洋东部地区预报均方根误差(RMSE)大于中、西部,东部预报第1天RMSE为0.6℃左右,而中、西部均小于0.3℃。在不同的年际变化位相,预报RMSE在拉尼娜出现时期最大,正常年份次之,厄尔尼诺时期最小,RMSE在拉尼娜时期比在厄尔尼诺时期可达20%。预报偏差整体表现为东正、西负。相关预报技巧上,中部最好,可预报天数基本为10天以上,赤道冷舌附近可预报天数为4~7天,赤道西边部分地区可预报天数为3天。预报模型在赤道太平洋东部地区各月份预报技巧普遍低于西部地区,相比较而言各区域10、11月份预报技巧最低。总的来说,基于LSTM构建的SST预报模型能很好地捕捉到SST在时序上的演变特征,在不同案例中预报表现良好。同时该预报模型依靠数据驱动,能迅速且较好地预报未来10天以内的日平均SST的短期变化。

Astrocytic endothelin-1 overexpression impairs learning and memory ability in ischemic stroke via altered hippocampal neurogenesis and lipid metabolism

Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.

基于Thornthwaite Memorial模型的近54年河南省农业气候生产力时空变化特征分析

利用河南省1961—2014年近54年的19个气象站逐月逐日气象资料,基于Arc GIS 10.1软件平台,采用气候倾向率、反距离加权插值(简称IDW)、Mann-Kendall检验等方法对降水和气温的时空变化进行分析;同时运用Thornthwaite Memorial模型对农业气候生产力时空特征进行研究,利用SPSS软件分析农业气候生产力对降水量和气温的敏感性。结果表明:(1)河南省近54年的降水量呈波动减少态势,减少幅度为8.92 mm/10年;河南省大部分地区的降水倾向率是负值,主要分布在豫东、豫南和豫北地区;(2)河南省近54年的气温整体呈上升趋势,上升速率为0.157℃/10年,纬度较低的南部地区气温较高,气温较低的地区主要在西部山区,且气温倾向率均为正值;(3)河南省近54年的农业气候生产力总体上呈微弱的上升趋势,农业气候生产力距平变化明显,正距平年份比负距平年份多,呈现由南向北递减的特征,南部农业气候生产力较大,西部的农业气候生产力较小,但大部分地区的农业气候生产力呈下降趋势,豫东和豫南地区农业气候生产力上升速率较快,豫西和豫北地区的则下降;(4)河南省农业气候生产力与降水量相关性显著,但与气温的相关性不显著,气候生产力受降水和气温的双重影响,降水量是影响农业气候生产力的主导因素,气候变暖对气候生产力的提高是有利的。随着降水量的逐渐减小,气温的逐渐增加,对农业气候生产力的影响也在进一步加大,要合理利用水资源,兴修水利来保障农业用水,加强稳固河南省在全国的农业地位。

SIRT2 as a potential new therapeutic target for Alzheimer's disease

Alzheimer's disease is the most common cause of dementia globally with an increasing incidence over the years,bringing a heavy burden to individuals and society due to the lack of an effective treatment.In this context,sirtuin 2,the sirtuin with the highest expression in the brain,has emerged as a potential therapeutic target for neurodegenerative diseases.This review summarizes and discusses the complex roles of sirtuin 2 in different molecular mechanisms involved in Alzheimer's disease such as amyloid and tau pathology,microtubule stability,neuroinflammation,myelin formation,autophagy,and oxidative stress.The role of sirtuin 2 in all these processes highlights its potential implication in the etiology and development of Alzheimer's disease.However,its presence in different cell types and its enormous variety of substrates leads to apparently contra dictory conclusions when it comes to understanding its specific functions.Further studies in sirtuin 2 research with selective sirtuin2 modulators targeting specific sirtuin 2 substrates are necessary to clarify its specific functions under different conditions and to validate it as a novel pharmacological target.This will contribute to the development of new treatment strategies,not only for Alzheimer's disease but also for other neurodegenerative diseases.

Treadmill exercise improves hippocampal neural plasticity and relieves cognitive deficits in a mouse model of epilepsy

Epilepsy frequently leads to cognitive dysfunction and approaches to treatment remain limited.Although regular exercise effectively improves learning and memory functions across multiple neurological diseases,its application in patients with epilepsy remains controversial.Here,we adopted a 14-day treadmill-exercise paradigm in a pilocarpine injection-induced mouse model of epilepsy.Cognitive assays confirmed the improvement of object and spatial memory after endurance training,and electrophysiological studies revealed the maintenance of hippocampal plasticity as a result of physical exercise.Investigations of the mechanisms underlying this effect revealed that exercise protected parvalbumin interneurons,probably via the suppression of neuroinflammation and improved integrity of blood-brain barrier.In summary,this work identified a previously unknown mechanism through which exercise improves cognitive rehabilitation in epilepsy.