High mobility group box 1 in the central nervous system:regeneration hidden beneath inflammation

High-mobility group box 1 was first discovered in the calf thymus as a DNA-binding nuclear protein and has been widely studied in diverse fields,including neurology and neuroscience.High-mobility group box 1 in the extracellular space functions as a pro-inflammatory damage-associated molecular pattern,which has been proven to play an important role in a wide variety of central nervous system disorders such as ischemic stroke,Alzheimer’s disease,frontotemporal dementia,Parkinson’s disease,multiple sclerosis,epilepsy,and traumatic brain injury.Several drugs that inhibit high-mobility group box 1 as a damage-associated molecular pattern,such as glycyrrhizin,ethyl pyruvate,and neutralizing anti-high-mobility group box 1 antibodies,are commonly used to target high-mobility group box 1 activity in central nervous system disorders.Although it is commonly known for its detrimental inflammatory effect,high-mobility group box 1 has also been shown to have beneficial pro-regenerative roles in central nervous system disorders.In this narrative review,we provide a brief summary of the history of high-mobility group box 1 research and its characterization as a damage-associated molecular pattern,its downstream receptors,and intracellular signaling pathways,how high-mobility group box 1 exerts the repair-favoring roles in general and in the central nervous system,and clues on how to differentiate the pro-regenerative from the pro-inflammatory role.Research targeting high-mobility group box 1 in the central nervous system may benefit from differentiating between the two functions rather than overall suppression of high-mobility group box 1.

出行即服务系统理论与实践:回顾与展望

交通是城市绿色低碳转型中最受关注的领域之一,也是数字化渗透及数字平台最为活跃的领域。出行即服务(Mobility as a Service, MaaS)系统是绿色交通的典型代表,是一种新型交通组织和供给方式,反映了当前出行需求的深刻变化和城市交通组织范式转变的耦合。全球范围内已出现了上百个大小规模不等和模式各异的MaaS实践创新,北京MaaS是中国持续至今、影响最大的MaaS实践。目前MaaS实践提出的理论和方法主要基于欧美发达国家,无法充分描述和分析中国实践。在文献研究的基础上,延伸纳入了中国经验,提出了具有全球普适性的一个MaaS系统分析框架,强调辨析全球范围内的MaaS异同均可以从三个维度展开,即嵌入的社会背景、发展目标和产生的社会经济环境影响;并应用此框架对国内外五个典型MaaS进行了比较研究,重点解码了北京MaaS的激励机制、商业模式和商业生态。本文旨在推动MaaS理论和研究方法的全球发展,重点提出了四个方面的关注:(1)MaaS系统的发展再次考验着城市交通如何回归其公共属性;(2)MaaS实践嵌入在城市社会背景中,具有明显的差异性。模式选择是对城市既有社会背景和交通格局的继承,但也可能就此发生转向。MaaS打开了一次城市交通转型的机会窗口;(3)MaaS系统的可持续运营依然面临挑战;(4)数字技术带来数据产权、数据隐私和安全等亟待解决的新问题。所有研究案例表明,数字技术的快速发展需要匹配治理模式创新,MaaS生态的协同进化至关重要。

2024 Wheelchair Compendium of Physical Activities:An update of activity codes and energy expenditure values

Purpose:This paper presents an update of the 2011 Wheelchair Compendium of Physical Activities designed for wheelchair users and is referred to as the 2024 Wheelchair Compendium.The Wheelchair Compendium aims to curate existing knowledge of the energy expenditure for wheelchair physical activities(PAs).Methods:A systematic review of the published energy expenditure of PA for wheelchair users was completed between 2011 and May 2023.We added these data to the 2011 Wheelchair Compendium data that was compiled previously in a systematic review through 2011.Results:A total of 47 studies were included,and 124 different wheelchair PA reported energy expenditure values ranging from 0.8 metabolic equivalents for wheelchair users(filing papers,light effort)to 11.8 metabolic equivalents for wheelchair users(Nordic sit skiing).Conclusion:In introducing the updated 2024 Wheelchair Compendium,we hope to bridge the resource gap and challenge the prevailing narratives that inadvertently exclude wheelchair users from physical fitness and health PAs.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

Robot-Oriented 6G Satellite-UAV Networks: Requirements, Paradigm Shifts, and Case Studies

Networked robots can perceive their surroundings, interact with each other or humans,and make decisions to accomplish specified tasks in remote/hazardous/complex environments. Satelliteunmanned aerial vehicle(UAV) networks can support such robots by providing on-demand communication services. However, under traditional open-loop communication paradigm, the network resources are usually divided into user-wise mostly-independent links,via ignoring the task-level dependency of robot collaboration. Thus, it is imperative to develop a new communication paradigm, taking into account the highlevel content and values behind, to facilitate multirobot operation. Inspired by Wiener’s Cybernetics theory, this article explores a closed-loop communication paradigm for the robot-oriented satellite-UAV network. This paradigm turns to handle group-wise structured links, so as to allocate resources in a taskoriented manner. It could also exploit the mobility of robots to liberate the network from full coverage,enabling new orchestration between network serving and positive mobility control of robots. Moreover,the integration of sensing, communications, computing and control would enlarge the benefit of this new paradigm. We present a case study for joint mobile edge computing(MEC) offloading and mobility control of robots, and finally outline potential challenges and open issues.

Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Cardiac rehabilitation after cardiac surgery:An important underutilized treatment strategy

Physical inactivity remains in high levels after cardiac surgery,reaching up to 50%.Patients present a significant loss of functional capacity,with prominent muscle weakness after cardiac surgery due to anesthesia,surgical incision,duration of cardiopulmonary bypass,and mechanical ventilation that affects their quality of life.These complications,along with pulmonary complications after surgery,lead to extended intensive care unit(ICU)and hospital length of stay and significant mortality rates.Despite the well-known beneficial effects of cardiac rehabilitation,this treatment strategy still remains broadly underutilized in patients after cardiac surgery.Prehabilitation and ICU early mobilization have been both showed to be valid methods to improve exercise tolerance and muscle strength.Early mobilization should be adjusted to each patient’s functional capacity with progressive exercise training,from passive mobilization to more active range of motion and resistance exercises.Cardiopulmonary exercise testing remains the gold standard for exercise capacity assessment and optimal prescription of aerobic exercise intensity.During the last decade,recent advances in healthcare technology have changed cardiac rehabilitation perspectives,leading to the future of cardiac rehabilitation.By incorporating artificial intelligence,simulation,telemedicine and virtual cardiac rehabilitation,cardiac surgery patients may improve adherence and compliance,targeting to reduced hospital readmissions and decreased healthcare costs.

UAV-assisted cooperative offloading energy efficiency system for mobile edge computing

Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

MiR-142-3p Regulates ILC1s by Targeting HMGB1 via the NF-κB Pathway in a Mouse Model of Early Pregnancy Loss

Objective Innate lymphoid cells(ILCs)are a class of newly discovered immunocytes.Group 1 ILCs(ILC1s)are identified in the decidua of humans and mice.High mobility group box 1(HMGB1)is predicted to be one of the target genes of miR-142-3p,which is closely related to pregnancy-related diseases.Furthermore,miR-142-3p and HMGB1 are involved in regulating the NF-κB signaling pathway.This study aimed to examine the regulatory effect of miR-142-3p on ILC1s and the underlying mechanism involving HMGB1 and the NF-κB signaling pathway.Methods Mouse models of normal pregnancy and abortion were constructed,and the alterations of ILC1s,miR-142-3p,ILC1 transcription factor(T-bet),and pro-inflammatory cytokines of ILC1s(TNF-α,IFN-γand IL-2)were detected in mice from different groups.The targeting regulation of HMGB1 by miR-142-3p in ILC1s,and the expression of HMGB1 in normal pregnant mice and abortive mice were investigated.In addition,the regulatory effects of miR-142-3p and HMGB1 on ILC1s were detected in vitro by CCK-8,Annexin-V/PI,ELISA,and RT-PCR,respectively.Furthermore,changes of the NF-κB signaling pathway in ILC1s were examined in the different groups.For the in vivo studies,miR-142-3p-Agomir was injected in the uterus of abortive mice to evaluate the abortion rate and alterations of ILC1s at the maternal-fetal interface,and further detect the expression of HMGB1,pro-inflammatory cytokines,and the NF-κB signaling pathway.Results The number of ILC1s was significantly increased,the level of HMGB1 was significantly upregulated,and that of miR-142-3p was considerably downregulated in the abortive mice as compared with the normal pregnant mice(all P<0.05).In addition,miR-142-3p was found to drastically inhibit the activation of the NF-κB signaling pathway(P<0.05).The number of ILC1s and the levels of pro-inflammatory cytokines were significantly downregulated and the activation of the NF-κB signaling pathway was inhibited in the miR-142-3p Agomir group(all P<0.05).Conclusion miR-142-3p can regulate ILC1s by targeting HMGB1 via the NF-κB signaling pathway,and attenuate the inflammation at the maternal-fetal interface in abortive mice.

Bone marrow-derived mesenchymal stem cell-derived exosomeloaded miR-129-5p targets high-mobility group box 1 attenuates neurological-impairment after diabetic cerebral hemorrhage

BACKGROUND Diabetic intracerebral hemorrhage(ICH)is a serious complication of diabetes.The role and mechanism of bone marrow mesenchymal stem cell(BMSC)-derived exosomes(BMSC-exo)in neuroinflammation post-ICH in patients with diabetes are unknown.In this study,we investigated the regulation of BMSC-exo on hyperglycemia-induced neuroinflammation.AIM To study the mechanism of BMSC-exo on nerve function damage after diabetes complicated with cerebral hemorrhage.METHODS BMSC-exo were isolated from mouse BMSC media.This was followed by transfection with microRNA-129-5p(miR-129-5p).BMSC-exo or miR-129-5poverexpressing BMSC-exo were intravitreally injected into a diabetes mouse model with ICH for in vivo analyses and were cocultured with high glucoseaffected BV2 cells for in vitro analyses.The dual luciferase test and RNA immunoprecipitation test verified the targeted binding relationship between miR-129-5p and high-mobility group box 1(HMGB1).Quantitative polymerase chain reaction,western blotting,and enzyme-linked immunosorbent assay were conducted to assess the levels of some inflammation factors,such as HMGB1,interleukin 6,interleukin 1β,toll-like receptor 4,and tumor necrosis factorα.Brain water content,neural function deficit score,and Evans blue were used to measure the neural function of mice.RESULTS Our findings indicated that BMSC-exo can promote neuroinflammation and functional recovery.MicroRNA chip analysis of BMSC-exo identified miR-129-5p as the specific microRNA with a protective role in neuroinflammation.Overexpression of miR-129-5p in BMSC-exo reduced the inflammatory response and neurological impairment in comorbid diabetes and ICH cases.Furthermore,we found that miR-129-5p had a targeted binding relationship with HMGB1 mRNA.CONCLUSION We demonstrated that BMSC-exo can reduce the inflammatory response after ICH with diabetes,thereby improving the neurological function of the brain.

Time Parameter Based Low-Energy Data Encryption Method for Mobile Applications

Various mobile devices and applications are now used in daily life.These devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G mobile networks.High-security cryptography guarantees that essential data can be transmitted securely;however,it increases energy consumption and reduces data processing speed.Therefore,this study proposes a low-energy data encryption(LEDE)algorithm based on the Advanced Encryption Standard(AES)for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things(IoT)devices.In the proposed LEDE algorithm,the system time parameter is employed to create a dynamic S-Box to replace the static S-Box of AES.Tests indicated that six-round LEDE encryption achieves the same security level as 10-round conventional AES encryption.This reduction in encryption time results in the LEDE algorithm having a 67.4%lower energy consumption and 43.9%shorter encryption time than conventional AES;thus,the proposed LEDE algorithm can improve the performance and the energy consumption of IoT edge devices.

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Riding towards a sustainable future:an evaluation of bike sharing’s environmental benefits in Xiamen Island,China

In the pursuit of sustainable urbanization,Bike-Sharing Services(BSS)emerge as a pivotal instrument for promoting green,low-carbon transit.While BSS is often commended for its environmental benefits,we offer a more nuanced analysis that elucidates previously neglected aspects.Through the Dominant Travel Distance Model(DTDM),we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance.Utilizing multiscale geographically weighted regression(MGWR),we illuminate the relationship between BSS’s environmental benefits and built-environment attributes.The life cycle analysis(LCA)quantifies greenhouse gas(GHG)emissions from production to operation,providing a deeper understanding of BSS’s environmental benefits.Notably,our study focuses on Xiamen Island,a Chinese“Type Ⅱ large-sized city”(1–3 million population),contrasting with the predominantly studied“super large-sized cities”(over 10 million population).Our findings highlight:(1)A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO_(2)-eq,accumulating monthly savings of 144.477 t CO_(2)-eq.(2)Areas in the southwest,northeast,and southeast of Xiamen Island,characterized by high population densities,register significant BSS environmental benefits.(3)At a global level,the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation.(4)Regionally,MGWR enhances model precision,indicating that these five factors function at diverse spatial scales,affecting BSS’s environmental benefits variably.

Modeling the performance of perovskite solar cells with inserting porous insulating alumina nanoplates

Peng et al.[Science 379683(2023)]reported an effective method to improve the performance of perovskite solar cells by using thicker porous insulator contact(PIC)-alumina nanoplates.This method overcomes the trade-off between the open-circuit voltage and the fill factor through two mechanisms:reduced surface recombination velocity and increased bulk recombination lifetime due to better perovskite crystallinity.From arguments of drift-diffusion simulations,we find that an increase in mobility and carrier recombination lifetime in bulk are the key factors for minimizing the resistance-effect from thicker PICs and achieving a maximum power conversion efficiency(PCE)at approximately 25%reduced contact area.Furthermore,the partially replacement of perovskite films with thicker PICs would result in a reduction in short-current density,but the relative low refractive index of the PICs imbedded into the high refractive index perovskite creates light trapping structures that compensate for this loss.

Dynamic Characteristics of the Long Runout Rock-ice Avalanche at High Altitude——A Case from the Zelongnong Basin,Eastern Himalayan Syntaxis,China

Rock-ice avalanches have frequently occurred in the Eastern Himalayan Syntaxis region due to climate change and active tectonic movements.These events commonly trigger catastrophic geohazard chains,including debris flows,river blockages,and floods.This study focuses on the Zelongnong Basin,analyzing the geomorphic and dynamic characteristics of high-altitude disasters.The basin exhibits typical vertical zonation,with disaster sources initiating at elevations exceeding 4000 m and runout distances reaching up to 10 km.The disaster chain movement involves complex dynamic effects,including impact disintegration,soil-rock mixture arching,dynamic erosion,and debris deposition,enhancing understanding of the flow behavior and dynamic characteristics of rock-ice avalanches.The presence of ice significantly increases mobility due to lubrication and frictional melting.In the disaster event of September 10,2020,the maximum flow velocity and thickness reached 40 m/s and 43 m,respectively.Furthermore,continuous deformation of the Zelongnong glacier moraine was observed,with maximum cumulative deformations of 44.68 m in the distance direction and 25.96 m in the azimuth direction from March 25,2022,to August 25,2022.In the future,the risk of rock-ice avalanches in the Eastern Himalayan Syntaxis region will remain extremely high,necessitating a focus on early warning and risk mitigation strategies for such basin disasters.

Mechanism of high Li-ion conductivity in poly(vinylene carbonate)-poly(ethylene oxide)cross-linked network based electrolyte revealed by solid-state NMR

Solid polymer electrolytes(SPEs)have become increasingly important in advanced lithium-ion batteries(LIBs)due to their improved safety and mechanical properties compared to organic liquid electrolytes.Cross-linked polymers have the potential to further improve the mechanical property without trading off Li-ion conductivity.In this study,focusing on a recently developed cross-linked SPE,i.e.,the one based on poly(vinylene carbonate)-poly(ethylene oxide)cross-linked network(PVCN),we used solid-state nuclear magnetic resonance(NMR)techniques to investigate the fundamental interaction between the chain segments and Li ions,as well as the lithium-ion motion.By utilizing homonuclear/heteronuclear correlation,CP(cross-polarization)kinetics,and spin-lattice relaxation experiments,etc.,we revealed the structural characteristics and their relations to lithium-ion mobilities.It is found that the network formation prevents poly(ethylene oxide)chains from crystallization,which could create sufficient space for segmental tumbling and Li-ion co nductio n.As such,the mechanical property is greatly improved with even higher Li-ion mobilities compared to the poly(vinylene carbonate)or poly(ethylene oxide)based SPE analogues.