Superior and safer lithium sulfur batteries realized by robust polysulfides-retarding dam with high flame retardance

The unparalleled energy density has granted lithium-sulfur batteries(LSBs)with attractive usages.Unfortunately,LSBs still face some unsurpassed challenges in industrialization,with polysulfides shuttling,dendrite growth and thermal hazard as the major problems triggering the cycling instability and low safety.With the merit of convenience,the method of designing functional separator has been adapted.Concretely,the carbon aerogel confined with CoS_(2)(CoS_(2)-NCA)is constructed and coated on Celgard separator surface,acquiring CoS_(2)-NCA modified separator(CoS_(2)-NCA@C),which holds the promoted electrolyte affinity and flame retardance.As revealed,CoS_(2)-NCA@C cell gives a high discharge capacity 1536.9 mAh/g at 1st cycle,much higher than that of Celgard cell(987.1 mAh/g).Moreover,the thermal runaway triggering time is dramatically prolonged by 777.4 min,corroborating the promoted thermal safety of cell.Noticeably,the higher coulombic efficiency stability and lower overpotential jointly confirm the efficacy of CoS_(2)-NCA@C in suppressing the lithium dendrite growth.Overall,this work can provide useful inspirations for designing functional separator,coping with the vexing issues of LSBs.

Persistent Variations in the East Asian Trough from March to April and the Possible Mechanism

The East Asian trough(EAT)profoundly influences the East Asian spring climate.In this study,the relationship of the EATs among the three spring months is investigated.Correlation analysis shows that the variation in March EAT is closely related to that of April EAT.Extended empirical orthogonal function(EEOF)analysis also confirms the co-variation of the March and April EATs.The positive/negative EEOF1 features the persistent strengthened/weakened EAT from March to April.Further investigation indicates that the variations in EEOF1 are related to a dipole sea surface temperature(SST)pattern over the North Atlantic and the SST anomaly over the tropical Indian Ocean.The dipole SST pattern over the North Atlantic,with one center east of Newfoundland Island and another east of Bermuda,could trigger a Rossby wave train to influence the EAT in March−April.The SST anomaly over the tropical Indian Ocean can change the Walker circulation and influence the atmospheric circulation over the tropical western Pacific,subsequently impacting the southern part of the EAT in March−April.Besides the SST factors,the Northeast Asian snow cover could change the regional thermal conditions and lead to persistent EAT anomalies from March to April.These three impact factors are generally independent of each other,jointly explaining large variations in the EAT EEOF1.Moreover,the signals of the three factors could be traced back to February,consequently providing a potential prediction source for the EAT variation in March and April.

同伴支持教育对维持性血液透析患者自我管理行为的影响

目的探讨同伴支持教育对维持性血液透析患者自我管理行为的影响。方法选择本透析中心79例血液透析患者,按照随机数表法分为对照组和干预组,对照组给予常规健康教育,干预组在对照组的基础上接受同伴支持教育,采用血液透析相关知识问卷和血液透析患者自我管理量表测量两组患者干预前、干预后6月血液透析相关知识得分和自我管理行为得分,观察两组患者透析相关指标情况。结果干预6月后干预组患者血液透析相关知识得分(22.04±2.89)显著高于对照组(17.55±3.18)(P<0.001);自我管理行为得分(71.23±3.16)显著高于对照组(59.94±3.24)(P<0.001);透析相关指标包括收缩压(135.65±7.81)、血磷值(1.81±0.57)、内瘘失功率(0%)、超滤量超过自身体重5%的发生率(8.2%)低于对照组(140.52±7.17、2.01±0.52、5.12%、12.3%)(P<0.001);血红蛋白(103.10±12.52)和血清白蛋白(40.45±2.18)与对照组(102.24±13.21、40.32±2.48)相比差异无统计学意义(P>0.05)。结论同伴支持教育能改善血液透析患者的自我管理行为,提高透析相关生活质量。

维持性血液透析病人体力活动水平及其影响因素

目的:了解维持性血液透析病人体力活动水平,探讨其体力活动的影响因素。方法:选择区域内3所三级医院的透析中心,采用分层抽样选取218例病人进行研究,运用国际体力活动问卷(IPAQ)对血液透析病人体力活动水平进行调查,使用一般情况量表、Piper疲乏修订量表、社会支持评定量表、华西心晴指数量表对病人进行调查,分析血红蛋白、磷、甲状旁腺激素水平对体力活动水平的影响。结果:体力活动能量总能耗中位数为1 680.5(531.56,3 543.79)MET-min/week,将单因素分析有统计学意义的指标在职情况、血红蛋白是否达标、疲乏程度、心晴指数水平纳入Logistic回归分析。不在职工作[OR=8.32,95%CI(1.77,38.94),P<0.05]、疲乏得分[OR=1.79,95%CI(1.11,2.89),P<0.05]、血红蛋白水平不达标[OR=2.43,95%CI(1.36,4.35),P<0.05]为影响维持性血液透析病人体力活动的影响因素。结论:MHD病人体力活动水平仍偏低,血红蛋白水平、疲乏和在职情况为MHD体力活动水平的影响因素,因此,医护人员需加强不在职病人评估和指导,加强对病人疲乏症状的评估和干预。

透析中卧位脚踏车运动对血液透析病人运动能力的影响

目的:观察24周的卧位脚踏车运动干预对维持性血液透析(MHD)病人运动能力水平和安全性的影响。方法:选择2020年1月—2020年6月乐山市某三级医院的73例MHD病人,随机分为两组,对照组(36例)给予常规血液透析护理和健康指导,试验组37例在对照组基础上予以透析中卧位脚踏车运动,每周2次或3次。比较两组病人基线、干预后12周、干预后24周运动能力(6min步行距离、30s坐立数、握力)及透析中不良事件发生率。结果:试验组在干预12周后,6min步行距离、握力高于对照组(P<0.05),干预24周后,6min步行距离、握力、30s起坐数高于对照组(P<0.05),重复测量方差分析结果显示,不同时间点6min步行距离、握力、30s起坐数比较,差异有统计学意义(P<0.05),6min步行距离和30s起坐时间和处理因素交互效应差异有统计学意义(P<0.05),两组病人透析中不良事件发生率比较差异无统计学意义(P>0.05)。结论:透析中卧位脚踏车运动可以提高血液透析病人运动能力,且安全性较高。

不同岩性组合条件下顶板采动效应研究

针对工作面回采后采动顶板稳定性的问题,通过FLAC3D数值模拟软件建立8种不同岩性组合条件下顶板采动效应模型,研究不同组合条件下采动顶板的应力和位移分布变化规律。研究结果表明:顶板全为硬岩时顶板垂直集中应力最大,全为软岩时最小;顶板的剪应力集中区主要位于上覆岩层的硬岩中,剪应力最大组合顶板类型为软-硬-软-硬组合;在不同岩性组合顶板条件下,顶板为软岩时其变形量远大于顶板为硬岩及软硬岩结合时的变形量;相同顶板条件下随着距离的加大,顶板垂直位移量呈现降低趋势。研究结果可为工作面布置方式和顶板支护方式的选择提供参考依据。

Security and privacy in 6G networks:New areas and new challenges

With the deployment of more and more 5g networks,the limitations of 5g networks have been found,which undoubtedly promotes the exploratory research of 6G networks as the next generation solutions.These investigations include the fundamental security and privacy problems associated with 6G technologies.Therefore,in order to consolidate and solidify this foundational research as a basis for future investigations,we have prepared a survey on the status quo of 6G security and privacy.The survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G networking.We then discuss four key aspects of 6G networks–real-time intelligent edge computing,distributed artificial intelligence,intelligent radio,and 3D intercoms–and some promising emerging technologies in each area,along with the relevant security and privacy issues.The survey concludes with a report on the potential use of 6G.Some of the references used in this paper along and further details of several points raised can be found at:security-privacyin5g-6g.github.io.

Attacks and defences on intelligent connected vehicles:a survey

Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity,which opens up new possibilities for different cyber-attacks,including in-vehicle attacks(e.g.,hijacking attacks)and vehicle-to-everything communicationattacks(e.g.,data theft).These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies.Although many efforts are made to improve the resilience to cyber attacks,there are still many unsolved challenges.This paper first identifies some major security attacks on intelligent connected vehicles.Then,we investigate and summarize the available defences against these attacks and classify them into four categories:cryptography,network security,software vulnerability detection,and malware detection.Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.

A Weighted Threshold Secret Sharing Scheme for Remote Sensing Images Based on Chinese Remainder Theorem

The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images.The emergence of cloud storage has brought new opportunities for storage and management of massive remote sensing images with its large storage space,cost savings.However,the openness of cloud brings challenges for image data security.In this paper,we propose a weighted image sharing scheme to ensure the security of remote sensing in cloud environment,which takes the weights of participants(i.e.,cloud service providers)into consideration.An extended Mignotte sequence is constructed according to the weights of participants,and we can generate image shadow shares based on the hash value which can be obtained from gray value of remote sensing images.Then we store the shadows in every cloud service provider,respectively.At last,we restore the remote sensing image based on the Chinese Remainder Theorem.Experimental results show the proposed scheme can effectively realize the secure storage of remote sensing images in the cloud.The experiment also shows that no matter weight values,each service providers only needs to save one share,which simplifies the management and usage,it also reduces the transmission of secret information,strengthens the security and practicality of this scheme.

VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems

Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.

A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

Fooling intrusion detection systems using adversarially autoencoder

Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows.Although having been used in the real world widely,the above methods are vulnerable to some types of attacks.In this paper,we propose a novel attack framework,Anti-Intrusion Detection AutoEncoder(AIDAE),to generate features to disable the IDS.In the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,respectively.Additionally,a generative adversarial network is used to learn the flexible prior distribution of the latent space.The correlation between continuous and discrete features can be kept by using the proposed training scheme.Experiments conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.

老年破裂前循环动脉瘤患者的急诊显微手术治疗研究

目的探讨采用显微外科手术夹闭老年患者破裂前循环动脉瘤的技术方法和临床疗效。方法回顾性分析都江堰市医疗中心神经外科自2012年5月至2022年12月收治的80例老年破裂前循环动脉瘤患者的病例资料。采用眶上外侧入路、翼点入路或扩大翼点切口开颅,在显微镜下经外侧裂入路,部分患者行Pain's点或改良Pain's点穿刺侧脑室额角,行脑内血肿清除和动脉瘤夹闭,统计动脉瘤夹闭情况。术后6个月随访,使用改良Rankin量表(mRS)来评价患者的临床预后情况。结果本组患者共89个动脉瘤,其中87个(74例)夹闭完全,夹闭成功率97.75%。术后并发症:脑水肿31例(38.75%),经脱水对症治疗好转;血管痉挛38例(47.50%),经扩血管治疗好转;脑积水24例(30.00%),行脑室-腹腔分流术9例,脑积水消失,余15例对症治疗后好转。术后随访6个月,1例患者失访,余79例患者中预后不良(mRS 3~6分)10例,预后良好(mRS 0~2分)69例,预后良好率87.34%。结论采用显微手术技术清除脑内血肿和夹闭动脉瘤的方法对于老年破裂前循环动脉瘤患者具有较高的安全性和有效性。

The optimization research on the coupled of active and passive energy supplying in public institutions in China

The building sector is one of the largest energy user and carbon emitters globally.To increase the utilization rate of renewable energy and reduce carbon dioxide emissions,the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied.In this study,the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software,combined with energy conversion efficiency and data released by the National Bureau of Statistics,and the total energy demand and total energy supply of public institutions were predicted using the load density method.Based on the coupling mechanism of the MARKAL model,the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined.Through the study of the number of public institutions in various regions of China,energy consumption characteristics,construction area,and other related data,the reverse energy flow method is creatively proposed,and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established.The results show that the central region has the highest utilization rate of renewable energy in the public sector,reaching 36.18%.The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%,and it was 12.82% in cold zones.By 2025,the proportion of renewable energy resources in China is expected to reach 29.2%.The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China.

血透护士主观幸福感现状调查及影响因素分析

目的了解血透护士主观幸福感现状及其影响因素,为提高血透护士的主观幸福感提供干预依据。方法于2019年10—11月应用问卷星程序对成都市303名血透护士进行匿名问卷调查,问卷内容包含一般情况调查、主观幸福感量表、社会支持评定量表(Social Support Rate Scale,SSRS)、一般自我效能感量表(General Self-Efficacy Scale, GSES),并对数据进行分析。结果血透护士主观幸福感中幸福感指数得分为10.27±2.50、情感指数得分为4.48±1.59、生活满意度得分为21.38±6.47;社会支持总分为40.13±7.99,其中客观支持得分为9.40±3.18,主观支持得分为22.85±4.85,对支持的利用度得分为7.88±1.86;一般自我效能感得分为2.50±0.54。个人收入、社会支持度及一般自我效能感均为主观幸福感的影响因素,差异均有统计学意义(均P<0.05);社会支持度与一般自我效能感均与主观幸福感呈正相关(均P<0.01);个人收入、社会支持、一般自我效能感均为影响血透护士主观幸福感的因素(均P<0.05)。结论血透护士主观幸福感处于中等水平,个人月收入、社会支持度及一般自我效能感与血透护士主观幸福感密切相关。

Mechanism and clinical efficacy of peripheral blood alive cell treatment on androgenic alopecia

To the Editor:Androgenic alopecia(AGA)is a common chronic hair loss disorder that usually begins in adolescence,with hair follicle miniaturization.[1]Clinical manifestations include reduced hair diameter and volume,potentially leading to baldness.AGA,characterized by progressive hair loss,can affect both sexes.Inflammation around the hair follicle,stress,emotional tension,and poor lifestyle and eating habits can further aggravate the symptoms of AGA.[2]Currently,minoxidil and finasteride(FNS)are the only drug therapies specifically approved for AGA by the FDA.[3]However,these two drugs have obvious side effects and poor patient compliance,so it is urgent to find a safe and effective clinical treatment method for AGA.

Challenges and future directions of secure federated learning:a survey

Federated learning came into being with the increasing concern of privacy security,as people’s sensitive information is being exposed under the era of big data.It is an algorithm that does not collect users’raw data,but aggregates model parameters from each client and therefore protects user’s privacy.Nonetheless,due to the inherent distributed nature of federated learning,it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server.In addition,some recent studies have shown that attackers can recover information merely from parameters.Hence,there is still lots of room to improve the current federated learning frameworks.In this survey,we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning.Several open issues and existing solutions in federated learning are discussed.We also point out the future research directions of federated learning.

Improving Data Utility Through Game Theory in Personalized Differential Privacy

Due to dramatically increasing information published in social networks,privacy issues have given rise to public concerns.Although the presence of differential privacy provides privacy protection with theoretical foundations,the trade-off between privacy and data utility still demands further improvement.However,most existing studies do not consider the quantitative impact of the adversary when measuring data utility.In this paper,we firstly propose a personalized differential privacy method based on social distance.Then,we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each other.We formalize all the payoff functions in the differential privacy sense,which is followed by the establishment of a static Bayesian game.The trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning algorithm.The proposed method achieves fast convergence by reducing the cardinality from n to 2.In addition,the in-place trade-off can maximize the user's data utility if the action sets of the user and the adversary are public while the strategy sets are unrevealed.Our extensive experiments on the real-world dataset prove the proposed model is effective and feasible.

Intestinal Dopamine Receptor D2 is Required for Neuroprotection Against 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced Dopaminergic Neurodegeneration

A wealth of evidence has suggested that gastrointestinal dysfunction is associated with the onset and progression of Parkinson’s disease(PD).However,the mechanisms underlying these links remain to be defined.Here,we investigated the impact of deregulation of intestinal dopamine D2 receptor(DRD2)signaling in response to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced dopaminergic neurodegeneration.Dopamine/dopamine signaling in the mouse colon decreased with ageing.Selective ablation of Drd2,but not Drd4,in the intestinal epithelium,caused a more severe loss of dopaminergic neurons in the substantia nigra following MPTP challenge,and this was accompanied by a reduced abundance of succinate-producing Alleoprevotella in the gut microbiota.Administration of succinate markedly attenuated dopaminergic neuronal loss in MPTP-treated mice by elevating the mitochondrial membrane potential.This study suggests that intestinal epithelial DRD2 activity and succinate from the gut microbiome contribute to the maintenance of nigral DA neuron survival.These findings provide a potential strategy targeting neuroinflammation-related neurological disorders such as PD.

Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence

The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs share health data that contain sensitive information.Therefore,the data exchange process raises privacy concerns,especially when the integration of health data from multiple sources(linkage attack)results in further leakage.Linkage attack is a type of dominant attack in the privacy domain,which can leverage various data sources for private data mining.Furthermore,adversaries launch poisoning attacks to falsify the health data,which leads to misdiagnosing or even physical damage.To protect private health data,we propose a personalized differential privacy model based on the trust levels among users.The trust is evaluated by a defined community density,while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy.To avoid linkage attacks in personalized differential privacy,we design a noise correlation decoupling mechanism using a Markov stochastic process.In addition,we build the community model on a blockchain,which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs.Extensive experiments and analysis on real-world datasets have testified the proposed model,and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.