Mechanical reliable,NIR light-induced rapid self-healing hydrogel electrolyte towards flexible zinc-ion hybrid supercapacitors with low-temperature adaptability and long service life

Hydrogel electrolytes hold great potential in flexible zinc ion supercapacitors(ZICs)due to their high conductivity,good safety,and flexibility.However,freezing of electrolytes at low temperature(subzero)leads to drastic reduction in ionic conductivity and mechanical properties that deteriorates the performance of flexible ZICs.Besides,the mechanical fracture during arbitrary deformations significantly prunes out the lifespan of the flexible device.Herein,a Zn^(2+)and Li^(+)co-doped,polypyrrole-dopamine decorated Sb_(2)S_(3)incorporated,and polyvinyl alcohol/poly(N-(2-hydroxyethyl)acrylamide)double-network hydrogel electrolyte is constructed with favorable mechanical reliability,anti-freezing,and self-healing ability.In addition,it delivers ultra-high ionic conductivity of 8.6 and 3.7 S m^(-1)at 20 and−30°C,respectively,and displays excellent mechanical properties to withstand tensile stress of 1.85 MPa with tensile elongation of 760%,together with fracture energy of 5.14 MJ m^(-3).Notably,the fractured hydrogel electrolyte can recover itself after only 90 s of infrared illumination,while regaining 83%of its tensile strain and almost 100%of its ionic conductivity during−30–60°C.Moreover,ZICs coupled with this hydrogel electrolyte not only show a wide voltage window(up to 2 V),but also provide high energy density of 230 Wh kg^(-1)at power density of 500 W kg^(-1)with a capacity retention of 86.7%after 20,000 cycles under 20°C.Furthermore,the ZICs are able to retain excellent capacity even under various mechanical deformation at−30°C.This contribution will open up new insights into design of advanced wearable flexible electronics with environmental adaptability and long-life span.

TPACK框架下GeoScene Online与地理教学融合的实践

技术革新影响学科教学方式选择,TPACK模式为解决当下学科教学应用新技术“两张皮”问题提供了新思路。本文从科勒和米什拉的理论出发,尝试将GeoScene Online平台与地理课堂教学融合,提出循序渐进、跨学科、基于真实情境和交互式的融合原则,进而在TPACK模式下将GeoScene Online功能特点与高中地理必修内容进行融合分析,构建以PCK、TCK、TPK三条子路径为导向的GeoScene Online与地理教学融合模式,并以“耕地”为主题进行案例实践探索。

A Self-Healing Optoacoustic Patch with High Damage Threshold and Conversion Efficiency for Biomedical Applications

Compared with traditional piezoelectric ultrasonic devices,optoacoustic devices have unique advantages such as a simple preparation process,anti-electromagnetic interference,and wireless long-distance power supply.However,current optoacoustic devices remain limited due to a low damage threshold and energy conversion efficiency,which seriously hinder their widespread applications.In this study,using a self-healing polydimethylsiloxane(PDMS,Fe-Hpdca-PDMS)and carbon nanotube composite,a flexible optoacoustic patch is developed,which possesses the self-healing capability at room temperature,and can even recover from damage induced by cutting or laser irradiation.Moreover,this patch can generate high-intensity ultrasound(>25 MPa)without the focusing structure.The laser damage threshold is greater than 183.44 mJ cm^(-2),and the optoacoustic energy conversion efficiency reaches a major achievement at 10.66×10^(-3),compared with other carbon-based nanomaterials and PDMS composites.This patch is also been successfully examined in the application of acoustic flow,thrombolysis,and wireless energy harvesting.All findings in this study provides new insight into designing and fabricating of novel ultrasound devices for biomedical applications.

Online Consensus Control of Nonlinear Affine Systems From Disturbed Data

Dear Editor,In this letter, we introduce a novel online distributed data-driven robust control approach for learning controllers of unknown nonlinear multi-agent systems(MASs) using state-dependent representations.

High performance photodegradation resistant PVA@TiO_(2)/carboxyl-PES self-healing reactive ultrafiltration membrane

The occurrence of ultrafiltration(UF)membrane fouling frequently hampers the sustainable advancement of UF technology.Reactive self-cleaning UF membranes can effectively alleviate the problem of membrane fouling.Nevertheless,the self-cleaning process may accelerate membrane aging.Addressing these concerns,we present an innovative design concept for composite self-healing materials based on self-cleaning UF membranes.To begin,TiO_(2)nanoparticles were incorporated into the polymer molecular structure via molecular design,resulting in the synthesis of TiO_(2)/carboxyl-polyether sulfone(PES)hybrid materials.Subsequently,the nonsolvent-induced phase inversion technique was employed to prepare a novel of UF membrane.Lastly,a polyvinyl alcohol(PVA)hydrogel coating was applied to the hybrid UF membrane surface to create PVA@TiO_(2)/carboxyl-PES self-healing reactive UF membranes.By establishing a covalent bond,the TiO_(2)nanoparticles were effectively and uniformly dispersed within the UF membrane,leading to exceptional self-cleaning properties.Furthermore,the water-absorbing and swelling properties of PVA hydrogel,along with its capacity to form hydrogen bonds with water molecules,resulted in UF membranes with improved hydrophilicity and active self-healing abilities.The results demonstrated that the water contact angle of PVA@5%TiO_(2)/carboxyl-PES UF membrane was 43.1°.Following a 1-h exposure to simulated solar exposure,the water flux recovery ratio increased from 48.16%to 81.03%.Moreover,even after undergoing five cycles of 12-h simulated sunlight exposure,the UF membranes exhibited a consistent retention rate of over 97%,thus fully demonstrating their exceptional self-cleaning,antifouling,and selfhealing capabilities.We anticipate that the self-healing reactive UF membrane system will serve as a pioneering and comprehensive solution for the self-cleaning antifouling challenges encountered in UF membranes while also effectively mitigating the aging effects of reactive UF membranes.

An intrinsically self-healing and anti-freezing molecular chains induced polyacrylamide-based hydrogel electrolytes for zinc manganese dioxide batteries

The anti-freezing strategy of hydrogels and their self-healing structure are often contradictory,it is vital to break through the molecular structure to design and construct hydrogels with intrinsic anti-freezing/self-healing for meeting the rapid development of flexible and wearable devices in diverse service conditions.Herein,we design a new hydrogel electrolyte(AF/SH-Hydrogel)with intrinsic anti-freezing/self-healing capabilities by introducing ethylene glycol molecules,dynamic chemical bonding(disulfide bond),and supramolecular interaction(multi-hydrogen bond)into the polyacrylamide molecular chain.Thanks to the exceptional freeze resistance(84%capacity retention at-20℃)and intrinsic self-healing capabilities(95%capacity retention after 5 cutting/self-healing cycles),the obtained AF/SH-Hydrogel makes the zinc||manganese dioxide cell an economically feasible battery for the state-of-the-art applications.The Zn||AF/SH-Hydrogel||MnO_(2)device offers a near-theoretical specific capacity of 285 m A h g^(-1)at 0.1 A g^(-1)(Coulombic efficiency≈100%),as well as good self-healing capability and mechanical flexibility in an ice bath.This work provides insight that can be utilized to develop multifunctional hydrogel electrolytes for application in next generation of self-healable and freeze-resistance smart aqueous energy storage devices.

Effect of drying cracks on swelling and self-healing of bentonite-sand blocks used as engineered barriers for radioactive waste disposal

Experiments were conducted to evaluate the healing of drying cracks in air-dried bentonite-sand blocks after hydration and swelling in groundwater,providing justifications to simplify the protection of blocks prior to installation in a high-level radioactive waste repository.Synthetic groundwater was prepared to represent the geochemistry of Beishan groundwater,and was used to hydrate the blocks during the swelling pressure and swelling strain measurements,as Beishan is the most promising site for China's repository.Healing of the surface cracks was recorded by photography,and healing of the internal cracks was visualized by CT images and hydraulic conductivity of air-dried blocks.The results indicate that the maximum swelling pressure and swelling strain are primarily affected by the geochemistry of Beishan groundwater,but not affected by the drying cracks.The maximum swelling pressure and swelling strain of air-dried blocks are comparable to or even higher than the pressure and strain of fresh blocks.The maximum swelling pressure measured in strong(i.e.high ion strength)Beishan groundwater was 44%of the pressure measured in deionized(DI)water,and the maximum swelling strain was reduced to 23%of the strain measured in DI water.Nevertheless,the remained swelling of the blocks hydrated in strong Beishan groundwater was sufficient to heal the surface and internal drying cracks,as demonstrated by the pictures of surface cracks and CT images.The hydraulic conductivity of the air-dried block permeated with strong groundwater was comparable(3.7×higher)to the hydraulic conductivity of the fresh block,indicating the self-healing of drying cracks after hydration and swelling in groundwater.A simplified method of protecting the block with plastic wraps before installation is recommended,since the remained swelling of the block hydrated in Beishan groundwater is sufficient to heal the drying cracks.

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

基于Online-GRU信道预测的星上自适应功率控制方法

针对传统卫星功率控制方法存在资源浪费、时延长的问题,提出一种基于在线-门控循环单元(Online-GRU)信道预测的星上自适应功率控制方法,通过在线训练更新网络参数来解决离线预测算法存在的累积误差的问题。仿真结果表明,提出的在线训练算法比离线算法预测精确度提升了38.30%,相比在线-长短期记忆网络(Online-LSTM)节约了63.21%的训练时间;提出的自适应功率控制方法比固定发射功率的方法节约了55.74%的发射功率;同时,相比基于地面定时反馈信道状态的自适应功率控制方法具备更好的鲁棒性。

QoE oriented intelligent online learning evaluation technology in B5G scenario

Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.

Will Online Car-Hailing Affect Consumers’ Decisions about Automobile Purchase?—An Empirical Study Based on Questionnaire Investigation

The online car-hailing industry, which provides the right of use, has a certain impact on the traditional automobile market, but there is no unified theory on whether it has a positive impact or a negative impact. Based on 362 consumer questionnaire data, this study builds a structural equation model to discuss the driving factors of residents’ choice of online car-hailing and whether the development of online car-hailing will have a certain substitution impact on the sales of private cars. From the perspective of consumers’ purchase intention, the research results show that consumers’ price consciousness, convenience consciousness, environmental protection consciousness and possession tendency will affect their choice of travel mode, and the use of online car-hailing is positively correlated with consumers’ willingness to replace private car ownership with online car-hailing.

Research on the Collaborative Governance of Social Responsibility in Online Audiovisual Enterprises

This paper aims to analyze the present conditions of the social responsibility ecosystem in online audiovisual enterprises in the digital age. It focuses on the governance of social responsibility in these enterprises and conducts an in-depth analysis of the problems and influencing factors related to the social responsibility aberrations of online audiovisual enterprises. Drawing upon social responsibility theory and collaborative governance theory, this research constructs a social responsibility guidance and governance system guided by the public, supported by the voluntary fulfillment of responsibilities by online audiovisual enterprises, and based on the collaborative participation of diverse stakeholders. It explores and optimizes the implementation pathways of this system, providing theoretical support and practical guidance for promoting the sustainable development of online audiovisual enterprises. Furthermore, it aims to contribute to the creation of a harmonious Internet ecosystem.

Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring

Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Efficient unequal error protection for online fountain codes

In this paper,an efficient unequal error protection(UEP)scheme for online fountain codes is proposed.In the buildup phase,the traversing-selection strategy is proposed to select the most important symbols(MIS).Then,in the completion phase,the weighted-selection strategy is applied to provide low overhead.The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme.Simulation results show that in terms of MIS and the least important symbols(LIS),when the bit error ratio is 10-4,the proposed scheme can achieve 85%and 31.58%overhead reduction,respectively.

Which Neighborhoods Have Easier Access to Online Home Delivery Services?A Spatiotemporal Accessibility Analysis in Nanjing,China

The rise in online home delivery services(OHDS)has had a significant impact on how urban services are supplied and used in recent years.Studies on the spatial accessibility of OHDS are emerging,but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS.This study measures the spatiotemporal accessibility of four types of OHDS,namely leisure,fresh and convenient,medical,and catering services.The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test(K-W test).The case study in Nanjing,China,suggests that:1)spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension.2)The spatiotemporal accessibility of OHDS varies geographically and socioeconomically.Neighborhoods located in the main city or neighborhoods with higher housing prices,higher population density,and higher point of interest(POI)mix have better OHDS spatiotemporal accessibility.Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective,and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.