Design Principles and Mechanistic Understandings of Non-Noble-Metal Bifunctional Electrocatalysts for Zinc-Air Batteries

Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs.Therefore,feasible and advanced non-noble-metal elec-trocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction.In this review,we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field.Then,we discussed the work-ing mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design,crystal structure tuning,interface strategy,and atomic engineering.We also included theoretical studies,machine learning,and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions.Finally,we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

A Survey on an Emerging Safety Challenge for Autonomous Vehicles:Safety of the Intended Functionality

As the complexity of autonomous vehicles(AVs)continues to increase and artificial intelligence algorithms are becoming increasingly ubiquitous,a novel safety concern known as the safety of the intended functionality(SOTIF)has emerged,presenting significant challenges to the widespread deployment of AVs.SOTIF focuses on issues arising from the functional insufficiencies of the AVs’intended functionality or its implementation,apart from conventional safety considerations.From the systems engineering standpoint,this study offers a comprehensive exploration of the SOTIF landscape by reviewing academic research,practical activities,challenges,and perspectives across the development,verification,validation,and operation phases.Academic research encompasses system-level SOTIF studies and algorithm-related SOTIF issues and solutions.Moreover,it encapsulates practical SOTIF activities undertaken by corporations,government entities,and academic institutions spanning international and Chinese contexts,focusing on the overarching methodologies and practices in different phases.Finally,the paper presents future challenges and outlook pertaining to the development,verification,validation,and operation phases,motivating stakeholders to address the remaining obstacles and challenges.

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

UltraStar:A Lightweight Simulator of Ultra-Dense LEO Satellite Constellation Networking for 6G

The mega-constellation network has gained significant attention recently due to its great potential in providing ubiquitous and high-capacity connectivity in sixth-generation(6G)wireless communication systems.However,the high dynamics of network topology and large scale of mega-constellation pose new challenges to the constellation simulation and performance evaluation.In this paper,we introduce UltraStar,a lightweight network simulator,which aims to facilitate the complicated simulation for the emerging mega-constellation of unprecedented scale.Particularly,a systematic and extensible architecture is proposed,where the joint requirement for network simulation,quantitative evaluation,data statistics and visualization is fully considered.For characterizing the network,we make lightweight abstractions of physical entities and models,which contain basic representatives of networking nodes,structures and protocol stacks.Then,to consider the high dynamics of Walker constellations,we give a two-stage topology maintenance method for constellation initialization and orbit prediction.Further,based on the discrete event simulation(DES)theory,a new set of discrete events is specifically designed for basic network processes,so as to maintain network state changes over time.Finally,taking the first-generation Starlink of 11927 low earth orbit(LEO)satellites as an example,we use UltraStar to fully evaluate its network performance for different deployment stages,such as characteristics of constellation topology,performance of end-to-end service and effects of network-wide traffic interaction.The simulation results not only demonstrate its superior performance,but also verify the effectiveness of UltraStar.

Evaluation of toppling rock slopes using a composite cloud model with DEMATEL–CRITIC method

Safety evaluation of toppling rock slopes developing in reservoir areas is crucial. To reduce the uncertainty of safety evaluation, this study developed a composite cloud model, which improved the combination weights of the decision-making trial and evaluation laboratory (DEMATEL) and criteria importance through intercriteria correlation (CRITIC) methods. A safety evaluation system was developed according to in situ monitoring data. The backward cloud generator was used to calculate the numerical characteristics of a cloud model of quantitative indices, and different virtual clouds were used to synthesize some clouds into a generalized one. The synthesized numerical characteristics were calculated to comprehensively evaluate the safety of toppling rock slopes. A case study of a toppling rock slope near the Huangdeng Hydropower Station in China was conducted using monitoring data collected since operation of the hydropower project began. The results indicated that the toppling rock slope was moderately safe with a low safety margin. The composite cloud model considers the fuzziness and randomness of safety evaluation and enables interchange between qualitative and quantitative knowledge. This study provides a new theoretical method for evaluating the safety of toppling rock slopes. It can aid in the predication, control, and even prevention of disasters.

Solid-phase microextraction of endogenous metabolites from intact tissue validated using a Biocrates standard reference method kit

Improved analytical methods for the metabolomic profiling of tissue samples are constantly needed.Currently,conventional sample preparation methods often involve tissue biopsy and/or homogenization,which disrupts the endogenous metabolome.In this study,solid-phase microextraction(SPME)fibers were used to monitor changes in endogenous compounds in homogenized and intact ovine lung tissue.Following SPME,a Biocrates AbsoluteIDQ assay was applied to make a downstream targeted metabolomics analysis and confirm the advantages of in vivo SPME metabolomics.The AbsoluteIDQ kit enabled the targeted analysis of over 100 metabolites via solid-liquid extraction and SPME.Statistical analysis revealed significant differences between conventional liquid extractions from homogenized tissue and SPME results for both homogenized and intact tissue samples.In addition,principal component analysis revealed separated clustering among all the three sample groups,indicating changes in the metabolome due to tissue homogenization and the chosen sample preparation method.Furthermore,clear differences in free metabolites were observed when extractions were performed on the intact and homogenized tissue using identical SPME procedures.Specifically,a direct comparison showed that 47 statistically distinct metabolites were detected between the homogenized and intact lung tissue samples(P<0.05)using mixed-mode SPME fibers.These changes were probably due to the disruptive homogenization of the tissue.This study's findings highlight both the importance of sample preparation in tissue-based metabolomics studies and SPME's unique ability to perform minimally invasive extractions without tissue biopsy or homogenization while providing broad metabolite coverage.

The strain rate sensitive and anisotropic behavior of rare-earth magnesium alloy ZEK100 sheet

To overcome the limitation in formability at room temperature,manufacturers have developed magnesium alloys with remarkable properties by adding rare-earth elements.The rare-earth magnesium alloys behave differently from the conventional alloys,especially with respect to their coupled anisotropic and strain rate sensitive behavior.In the current work,such behavior of the rare-earth Mg alloy ZEK100 sheet at room temperature is investigated with the aid of the elastic viscoplastic self-consistent polycrystal plasticity model.Different strain rate sensitivities(SRSs)for various deformation modes are employed by the model to simulate the strain rate sensitive behaviors under different loading directions and loading rates.Good agreement between the experiments and simulations reveals the importance and necessity of using different SRSs for each deformation mode in hexagonal close-packed metals.Furthermore,the relative activities of each deformation mode and the texture evolution during different loadings are discussed.The anisotropic and strain rate sensitive behavior is ascribed to the various operating deformation modes with different SRSs during loading along different directions.

In vivo solid phase microextraction for therapeutic monitoring and pharmacometabolomic fingerprinting of lung during in vivo lung perfusion of FOLFOX

In vivo lung perfusion(IVLP)is a novel isolated lung technique developed to enable the local,in situ administration of high-dose chemotherapy to treat metastatic lung cancer.Combination therapy using folinic acid(FOL),5-fluorouracil(F),and oxaliplatin(OX)(FOLFOX)is routinely employed to treat several types of solid tumours in various tissues.However,F is characterized by large interpatient variability with respect to plasma concentration,which necessitates close monitoring during treatments using of this compound.Since plasma drug concentrations often do not reflect tissue drug concentrations,it is essential to utilize sample-preparation methods specifically suited to monitoring drug levels in target organs.In this work,in vivo solid-phase microextraction(in vivo SPME)is proposed as an effective tool for quantitative therapeutic drug monitoring of FOLFOX in porcine lungs during pre-clinical IVLP and intravenous(IV)trials.The concomitant extraction of other endogenous and exogenous small molecules from the lung and their detection via liquid chromatography coupled to high resolution mass spectrometry(LC-HRMS)enabled an assessment of FOLFOX's impact on the metabolomic profile of the lung and revealed the metabolic pathways associated with the route of administration(IVLP vs.IV)and the therapy itself.This study also shows that the immediate instrumental analysis of metabolomic samples is ideal,as long-term storage at80℃ results in changes in the metabolite content in the sample extracts.

Intermittent Control for Fixed-Time Synchronization of Coupled Networks

Dear Editor, This letter deals with fixed-time synchronization(Fd-TS) of complex networks(CNs) under aperiodically intermittent control(AIC)for the first time. The average control rate and a new Lyapunov function are proposed to overcome the difficulty of dealing with fixedtime stability/synchronization of CNs for AIC.

Comparison of different approaches for direct coupling of solid-phase microextraction to mass spectrometry for drugs of abuse analysis in plasma

The direct coupling of solid-phase microextraction(SPME)to mass spectrometry(MS)(SPME-MS)has proven to be an effective method for the fast screening and quantitative analysis of compounds in complex matrices such as blood and plasma.In recent years,our lab has developed three novel SPME-MS techniques:SPME-microfluidic open interface-MS(SPME-MOI-MS),coated blade spray-MS(CBS-MS),and SPME-probe electrospray ionization-MS(SPME-PESI-MS).The fast and high-throughput nature of these SPME-MS technologies makes them attractive options for point-of-care analysis and anti-doping testing.However,all these three techniques utilize different SPME geometries and were tested with different MS instruments.Lack of comparative data makes it difficult to determine which of these methodologies is the best option for any given application.This work fills this gap by making a comprehensive comparison of these three technologies with different SPME devices including SPME fibers,CBS blades,and SPME-PESI probes and SPME-liquid chromatography-MS(SPME-LC-MS)for the analysis of drugs of abuse using the same MS instrument.Furthermore,for the first time,we developed different desorption chambers for MOI-MS for coupling with SPME fibers,CBS blades,and SPME-PESI probes,thus illustrating the universality of this approach.In total,eight analytical methods were developed,with the experimental data showing that all the SPME-based methods provided good analytical performance with R^(2)of linearities larger than 0.9925,accuracies between 81%and 118%,and good precision with an RSD%≤13%.

Feasibility Assessment of Fast Numerical Simulations for Real-Time Monitoring and Control of PEM Fuel Cells

Computational models that ensure accurate and fast responses to the variations in operating conditions,such as the cell tem-perature and relative humidity(RH),are essential monitoring tools for the real-time control of proton exchange membrane(PEM)fuel cells.To this end,fast cell-area-averaged numerical simulations are developed and verifi ed against the present experiments under various RH levels.The present simulations and measurements are found to agree well based on the cell voltage(polarization curve)and power density under variable RH conditions(RH=40%,RH=70%,and RH=100%),which verifi es the model accuracy in predicting PEM fuel cell performance.In addition,computationally feasible reduced-order models are found to deliver a fast output dataset to evaluate the charge/heat/mass transfer phenomena as well as water production and two-phase fl ow transport.Such fast and accurate evaluations of the overall fuel cell operation can be used to inform the real-time control systems that allow for the improved optimization of PEM fuel cell performance.

Graph-based structural joint pose estimation in non-line-of-sight conditions

In post-earthquake surveys,it is difficult(and often infeasible)to observe and quantify displacements beyond line-of-sight(LOS),given seismic force-resisting and gravity systems exist completely or partially within a building′s enclosure.To overcome this limitation,we develop a novel framework that generalizes graph-based state estimation towards structural joint localization via engineered landmarks.These landmarks provide an indirect means to estimate residual displacements where direct LOS is unavailable.Within our framework,engineered landmarks define topologies of uniquely identifiable landmarks that are either visible or non-visible to a robot performing simultaneous localization and mapping(SLAM).Within the SLAM approach,factors encoding robot odometry and robot-to-visible landmark measurements are formulated for the cases of wireless sensing and fiducial object detection and tracking.Visible landmarks are rigidly attached to non-visible landmark subsets for each engineered landmark,where the complete set of non-visible landmarks form globally rigid and localizable connectivity graphs via range-based factors.Complimentary subsets of non-visible landmarks are embedded within the base structure and uniquely define joint pose via geometric factors.All factors are unified within a common graph to solve for the maximum a posteriori estimate of robot,landmark,and joint states via nonlinear least squares optimization.To demonstrate the applicability of our approach,we apply the Monte Carlo method over a parameterization of system noise to calculate residual joint pose error distributions,maximum average inter-story drift ratios,and related summary statistics for a 19-story nonlinear structural model.By performing nonlinear time history analyses over sets of service-level and maximum considered earthquakes,our parametric study gives insight into our method′s application towards post-earthquake building evaluation in non-LOS conditions.

Thrust characteristics of nano-carbon/Al/oxygenated salt nanothermites for micro-energetic applications

Combustion within small motors is key in the application-specific development of nanothermite-based micro-energetic systems. This study evaluates the performance of nanothermite mixtures in a converging-diverging nozzle and an open tube. Mixtures were prepared using nano-aluminum(n-Al),potassium perchlorate(KClO_(4)), and different carbon nanomaterials(CNMs) including graphene-oxide(GO), reduced GO, carbon nanotubes(CNTs) and nanofibers(CNFs). The mixtures were packed at different densities and ignited by laser beam. Performance was measured using thrust measurement,high-speed imaging, and computational fluid dynamics modeling, respectively. Thrust, specific impulse(ISP), volumetric impulse(ISV), as well as normalized energy were found to increase notably with CNM content. Two distinctive reaction regimes(fast and slow) were observed in combustion of low and high packing densities(20% and 55%TMD), respectively. Total impulse(IFT) and ISPwere maximized in the 5%GO/Al/KClO_4 mixture, producing 7.95 m N·s and 135.20 s respectively at 20%TMD, an improvement of 57%compared to a GO-free sample(5.05 m N·s and 85.88 s). CFD analysis of the motors over predicts the thrust generated but trends in nozzle layout and packing density agree with those observed experimentally;peak force was maximized by reducing packing density and using an open tube. The numerical force profiles fit better for the nozzle cases than the open tube scenarios due to the rapid nature of combustion. This study reveals the potential of GO in improving oxygenated salt-based nanothermites,and further demonstrates their applicability for micro-propulsion and micro-energetic applications.

Synthesis and Characterization of Novel Hydrolytically Degradable Polyesters

Six novel hydrolytically degradable polyesters were synthesized from thiodipropionic acid(TDPA)and five diols by melt polycondensation,and characterized by FT-IR,1H NMR,gel permeation chromatography,differential scanning calorimetry and thermogravimetry analysis.The polystyrene-equivalent number-average(Mn)and weight-average molecular weight(Mw)of these polyesters ranged from 4900-11100 Da and 7900-20879 Da,respectively,with PDI values of 1.48-1.98.The melting point varied from 62.3-127.9℃,and the 50%mass-loss temperature ranged between 387-417℃.The degradation of these polyesters was studied in terms of relative weight loss in distilled water at different pH.Weight losses of 14%-26%were obtained at pH 7.0,26%-38%at pH 6.0,and 32%-43%at pH 8.3 over a 20-week period.The ecotoxicity study suggested that safety of the synthesized polyesters for the eisenia foetida.These results indicate that these polyesters have a combination of good thermal and degradability behaviors,which can be tailored through selection of the diol monomers used in the synthesis.

Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

In light of the escalating demand and intricacy of services in contemporary terrestrial,maritime,and aerial combat operations,there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks.Software-Defined Networking(SDN)proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts,due to its intrinsic ability to flexibly allocate and centrally administer network resources.This study pivots around the optimization of SDN controller deployment within airborne data link clusters.A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed.Within this architectural framework,the controller deployment issue is reframed as a two-fold problem:subdomain partition-ing and central interaction node selection.We advocate a subdomain segmentation approach grounded in node value ranking(NDVR)and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm(AFSA).The advanced NDVR-AFSA(Node value ranking-Improved artificial fish swarm algorithm)algorithm makes use of a chaos algorithm for population initialization,boosting population diversity and circumventing premature algorithm convergence.By the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations,the algorithm’s search range adaptability is enhanced,thereby increasing the possibility of obtaining globally optimal solutions,while concurrently augmenting cluster reliability.The simulation results verify the advantages of the NDVR-IAFSA algorithm,achieve a better load balancing effect,improve the reliability of aviation data link cluster,and significantly reduce the average propagation delay and disconnection rate,respectively,by 12.8%and 11.7%.This shows that the optimization scheme has important significance in practical application,and can meet the high requirements of modern sea,land,and air operations to aviation airborne communication networks.

Synthesis strategies of covalent organic frameworks: An overview from nonconventional heating methods and reaction media

Covalent organic frameworks(COFs), as an emerging class of porous crystalline materials constructed by covalent links between the building monomers, have gained tremendous attention. Over the past 15 years, COFs have made rapid progress and substantial development in the chemistry and materials fields. However, the synthesis of COFs has been dominated by solvothermal methods for a long time and it usually involves high temperature, high pressure and toxic organic solvents, which created many challenges for environmental considerations. Recently,the exploration of new approaches for facile fabrication of COFs has aroused extensive interest. Hence, in this review, we comprehensively describe the synthetic strategies of COFs from the aspects of nonconventional heating methods and reaction media. In addition, the advantages,limitations and properties of the preparation methods are compared. Finally, we outline the main challenges and development prospects of the synthesis of COFs in the future and propose some possible solutions.

视觉感知中的边际效应递减现象

边际效应递减法则是经济学基本理论,在管理、环境、教育等很多领域都实证了该法则的存在性.文中通过实验验证视觉感知中是否存在边际效应递减现象.首先选取随机生成的散点图作为实验数据,选取散点图相关性作为感知对象,招募参与者并设计了实验流程;然后让参与者根据视觉感知估计散点图相关系数大小,并记录参与者在多次测试中的感知偏差和完成时间等量化指标;最后用基数效应理论计算散点图相关性视觉感知偏差的总效应曲线和边际效应曲线,并选取边际效应作为评价指标.实验结果表明,参与者在实验中出现了边际效应递减现象,即随着测试次数的增加,参与者相关性视觉感知偏差的减小幅度呈现降低趋势;同时,参与者容易低估相关性,有可视化专业知识的参与者和其他参与者在感知偏差与完成时间上不存在显著性差异.

MXene-Based Elastomer Mimetic StretchableSensors: Design, Properties, and Applications

Flexible sensors based on MXene-polymer composites are highly prospective for next-generation wearable electronics used in human-machine interfaces.One of the motivating factors behind the progress of flexible sensors is the steady arrival of new conductive materials.MXenes,a new family of 2D nanomaterials,have been draw-ing attention since the last decade due to their high electronic conduc-tivity,processability,mechanical robustness and chemical tunability.In this review,we encompass the fabrication of MXene-based polymeric nanocomposites,their structure-property relationship,and applications in the flexible sensor domain.Moreover,our discussion is not only lim-ited to sensor design,their mechanism,and various modes of sensing platform,but also their future perspective and market throughout the world.With our article,we intend to fortify the bond between flexible matrices and MXenes thus promoting the swift advancement of flexible MXene-sensors for wearable technologies.

Insights and reviews on battery lifetime prediction from research to practice

The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliability.This article comprehensively examines various methods used to forecast battery health,including physics-based models,empirical models,and equivalent circuit models,among others.It delves into the promise of data-driven prognostics,utilizing both conventional machine learning and cuttingedge deep neural network techniques.The advantages and limitations of hybrid models are thoroughly analyzed,with a focus on the benefits of integrating diverse data sources to improve prognostic precision.Through practical case studies,the article showcases the effectiveness and flexibility of these approaches.It also critically addresses the challenges encountered in applying battery health prognostics in realworld scenarios,such as issues of scalability,complexity,and data anomalies.Despite these challenges,the article underscores the emerging opportunities brought about by recent technological,academic,and research advancements.These include the development of digital twin models for batteries,the use of data-centric AI and standardized benchmarking,the potential integration of blockchain technology for enhanced data security and transparency,and the synergy between edge and cloud computing to boost data analysis and processing.The primary goal of this article is to enrich the understanding of current battery health prognostic techniques and to inspire further research aimed at overcoming existing hurdles and tapping into new opportunities.It concludes with a visionary perspective on future research directions and potential developments in this evolving field,encouraging both researchers and practitioners to explore innovative solutions.