Kinetic-boosted CO_(2) electroreduction to formate via synergistic electric-thermal field on hierarchical bismuth with amorphous layer

Electrocatalytic converting CO_(2) into chemical products has emerged as a promising approach to achieving carbon neutrality.Herein,we report a bismuth-based catalyst with high curvature terminal and amorphous layer which fabricated via two-step electrodeposition achieves stable formate output in a wide voltage window of 600 mV.The Faraday efficiency(FE) of formate reached up to 99.4% at-0.8 V vs.RHE and it remained constant for more than 92 h at-15 mA cm^(-2).More intriguingly,FE formate of95.4% can be realized at a current density of industrial grade(-667.7 mA cm^(-2)) in flow cell.The special structure promoted CO_(2) adsorption and reduced its activation energy and enhanced the electric-thermal field and K^(+) enrichment which accelerated the reaction kinetics.In situ spectroscopy and theoretical calculation further confirmed that the introduction of amorphous structure is beneficial to adsorpting CO_(2)and stabling*OCHO intermediate.This work provides special insights to fabricate efficient electrocatalysts by means of structural and crystal engineering and makes efforts to realize the industrialization of bismuth-based catalysts.

Data-driven modeling on anisotropic mechanical behavior of brain tissue with internal pressure

Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.

Effects of counter-current driven by electron cyclotron waves on neoclassical tearing mode suppression

Through theoretical analysis,we construct a physical model that includes the influence of counter-external driven current opposite to the plasma current direction in the neoclassical tearing mode(NTM).The equation is used with this model to obtain the modified Rutherford equation with co-current and counter-current contributions.Consistent with the reported experimental results,numerical simulations have shown that the localized counter external current can only partially suppress NTM when it is far from the resonant magnetic surface.Under some circumstances,the Ohkawa mechanism dominated current drive(OKCD)by electron cyclotron waves can concurrently create both co-current and counter-current.In this instance,the minimal electron cyclotron wave power that suppresses a particular NTM was calculated by the Rutherford equation.The result is marginally less than when taking co-current alone into consideration.As a result,to suppress NTM using OKCD,one only needs to align the co-current with a greater OKCD peak well with the resonant magnetic surface.The effect of its lower counter-current does not need to be considered because the location of the counter-current deviates greatly from the resonant magnetic surface.

Data Driven Vibration Control:A Review

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

MPC-based Torque Distribution for Planar Motion of Four-wheel Independently Driven Electric Vehicles:Considering Motor Models and Iron Losses

The most critical obstacle for four-wheel independently driven electric vehicles(4WID-EVs)is the driving range.Being the actuators of 4WID-EVs,motors account for its major power consumption.In this sense,by properly distributing torques to minimize the power consumption,the driving range of 4WID-EV can be effectively improved.This paper proposes a model predictive control(MPC)-based torque distribution scheme,which minimizes the power consumption of 4WID-EVs while guaranteeing its tracking performance of planar motions.By incorporating the motor model considering iron losses,the optimal torque distribution can be achieved without an additional torque controller.Also,for this reason,the proposed control scheme is computationally efficient,since the power consumption term to be optimized,which is expressed as the product of the motor voltages and currents,is much simpler than that derived from the efficiency map.With reasonable simplification and linearization,the MPC problem is converted to a quadratic programming problem,which can be solved efficiently.The simulation results in MATLAB and CarSim co-simulation environments demonstrate that the proposed scheme effectively reduces power consumption with guaranteed tracking performance.

Power Consumption Characteristics Research on Mobile System of Electrically Driven Large-Load-Ratio Six-Legged Robot

The electrically driven large-load-ratio six-legged robot with engineering capability can be widely used in outdoor and planetary exploration.However,due to the particularity of its parallel structure,the effective utilization rate of energy is not high,which has become an important obstacle to its practical application.To research the power consumption characteristics of robot mobile system is beneficial to speed up it toward practicability.Based on the configuration and walking modes of robot,the mathematical model of the power consumption of mobile system is set up.In view of the tripod gait is often selected for the six-legged robots,the simplified power consumption model of mobile system under the tripod gait is established by means of reducing the dimension of the robot’s statically indeterminate problem and constructing the equal force distribution.Then,the power consumption of robot mobile system is solved under different working conditions.The variable tendencies of the power consumption of robot mobile system are respectively obtained with changes in the rotational angles of hip joint and knee joint,body height,and span.The articulated rotational zones and the ranges of body height and span are determined under the lowest power consumption.According to the walking experiments of prototype,the variable tendencies of the average power consumption of robot mobile system are respectively acquired with changes in duty ratio,body height,and span.Then,the feasibility and correctness of theory analysis are verified in the power consumption of robot mobile system.The proposed analysis method in this paper can provide a reference on the lower power research of the large-load-ratio multi-legged robots.

A deep learning driven hybrid beamforming method for millimeter wave MIMO system

The hybrid beamforming is a promising technology for the millimeter wave MIMO system,which provides high spectrum efficiency,high data rate transmission,and a good balance between transmission performance and hardware complexity.The most existing beamforming systems transmit multiple streams by formulating multiple orthogonal beams.However,the Neural network Hybrid Beamforming(NHB)adopts a totally different strategy,which combines multiple streams into one and transmits by employing a high-order non-orthogonal modulation strategy.Driven by the Deep Learning(DL)hybrid beamforming,in this work,we propose a DL-driven nonorthogonal hybrid beamforming for the single-user multiple streams scenario.We first analyze the beamforming strategy of NHB and prove it with better Bit Error Rate(BER)performance than the orthogonal hybrid beamforming even with the optimal power allocation.Inspired by the NHB,we propose a new DL-driven beamforming scheme to simulate the NHB behavior,which avoids time-consuming neural network training and achieves better BERs than traditional hybrid beamforming.Moreover,our simulation results demonstrate that the DL-driven nonorthogonal beamforming outperforms its traditional orthogonal beamforming counterpart in the presence of subconnected schemes and imperfect Channel State Information(CSI).

Deoxyribonucleic acid methylation driven aberrations in pancreatic cancer-related pathways

Pancreatic cancer(PanCa)presents a catastrophic disease with poor overall survival at advanced stages,with immediate requirement of new and effective treatment options.Besides genetic mutations,epigenetic dysregulation of signaling pathway-associated enriched genes are considered as novel therapeutic target.Mechanisms beneath the deoxyribonucleic acid methylation and its utility in developing of epi-drugs in PanCa are under trails.Combinations of epigenetic medicines with conventional cytotoxic treatments or targeted therapy are promising options to improving the dismal response and survival rate of PanCa patients.Recent studies have identified potentially valid pathways that support the prediction that future PanCa clinical trials will include vigorous testing of epigenomic therapies.Epigenetics thus promises to generate a significant amount of new knowledge of biological and medical importance.Our review could identify various components of epigenetic mechanisms known to be involved in the initiation and development of pancreatic ductal adenocarcinoma and related precancerous lesions,and novel pharmacological strategies that target these components could potentially lead to breakthroughs.We aim to highlight the possibilities that exist and the potential therapeutic interventions.

数智时代的信息分析方法:数据驱动、知识驱动及融合驱动

数智时代,面对大数据、大知识所带来的挑战,如何创新发展信息分析方法,关乎新时代信息分析工作的开展,关乎数据资源的开发利用。本文在梳理现有信息分析方法的基础上,提出数据驱动、知识驱动,以及数据与知识融合驱动的三种数智型方法思路。首先,提出基于文本、网络、音频、图像等的数据驱动以及与之相应的文本挖掘、图挖掘、音频挖掘、图像挖掘等信息分析模式;其次,提出基于专家知识库、通用知识库、领域知识图谱、通用知识图谱等的知识驱动信息分析模式;最后,提出基于特征、模型、决策三种层面的数据与知识融合驱动的信息分析模式。通过以上三种方法,构建能够系统融合大数据、大知识的信息分析方法,实现数智融合型信息分析,促进图书情报学科方法论发展,赋能国家决策和社会治理。图3。表1。参考文献59。

On the Effects of Driven Element L/D Ratio and Length in VHF-SHF Yagi-Uda Arrays

While the Yagi-Uda array has been studied for decades, one issue appears to have received less attention than perhaps it should, namely, the effects on performance of the array’s driven element length and its length-to-diameter ratio. This paper looks at that question. It shows that decreasing the L/D ratio increases impedance bandwidth, but it may shift the IBW band sufficiently far from the design frequency that other parameters such as gain and front-to-back ratio probably are adversely affected. It also shows that array performance is not relatively independent of element diameters. This paper also investigates the effect of lengthening the driven element, which can substantially improve IBW. Several iterations of a 3-element prototype and improved arrays are modeled with the Method of Moments and discussed in detail. A five step design procedure is recommended and applied to a Genetic Algorithm-optimized 3-element Yagi at 146 MHz. This array exhibits excellent performance in terms of gain, front-to-back ratio, and especially impedance bandwidth (nearly 14% for voltage standing wave ratio ≤ 2:1 with two frequencies at which 50 ? is almost perfectly matched). While the analysis and recommended design steps are applied to cylindrical array elements, which commonly are aluminum tubing for stand-alone VHF-SHF Yagis, they can be applied to other element geometries as well using equivalent cylindrical radii, for example, Printed Circuit Board traces for planar arrays. One consequence of lengthening the driven element while reducing its L/D ratio is that some reactance is introduced at the array feedpoint which must be tuned out, and two approaches for doing so are suggested.

Implosion Plasma Driven Fusion Pellet of Inertial Confinement(A Short Memorandum)

The implosion plasma drive fusion pellet of inertial confinement is a concept related to nuclear fusion,a process in which atomic nuclei combine to form heavier nuclei,releasing a large amount of energy in the process.The implosion plasma drive fusion pellet is a potential fuel source for achieving controlled nuclear fusion.ICF(inertial confinement fusion)is a technique used to achieve fusion by compressing a small target containing fusion fuel to extremely high densities and temperatures using lasers or other methods.The implosion plasma drive fusion pellet concept involves using a small pellet of deuterium and tritium(two isotopes of hydrogen)as fusion fuel,and then rapidly heating and compressing it using a pulsed power system.The implosion process creates a high-pressure plasma that ignites the fusion reactions,releasing energy in the form of neutrons and charged particles.The resulting energy can be captured and used for power generation.This technology is still in the experimental stage,and significant research and development is required to make it commercially viable.However,it has the potential to provide a virtually limitless source of clean energy with no greenhouse gas emissions or long-term radioactive waste.Be that as it may,ICF has to get exact control of the implosion process,mitigate insecurities,and create modern materials and advances to resist the extraordinary conditions of the combined response.

新质生产力:中国式现代化的新动能与新路径

纵观西方现代化和中国式现代化的历史进程,生产力的持续发展是现代化的共同特征。人类历史上每一次生产力的重大跃升都以一系列开创性的科学发现和技术突破为先导,因而科技创新是社会生产力发展的关键因素。随着新一轮科技革命和产业变革的孕育兴起,新质生产力将取代传统生产力,成为推动中国式现代化的重要力量。新质生产力是以科技创新为主导,实现关键性、颠覆性技术突破的生产力,本质上是社会生产力的一次重大跃升。发展新质生产力是推动高质量发展的内在要求和重要着力点,亦是中国式现代化的必然选择。加快形成新质生产力以推进中国式现代化,应在宏观层面坚持市场与政府有机结合,使二者协力推动科技创新;应在中观层面培育战略性新兴产业和未来产业,增强发展新动能;应在微观层面坚持“两个毫不动摇”,激发各类创新主体活力。

考虑灵活资源及模数驱动方法的电力系统调度方法综述

可再生能源及负荷种类的增多给电力系统运行带来更大不确定性,也给电力系统经济调度带来挑战。深入分析总结灵活资源特性并对不确定性的准确建模是评估电力系统灵活性和实现经济调度的基础。基于模型或数据驱动的调度建模方法面临诸多挑战,将模型与数据驱动方式相结合,并充分发挥二者优势是电力系统优化调度的发展方向。该文从灵活资源分类及特性、系统灵活性评估方法及优化调度的模型与数据驱动建模3个方面进行了归纳整理。首先,从电网侧、供应侧及需求侧3个方面介绍了系统中的灵活资源,并总结了其调节特性。其次,介绍了权重分配、数理统计及包络区间3种常用电力系统灵活性评价指标,并总结了不同方法的适用性。然后,总结了模型驱动或数据驱动的应用现状及其各自优缺点,并对模型数据交互驱动的研究现状进行了概述。最后,对考虑灵活资源的电力系统调度方案研究进行了展望。

锂离子电池健康状态估计及寿命预测研究进展综述

随着锂离子电池的应用越来越广泛,锂电池健康状态的精确估计和剩余寿命的实时预测对于锂电池系统的安全运行和降低运维成本具有重要意义。锂电池内部复杂的物理化学反应和外部复杂工作条件,使得实现精准的健康状态估计和寿命预测具有挑战性。该文综述近年来锂电池健康状态估计和剩余使用寿命预测方法的研究现状,分析基于物理/数学模型、数据驱动、模型法和数据驱动融合,以及多种数据驱动融合的锂电池健康状态估计方法的优缺点及适用条件,并对比分析不同数据驱动类型的锂电池寿命预测方法。指出锂电池健康状态估计及寿命预测尚存在的问题,并对未来研究方向进行展望,对完善锂电池健康状态估计和寿命预测算法理论体系、指导实际应用技术具有重要意义。

以新质生产力推动区域高质量发展

发展新质生产力是新时代新征程推动高质量发展的内在要求和重要着力点,我国区域之间异质性强,区域发展差异大,要以发展新质生产力为重大契机,深入实施区域协调发展战略、区域重大战略、主体功能区战略、新型城镇化战略,优化重大生产力布局,推动区域创新中心与区域产业体系深度融合发展,加强区域间协同融合,提高战略取向一致性,促进不同区域在发展新质生产力上形成合力,不断将我国区域空间回旋余地大的优势转化为新时代高质量发展的优势,加快形成多极点支撑、多层次联动、网络化发展的区域经济格局,提高我国经济发展韧性和高质量发展的可持续性,为中国式现代化和中华民族伟大复兴提供强有力的支撑。

基于数据驱动的分布式光伏发电功率预测方法研究进展

从综述的角度,以分布式光伏系统为对象,分析了功率预测技术的发展情况、存在的难点以及主要影响因素,梳理了应用数据驱动方法实现功率准确预测的技术路线。再以空间相关性、历史出力功率以及气象等影响因素为切入点,梳理了光伏系统数据驱动的功率预测研究现状,分析其相应的数据增强、时空图信息以及特征融合的手段,讨论了技术的优缺点。最后给出了功率预测数据驱动方法研究方向和发展建议。

支撑新型配电网数字化规划的图形⁃模型⁃数据融合关键技术

配电网规划领域期盼实现智能规划,其愿景在于实现无人或少人干预的全自动规划。在数字化转型的背景下,新型配电网规划将面临图形多样化、场景碎片化、数据规模化三大挑战。文中从图形-模型-数据融合的角度提出三大关键技术:基于电气图纸识别和拓扑智能分析的图形-模型融合技术、基于知识驱动的负荷/新能源推演分析和智能决策的模型-数据融合技术、基于多模态数据融合和多时空数据联动的图形-数据融合技术,尝试打破理论研究与数字化工程的壁垒。最后,对未来新型配电网数字化规划的发展进行思考和展望,为实现“以机为主,人机协同”的大闭环模式提供借鉴。

“互联网+”背景下“模拟电子技术实验”教学

针对传统“模拟电子技术实验”课程教学过程中存在的问题,在“互联网+”背景下,采用“三位一体”混合式教学模式进行教学改革。教学设计过程中通过任务驱动教学法,以实验任务为载体,进行“三位一体”混合式教学。该教学模式整合SPOC在线平台、虚拟仿真平台和便携式实验微平台的教学资源,构建学生“自主学习、自主实践、自主创新”的实验教学平台,体现了“互联网+教育”思想在“模拟电子技术实验”教学中的应用。

基于神经算子与类物理信息神经网络智能求解新进展

深度学习通过多层神经网络对数据进行学习,不仅能揭示潜藏信息,还能很好地解决复杂非线性问题.偏微分方程(PDE)是描述自然界中许多物理现象的基本数学模型.两者的碰撞与融合,产生了基于深度学习的PDE智能求解方法,它具有高效、灵活和通用等优点.文章聚焦PDE智能求解方法,以是否求解单一问题为判定依据,把求解方法分为两类:神经算子方法和类物理信息神经网络(PINN)方法,其中神经算子方法用于求解一类具有相同数学特征的PDE问题,类PINN方法用于求解单一问题.对于神经算子方法,从数据驱动和物理约束两个方面展开介绍,分析研究现状并指出现有方法的不足.对于类PINN方法,首先介绍了基础PINN的3种改进方法 (基于数据优化、基于模型优化和基于领域知识优化),然后详细介绍了基于物理驱动的两类解决方案:基于传统PDE离散方程的智能求解方案和无网格的非离散求解方案.最后总结技术路线,探讨现有研究存在的不足,给出可行的研究方案.最后,简要介绍智能求解程序发展现状,并对未来研究方向给出建议.

小样本学习技术在新型电力系统中的应用与挑战

数据驱动已成为新型电力系统建设及其数字化转型的核心范式,相关算法在负荷预测、状态检修、多主体调控等多项业务中展现出优越的工程效果与应用潜力。然而,实际工程数据往往面临着样本不足、样本不平衡等问题,制约了数据驱动算法的最终效果。因此,需要借助小样本学习来应对这一挑战。文中从数据、特征、模型3个层面探究了小样本学习技术,综述并分析了相关技术在场景生成、故障诊断、电力系统暂态稳定评估等业务的应用现状,并进一步指出小样本学习技术在新型电力系统中所面临的不足与挑战。