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.

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).

大数据驱动的企业与用户互动创新:理论框架与前沿课题

大数据驱动的企业与用户互动创新已成为企业洞察市场和用户动态、提升创新敏捷性和适应性的重要模式,其关键因素和作用机制日益成为创新、营销和信息系统等领域的热点议题。结合系统性综述设计和关键词共现分析,梳理大数据情境下企业与用户互动创新的研究洞见。针对情境和工具如何影响领先用户与普通用户关系及二者的相互作用机制两个研究缺口,提出大数据驱动的企业与用户互动创新“条件—路径—绩效”理论框架。依据该理论框架,结合文献和案例证据,重点阐述在什么情境和工具条件下企业与领先用户、普通用户以何种互动创新路径影响创新产品和大数据合作资产,由此形成理论创新。分析结果表明,互动创新路径选择本质是在异质性情境和工具条件下,企业在创新新颖性—泛用性以及数据分析成本—收益之间的动态权衡;两类创新路径的动态变化由互动创新工具成熟度、创新主导权的变更所触发。最后,进一步提炼出大数据驱动的企业与用户互动创新理论研究的三类前沿课题,并讨论其理论和实践意义。

基础教育资源配置大数据分析平台的整体架构与运行保障

教育大数据是反映基础教育资源配置现状、需求和问题的数据集合,也是重塑基础教育资源配置结构、构建良好教育生态的有效工具。教育大数据能够为资源精准配置提供客观依据、助力理性决策、拓展实践路径、提供监测反馈,可从数据资源层、处理层、分析层和可视化层,整体架构基础教育资源配置大数据分析平台,实现多学段基础教育数据精准采集、多模态基础教育数据分类储存、多因素基础教育数据指标计量、多维度基础教育数据结果呈现。基础教育资源配置大数据分析平台顺利运行的保障措施主要包括:政策跟进,赋予数据驱动合法地位;强化治理,保障平台教育数据质量;注重成效,发挥数据分析平台价值;多元联动,助力数据分析平台共建。通过这些措施,努力实现数据驱动基础教育资源优化配置,促进决策科学化、供给精准化、评估动态化。

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.

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.

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。

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。

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。

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。

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。

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。

农业知识驱动服务技术革新综述与前沿

农业知识驱动服务技术是指运用先进信息技术,科学、高效调配农业领域专业知识服务资源,为农业行业提供智能化知识服务的技术,在解决农业技术服务供需严重失衡等难点问题方面具有重要意义,日益成为支撑农业转型升级和高质量发展的重要引擎,代表着核心研究方向,伴随着技术发展全过程。目前农业行业迫切需要解决的是知识供给严重不足、服务效率不高的问题。农业知识驱动服务技术经历较长时间发展,在知识高效匹配和精准供给方面取得了较大进步,特别是2022年11月以来ChatGPT这类技术的出现,充分展现了超大规模预训练模型在知识智能服务方面的巨大潜力,这也是农业知识驱动服务可以取得突破的关键所在,可以在这方面发挥重要作用。该文在分析农业知识驱动服务相关技术现状的基础上,展望了农业领域可行的知识驱动服务技术路径,预测农业领域知识服务大模型研发构建会呈现参数由少到多、算力由弱趋强、强化训练逐渐加深的特点得到快速发展应用,未来将在专业技术指导、农业“装备-信息-农艺”融合、农业信息系统平台服务总线等方面系统升级现有农业知识服务范式,多模态服务将得到系统融合加深,人机交互模式将向“人性化”方向进一步黏合增强,从而为农业智能化转型升级提供全新的技术支撑,引领农业知识服务从数据检索、语义匹配迈向生成式知识驱动模式转变。

基于历史唯物主义视域的新质生产力:内涵、形成条件与有效路径

习近平总书记提出新质生产力这一概念,不仅对东北全面振兴具有重要指导意义,对我国实现经济高质量发展和中国式现代化也具有重大战略意义。新质生产力是由于生产力构成要素的质的不断提升而呈现出来的更为先进的生产力形式。新质生产力在本质上属于马克思主义的生产力范畴,具有实践性、全面性、发展性的性质。新质生产力外显于生产力的构成要素上,这些构成要素质量的提升即“劳动者”素质的持续提升、劳动资料的改进与广泛应用、劳动对象的不断扩张、科学技术的突飞猛进、管理水平的显著提升等,形成了孕育新质生产力的条件。依据新质生产力的形成条件,新质生产力的形成路径主要有:一是通过提升教育质量、培育世界一流企业、促进产业发展以培育高质量的“劳动者”;二是深入实施创新驱动发展战略以提升劳动资料及科学技术这两个要素的质量;三是建设超大规模的全国统一大市场以实现劳动对象的扩展;四是加强党的领导与培育企业家精神以实现企业管理的变革。

集成学习框架下的车辆跟驰行为建模

为了提高复杂行驶环境下车辆跟驰行为预测精度,提出了一种集成学习框架下融合理论驱动模型和数据驱动模型的车辆跟驰行为建模方法。基于stacking集成学习框架,选择理论驱动的智能驾驶模型(IDM)、考虑车辆队列和周围行驶条件因素的数据驱动的长短时记忆(LSTM)网络和门控循环单元(GRU)网络作为跟驰行为特征的一级学习算法,选择3种线性和8种非线性回归方法作为备选二级学习算法来融合一级学习器的输出特征。通过对比使用实际车辆轨迹数据计算的模型预测精度,确定了最优模型。研究结果表明:包含车辆队列和周围行驶条件变量的数据驱动跟驰模型比IDM模型的预测精度更高;多数情况下采用非线性二级学习算法的融合跟驰模型的预测精度高于IDM模型、数据驱动跟驰模型以及采用线性二级学习算法的融合跟驰模型;分别采用GBRT回归和随机森林回归作为二级学习算法的IDM-LSTM-stacking模型和IDM-GRU-stacking模型具有最高的预测精度;外界干扰下的融合跟驰模型稳定性优于单一的理论和数据驱动跟驰模型。集成学习为驾驶行为建模提供了新方法。