基于区间毕达哥拉斯犹豫模糊熵和交叉熵的ELECTRE II决策方法

针对属性权重完全未知或部分已知,属性值为区间毕达哥拉斯犹豫模糊数的多属性决策问题,提出了基于区间毕达哥拉斯犹豫模糊熵和交叉熵的ELECTRE II法。首先给出区间毕达哥拉斯犹豫模糊数的区间形式的得分函数和精度函数,定义新的距离测度。然后基于区间毕达哥拉斯犹豫模糊数的模糊因子、直觉因子和幅度因子,给出熵和交叉熵公式,并证明其性质,提出了基于熵和交叉熵确定属性权重的方法。最后提出了区间毕达哥拉斯犹豫模糊环境下的改进的ELECTRE II法,利用综合优势值对方案进行排序,并通过算例和比较分析验证了该方法的可行性和有效性。

SOLIDWORKS Electrical在通过式脚踏封口机自动化改造设计中的应用

小型企业塑封工艺中通过式脚踏封口机应用广泛,但在塑封重型工件时,工人操作非常费力且次品率高,无法保障塑封质量,为此对其进行机构优化与软硬件设计自动化改造。利用SOLIDWORKS Electrical快速建立3D虚拟电气配盘与生成各类BOM清单,大大缩短了研发周期,安装人员还可以根据虚拟电气装配路径进行准确安装。最终设计出结构合理,满足生产工艺要求的自动化装置,企业以极少改造成本提质增效,为机电一体化装置自动化设计提供了一个高效开发途径。

Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

A Novel Multiple DBC-staked units Package to Parallel More Chips for SiC Power Module

Silicon carbide(SiC) power modules play an essential role in the electric vehicle drive system. To improve their performance, reduce their size, and increase production efficiency, this paper proposes a multiple staked direct bonded copper(DBC) unit based power module packaging method to parallel more chips. This method utilizes mutual inductance cancellation effect to reduce parasitic inductance. Because the conduction area in the new package is doubled, the overall area of power module can be reduced. Entire power module is divided into smaller units to enhance manufacture yield, and improve design freedom. This paper provides a detailed design, analysis and fabrication procedure for the proposed package structure. Additionally, this paper offers several feasible solutions for the connection between power terminals and DBC untis. With the structure, 18dies were paralleled for each phase-leg in a econodual size power module. Both simulation and double pulse test results demonstrate that, compared to conventional layouts, the proposed package method has 74.8% smaller parasitic inductance and 34.9% lower footprint.

Optimal transcorneal electrical stimulation parameters for preserving photoreceptors in a mouse model of retinitis pigmentosa

Retinitis pigmentosa is a hereditary retinal disease that affects rod and cone photoreceptors,leading to progressive photoreceptor loss.Previous research supports the beneficial effect of electrical stimulation on photoreceptor survival.This study aims to identify the most effective electrical stimulation parameters and functional advantages of transcorneal electrical stimulation(tcES)in mice affected by inherited retinal degeneration.Additionally,the study seeked to analyze the electric field that reaches the retina in both eyes in mice and post-mortem humans.In this study,we recorded waveforms and voltages directed to the retina during transcorneal electrical stimulation in C57BL/6J mice using an intraocular needle probe with rectangular,sine,and ramp waveforms.To investigate the functional effects of electrical stimulation on photoreceptors,we used human retinal explant cultures and rhodopsin knockout(Rho^(-/-))mice,demonstrating progressive photoreceptor degeneration with age.Human retinal explants isolated from the donors’eyes were then subjected to electrical stimulation and cultured for 48 hours to simulate the neurodegenerative environment in vitro.Photoreceptor density was evaluated by rhodopsin immunolabeling.In vivo Rho^(-/-)mice were subjected to two 5-day series of daily transcorneal electrical stimulation using rectangular and ramp waveforms.Retinal function and visual perception of mice were evaluated by electroretinography and optomotor response(OMR),respectively.Immunolabeling was used to assess the morphological and biochemical changes of the photoreceptor and bipolar cells in mouse retinas.Oscilloscope recordings indicated effective delivery of rectangular,sine,and ramp waveforms to the retina by transcorneal electrical stimulation,of which the ramp waveform required the lowest voltage.Evaluation of the total conductive resistance of the post-mortem human compared to the mouse eyes indicated higher cornea-to-retina resistance in human eyes.The temperature recordings during and after electrical stimulation indicated no significant temperature change in vivo and only a subtle temperature increase in vitro(~0.5-1.5°C).Electrical stimulation increased photoreceptor survival in human retinal explant cultures,particularly at the ramp waveform.Transcorneal electrical stimulation(rectangular+ramp)waveforms significantly improved the survival and function of S and M-cones and enhanced visual acuity based on the optomotor response results.Histology and immunolabeling demonstrated increased photoreceptor survival,improved outer nuclear layer thickness,and increased bipolar cell sprouting in Rho^(-/-)mice.These results indicate that transcorneal electrical stimulation effectively delivers the electrical field to the retina,improves photoreceptor survival in both human and mouse retinas,and increases visual function in Rho^(-/-)mice.Combined rectangular and ramp waveform stimulation can promote photoreceptor survival in a minimally invasive fashion.

Millimetric devices for nerve stimulation:a promising path towards miniaturization

Nerve stimulation is a rapidly developing field,demonstrating positive outcomes across several conditions.Despite potential benefits,current nerve stimulation devices are large,complicated,and are powered via implanted pulse generators.These facto rs necessitate invasive surgical implantation and limit potential applications.Reducing nerve stimulation devices to millimetric sizes would make these interventions less invasive and facilitate broader therapeutic applications.However,device miniaturization presents a serious engineering challenge.This review presents significant advancements from several groups that have overcome this challenge and developed millimetricsized nerve stimulation devices.These are based on antennas,mini-coils,magneto-electric and optoelectronic materials,or receive ultrasound power.We highlight key design elements,findings from pilot studies,and present several considerations for future applications of these devices.

Electronic properties of 2D materials and their junctions

With an extensive range of distinctive features at nano meter-scale thicknesses,two-dimensional(2D)materials drawn the attention of the scientific community.Despite tremendous advancements in exploratory research on 2D materials,knowledge of 2D electrical transport and carrier dynamics still in its infancy.Thus,here we highlighted the electrical characteristics of 2D materials with electronic band structure,electronic transport,dielectric constant,carriers mobility.The atomic thinness of 2D materials makes substantially scaled field-effect transistors(FETs)with reduced short-channel effects conceivable,even though strong carrier mobility required for high performance,low-voltage device operations.We also discussed here about factors affecting 2D materials which easily enhanced the activity of those materials for various applications.Presently,Those 2D materials used in state-of-the-art electrical and optoelectronic devices because of the extensive nature of their electronic band structure.2D materials offer unprecedented freedom for the design of novel p-n junction device topologies in contrast to conventional bulk semiconductors.We also,describe the numerous 2D p-n junctions,such as homo junction and hetero junction including mixed dimensional junctions.Finally,we talked about the problems and potential for the future.

Preliminary study of the electrospray DPE peculiarities from the liquid surface in the presence of the CSWs

The Ultrasonic Electric Propulsion(UEP)system is a cutting-edge propulsion technology that is mostly used on platforms for small satellites(less than 10 kg).The characteristics of droplet partial emissions(DPEs)in the UEP system are investigated using a high-speed imaging technique(an ultra-high speed camera(NAC HX-6)and a long-distance microscope)in this work.The experiments demonstrate that there are a few partial emission modes,including left-side emission,double-side emission,and right-side emission,that are present in the droplet emission process of the UEP system.These modes are primarily caused by the partial formation of capillary standing waves(CSWs)on the emission surface of the ultrasonic nozzle.The emission rate for single-and double-sided emissions varies at different times,indicating that there are different CSWs engaged in droplet emission due to variations in the liquid film thickness and charge state of the liquid cones.Additionally,as the droplets emit continuously,a raised area on the emission surface appears,with several droplets emitting there as a result of charge accumulation.Additionally,photos of the CSWs with emitting droplets are obtained,which highlights the CSWs'distinctive wave morphology.

A new electric field mill array with each of the mill’s rotor controlled precisely by a GPS module:Equipment and initial results

We have newly designed an electrostatic sensor,called an electric field mill(EFM),to simplify the estimation of the charge position and charge amount transferred by lightning discharges.It is necessary for this remote estimation of the transferred charge to measure electric field changes caused by charge loss at the time of a lightning strike at multiple locations.For multiple-station measurement of electric field changes,not only speed but also phase for exposure and shielding of the sensing plates inside each EFM of the array should be synchronized to maintain the sensitivities of the deployed instruments.Currently,there is no such EFM with specified speed and phase control performance of the rotary part.Thus,we developed a new EFM in which the rotary mechanism was controlled consistently to within 3%error by a GPS module.Five EFMs had been distributed in the Hokuriku area of Japan during the winter season of 2022-2023 for a test observation.Here we describe the design and a simple calibration method for our new EFM array.Data analysis method based on the assumption of a simple monopole charge structure is also summarized.For validation,locations of assumed point charges were compared with three-dimensional lightning mapping data estimated by radio observations in the MF-HF bands.Initial results indicated the validity to estimate transferred charge amounts and positions of winter cloud-to-ground lightning discharges with our new EFM array.

Anisotropic metal–insulator transition in strained VO_(2)(B) single crystal

Mechanical strain can induce noteworthy structural and electronic changes in vanadium dioxide, imparting substantial scientific importance to both the exploration of phase transitions and the development of potential technological applications. Unlike the traditional rutile(R) phase, bronze-phase vanadium dioxide [VO_(2)(B)] exhibits an in-plane anisotropic structure. When subjected to stretching along distinct crystallographic axes, VO_(2)(B) may further manifest the axial dependence in lattice–electron interactions, which is beneficial for gaining insights into the anisotropy of electronic transport.Here, we report an anisotropic room-temperature metal–insulator transition in single-crystal VO_(2)(B) by applying in-situ uniaxial tensile strain. This material exhibits significantly different electromechanical responses along two anisotropic axes.We reveal that such an anisotropic electromechanical response mainly arises from the preferential arrangement of a straininduced unidirectional stripe state in the conductive channel. This insulating stripe state could be attributed to the in-plane dimerization within the distorted zigzag chains of vanadium atoms, evidenced by strain-modulated Raman spectra. Our work may open up a promising avenue for exploiting the anisotropy of metal–insulator transition in vanadium dioxide for potential technological applications.

概率不确定下考虑偏好的ELECTREⅢ方法研究及应用

针对概率不确定语言环境下的多属性群决策问题,为了更好地反映多位专家的评价信息对于不同属性的偏好程度,创新性地构建了一个新的属性偏好关系。基于考虑专家评价信息之间的差异程度,提出了差异度的概念与计算方法。首先,对多个专家的评价信息进行汇总,通过计算不同方案之间与不同属性之间的差异度,进而获得所有专家对于每个属性的偏好程度。其次,文章研究了在概率不确定语言环境下解决多属性群决策问题的ELECTREⅢ方法,通过计算方案之间的一致性指数与不一致性指数,构造方案间的级别优先关系,从而对备选方案进行综合排序。最后,通过绿色供应链供应商选择的算例分析及概率不确定语言环境下的其他方法进行比较分析,验证了该环境下ELECTREⅢ方法的适用性与可行性。

Patterning single-layer materials by electrical breakdown using atomic force microscopy

The development of nanoelectronics and nanotechnologies has been boosted significantly by the emergence of 2D materials because of their atomic thickness and peculiar properties,and developing a universal,precise patterning technology for single-layer 2D materials is critical for assembling nanodevices.Demonstrated here is a nanomachining technique using electrical breakdown by an AFM tip to fabricate nanopores,nanostrips,and other nanostructures on demand.This can be achieved by voltage scanning or applying a constant voltage while moving the tip.By measuring the electrical current,the formation process on single-layer materials was shown quantitatively.The present results provide evidence of successful pattern fabrication on single-layer MoS2,boron nitride,and graphene,although further confirmation is still needed.The proposed method holds promise as a general nanomachining technology for the future.

Achievable hole concentration at room temperature as a function of Mg concentration for MOCVD-grown p-GaN after sufficient annealing

Relationship between the hole concentration at room temperature and the Mg doping concentration in p-GaN grown by MOCVD after sufficient annealing was studied in this paper.Different annealing conditions were applied to obtain sufficient activation for p-GaN samples with different Mg doping ranges.Hole concentration,resistivity and mobility were characterized by room-temperature Hall measurements.The Mg doping concentration and the residual impurities such as H,C,O and Si were measured by secondary ion mass spectroscopy,confirming negligible compensations by the impurities.The hole concentration,resistivity and mobility data are presented as a function of Mg concentration,and are compared with literature data.The appropriate curve relating the Mg doping concentration to the hole concentration is derived using a charge neutrality equation and the ionized-acceptor-density[N-(A)^(-)](cm^(−3))dependent ionization energy of Mg acceptor was determined asE_(A)^(Mg)=184−2.66×10^(−5)×[N_(A)^(-)]1/3 meV.

Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

Structural Transformations and Multiferroic Properties of Bi_(1-x)Eu_(x)Fe_(0.95)Co_(0.05)O_(3)(x=0.05,0.10,0.15,and 0.20)

Bi_(1-x)Eu_(x)Fe_(0.95)Co_(0.05)O_(3 )(x=0.05,0.10,0.15,and 0.20) nanoparticles were prepared through the sol-gel technique.Its structure,local electronic structure,magnetic and electric properties were systematically investigated.X-ray diffraction data show(104),(110) bimodal alignment and high angular migration,indicating that with the increase of Eu substitution at Bi site,the structure of BFO undergoes a continuous change in crystal structure.The hysteresis loop and the FC/ZFC curve show how magnetism varies with the size of the field and temperature.Finally,the causes of magnetic changes were analyzed by studying SXAS and hysteresis loops.

Innovation and Firm Co-ownership Network in China’s Electric Vehicle Industry

Firms are embedded in complex networks,where diverse ideas combine and generate new ideas.Shareholders of firms are of-ten seen as critical external resources that have significant influence on firm innovation.The current literature tends to focus on the rela-tionship between firms and their shareholders,while paying less attention to the connections between firms with the same shareholders.This article identifies two types of network spillover effects,intra-city network effect and inter-city network effect,by visualizing the co-ownership networks in China’s electric vehicle(EV)industry.We find that firms with the same shareholders,which are defined as co-owned EV firms,are more innovative than non-co-owned ones.Furthermore,there are two dominant types of firm co-ownership ties formed by corporate and financial institution shareholders.While corporate shareholders help exploiting local tacit knowledge,financial institutions are more active in bridging inter-city connections.The conclusion is confirmed at both firm and city levels.This paper theor-izes the firm co-ownership network as a new form of institutional proximity and tested the result empirically.For policy consideration,we have emphasized the importance of building formal or informal inter-firm network,and the government should further enhance the knowledge flow channel by institutional construction.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Coexisting fast–slow dendritic traveling waves in a 3D-array electric field coupled neuronal network

Coexistence of fast and slow traveling waves without synaptic transmission has been found in hhhippocampal tissues,which is closely related to both normal brain activity and abnormal neural activity such as epileptic discharge. However, the propagation mechanism behind this coexistence phenomenon remains unclear. In this paper, a three-dimensional electric field coupled hippocampal neural network is established to investigate generation of coexisting spontaneous fast and slow traveling waves. This model captures two types of dendritic traveling waves propagating in both transverse and longitude directions: the N-methyl-D-aspartate(NMDA)-dependent wave with a speed of about 0.1 m/s and the Ca-dependent wave with a speed of about 0.009 m/s. These traveling waves are synaptic-independent and could be conducted only by the electric fields generated by neighboring neurons, which are basically consistent with the in vitro data measured experiments. It is also found that the slow Ca wave could trigger generation of fast NMDA waves in the propagation path of slow waves whereas fast NMDA waves cannot affect the propagation of slow Ca waves. These results suggest that dendritic Ca waves could acted as the source of the coexistence fast and slow waves. Furthermore, we also confirm the impact of cellular spacing heterogeneity on the onset of coexisting fast and slow waves. The local region with decreasing distances among neighbor neurons is more liable to promote the onset of spontaneous slow waves which, as sources, excite propagation of fast waves. These modeling studies provide possible biophysical mechanisms underlying the neural dynamics of spontaneous traveling waves in brain tissues.

A Special Issue“Planning and Optimal Operation of New-Type Power System”of Global Energy Interconnection

The power system,as an energy hub,plays a crucial role in the transformation of energy production and consumption.On July 19,2023,the International Energy Agency(IEA)released a Global Electricity Market Report for 2023-2024.This report indicates that the development of the world’s energy production is rapidly moving towards the critical point where the proportion of electricity generated from renewable sources surpasses that from non-renewable sources.

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.