Manipulating the expression of synaptic plasticity in neuromorphic devices provides essential foundations for developing intelligent,adaptive hardware systems.In recent years,advances have shifted from static emulatio...Manipulating the expression of synaptic plasticity in neuromorphic devices provides essential foundations for developing intelligent,adaptive hardware systems.In recent years,advances have shifted from static emulation toward dynamic,network-oriented plasticity design,offering enhanced computational accuracy and functional relevance.This review highlights how diversified plasticity behaviors,including multilevel long-term potentiation and depression for spatial models,tunable short-term memory for temporal models,as well as wavelength-selective response,excitatory and inhibitory synergy,and adaptive threshold modulation,collectively support key tasks such as stable learning,temporal processing,and context-aware adaptation.Beyond behavioral innovations,strategies such as multifunctional single-device integration,multimodal fusion,and heterogeneous system assembly enable compact,energy-efficient,and versatile neuromorphic architectures.Recent developments at the array level further demonstrate high-performance scalability and system-level applicability.Despite notable progress,current modulation strategies remain constrained in flexibility,diversity,and large-scale coordination.Future research should focus on enriching the behavioral repertoire of plasticity,advancing crossmodal convergence,and improving array-level uniformity,paving the way toward deployable,high-efficiency neuromorphic intelligence.展开更多
The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who ha...The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem;it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.展开更多
The project analyzes the student＇s participation in complementary activities such as Education Tutorial Program, Junior Company and Academic Center. These organizations have the goal to improve, expand and connect kn...The project analyzes the student＇s participation in complementary activities such as Education Tutorial Program, Junior Company and Academic Center. These organizations have the goal to improve, expand and connect knowledge learned during classes with several practical activities. They can provide a huge integration between the students and the professors in order to achieve better results in the pedagogical, structural and organizational parts of an engineering major degree. Therefore, the project goes through the impact of each entity in the student＇s life and the advantages to professional future, focusing the presence of these organizations in the Electrical Engineering Course of Federal University of Rio Grande do Norte.展开更多
To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated ...To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated salt composite phase change material(HSCPCM)with dual phase transition temperature zones has been proposed.This HSCPCM,denoted as SDMA10,combines hydrophilic modified expanded graphite,an acrylic emulsion coating,and eutectic hydrated salts to achieve leakage prevention,enhanced thermal stability,cycling stability,and superior phase change behavior.Battery modules incorporating SDMA10 demonstrate significant thermal control capabilities.Specifically,the cylindrical battery modules with SDMA10 can maintain maximum operating temperatures below 55°C at 4 C discharge rate,while prismatic battery modules can keep maximum operating temperatures below 65°C at 2 C discharge rate.In extreme battery overheating conditions simulated using heating plates,SDMA10 effectively suppresses thermal propagation.Even when the central heating plate reaches 300°C,the maximum temperature at the module edge heating plates remains below 85°C.Further,compared to organic composite phase change materials(CPCMs),the battery module with SDMA10 can further reduce the peak thermal runaway temperature by 93°C and delay the thermal runaway trigger time by 689 s,thereby significantly decreasing heat diffusion.Therefore,the designed HSCPCM integrates excellent latent heat storage and thermochemical storage capabilities,providing high thermal energy storage density within the thermal management and thermal runaway threshold temperature range.This research will offer a promising pathway for improving the thermal safety performance of battery packs in electric vehicles and other energy storage systems.展开更多
Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to t...Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to ten times more parameters than traditional sensors,making selection a challenging task even for experts.This study addresses this challenge,focusing on defensive guidance,navigation,and control(GNC)systems where precise and reliable angular velocity measurement is critical to overall performance.A comprehensive mathematical model is introduced to encapsulate all key MEMS parameters,accompanied by discussions on calibration and Allan variance interpretation.For six leading MEMS gyroscope applications,namely inertial navigation,integrated navigation,autopilot systems,rotating projectiles,homing guidance,and north finding,the most critical parameters are identified,distinguishing suitable and unsuitable sensor choices.Special emphasis is placed on inertial navigation systems,where practical rules of thumb for error evaluation are derived using six degrees of freedom motion equations.Rigorous simulations demonstrate the influence of various sensor parameters through real-world case studies,including static navigation,multi-rotor attitude estimation,gimbal stabilization,and north finding via a turntable.This work aims to be a beacon for practitioners across diverse fields,empowering them to make more informed design decisions.展开更多
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−freque...Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−frequency arc discharge experiments encounter limitations in isolating pressure wave characteristics due to persistent gas generation and arc reignition.To circumvent these challenges,an oil-immersed impulse voltage discharge platform was conceived and engineered to investigate pressure wave propagation dynamics.A pressure numerical simulation model and theoretical model of oil−solid interface reflection and refraction were subsequently established to elucidate the pressure propagation mechanism.The experimental and simulation results show that the pressure wave generated by pulsed arc discharge in oil propagates radially in the form of spherical waves.Due to the viscous loss and wave front expansion of transformer oil,the peak pressure decays exponentially with distance,with a decay coefficientβ=1.15.When pressure waves encounter metal obstacles inside transformer oil,there are two propagation paths:direct transmission through and multiple reflections through,and a mode transformation of pressure waves occurs at the oil−solid interface,mainly propagating through obstacles in the form of transverse waves.This work quantitatively delineates the energy pressure wave coupling,propagation dynamics,and attenuation mechanisms,providing critical insights for assessing and mitigating arc fault-induced transformer explosion risks.展开更多
This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg gam...This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg game is utilized to model the P2P energy trading process.Stackelberg equilibrium(SE)is regarded as a P2P optimal trading strategy.A two-stage privacy protection solution technique combining data-driven and model-driven is developed to obtain the SE.Specifically,energy storage scheduling problem in MGs is formulated as a Markov decision process with discrete periods,and a multi-action single-observation deep deterministic policy gradient(MASO-DDPG)algorithm is proposed to tackle optimal scheduling of energy storage in the first stage.According to optimal scheduling of energy storage,the closed-form expression for SE based on model-driven is derived,and distributed SE solution technique(DSET)is developed to obtain SE in the second stage.Case studies involving a 4-Microgrid demonstrate the P2P electricity price obtained by the two-stage method,as a novel pricing mechanism,can reasonably regulate microgrid operation mode and improve microgrid income participating in the P2P market,which verifies effectiveness and superiority of the proposed P2P energy trading model and two-stage solution method.展开更多
Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat...Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.展开更多
A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing t...A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing to their partial coupling with the grid,are particularly vulnerable to voltage dips and excessive reactive power absorption during fault events.This study proposes an adaptive control strategy based on Model Reference Adaptive Control integrated with stator flux-oriented vector control to regulate active and reactive power of a DFIG-based wind farm connected to a standard IEEE 9-bus power system under fault conditions.The proposed control scheme is developed and validated using detailed MATLAB/Simulink modeling under normal operation,symmetrical three-phase fault conditions,and post-fault recovery scenarios.A three-phase-to-ground fault is applied at the wind farm interconnection bus for a duration of 150 ms to evaluate transient performance.Simulation results demonstrate that the adaptive controller ensures fast power tracking,effective reactive power support,and enhanced voltage recovery compared to a conventional proportional–integral controller.Quantitatively,the proposed method improves voltage recovery time by approximately 45%,reduces active power overshoot by 38%,and lowers total harmonic distortion by 52% following fault clearance.Furthermore,the adaptive controller maintains stable operation under variations in wind speed and machine parameters without requiring retuning,highlighting its robustness against system uncertainties.The results confirm that the proposed control strategy significantly enhances fault ride-through capability,power quality,and dynamic stability of grid-interfaced wind farms.These findings demonstrate the practical applicability of adaptive control techniques for improving the reliability and resilience of modern power systems with high wind energy penetration.展开更多
With the increasing penetration of variable renewable energy,flexible resources are highly needed to hedge the growing uncertainty,and variability in the power system.Demand response has served as a cost-effective typ...With the increasing penetration of variable renewable energy,flexible resources are highly needed to hedge the growing uncertainty,and variability in the power system.Demand response has served as a cost-effective type of flexible resource in recent years.In order to balance the uncertainty of the system,it is crucial to assess how much flexibility demand response programs can provide.Thus,forecasting demand response potential is important for the operation of the bulk system.This paper proposes a modeling approach that can characterize the multi-timescale flexibility of demand response so that not only the power potential but also temporal-coupling characteristics can be considered.Furthermore,a day-ahead demand response potential forecasting method is proposed using deep convolutional generative adversarial networks.The proposed forecasting method is tested using data from 170 users in Pecan Street Dataport.The results show that the proposed method can forecast the multi-timescale flexibility of demand response with high accuracy.展开更多
Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)...Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)control strategy based on a power outer loop and voltage-current inner loops,aiming to enhance the stability and current-limiting capability of GFM converters during grid fault conditions.During voltage sags,the GFM converter’s voltage source behavior is maintained by dynamically adjusting the reactive power reference to provide voltage support,thereby effectively suppressing the steady-state component of the fault current.To address the active power imbalance induced by voltage sags,a dynamic active power reference correction method based on apparent power is designed to mitigate power angle oscillations and limit transient current.Moreover,an adaptive virtual impedance loop is implemented to enhance dynamic transient current-limiting performance during the fault initiation phase.This approach improves the responsiveness of the inner loop and ensures safe system operation under various fault severities.Under asymmetric fault conditions,a negative-sequence reactive current compensation strategy is incorporated to further suppress negative-sequence voltage and improve voltage symmetry.The proposed control scheme enables coordinated operation of multiple control objectives,including voltage support,current suppression,and power angle stability,across different fault scenarios.Finally,MATLAB/Simulink simulation results validate the effectiveness of the proposed strategy,showcasing its superior performance in current limiting and power angle stability,thereby significantly enhancing the system’s fault ride-through capability.展开更多
Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhance...Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhancement characteristics of IGCTs,HCCs demonstrate superior performance at reduced extinction angles,thereby minimizing reactive power consumption.This study presents a comprehensive investigation into reactive power control strategies for HCCs operating at small extinction angles.First,the topological configuration and commutation principle of HCC are elucidated.Subsequently,the mechanism of HCC reactive power control is analyzed,and a reactive power control strategy is proposed by combining the converter transformer taps with extinction angles.Moreover,the relationship between transformer taps and reactive power exchange under different rated extinction angles is calculated,and the theoretically rated extinction angle is proposed.Finally,to validate the proposed control strategy,a four-terminal ultra-high voltage direct current power grid incorporating HCC technology is modeled and sim-ulated using PSCAD/EMTDC.The simulation results demonstrate that the proposed strategy effectively supports AC systems by reducing reactive power absorption in HCCs,while simultaneously exhibiting enhanced reli-ability and economic efficiency.展开更多
The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)h...The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.展开更多
Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transien...Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transient stability.This paper investigates a common countermeasure involving a PI-based DC voltage controller for GFM-PV systems,revealing that their small-signal stability is sensitive to parameter tuning.The study develops a generalized DC voltage-dominated 2nd-order GFM model and successfully conducts complex torque analysis,showing that this approach can be effectively extended to other dynamics governed by DC voltage-dominated GFM systems.Subsequently,the paper establishes a stability criterion for GFM-PV systems and proposes a parameter tuning method for DC voltage controllers that incorporates damping margin considerations.The performance of the tuned single-machine-infinite-bus GFM-PV system is validated on the RT-LAB real-time simulation platform under scenarios involving solar irradiance fluctuations and grid frequency disturbances.The proposed method proves effective in ensuring the stability of the GFM-PV system,with robust theoretical support.展开更多
Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc...Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.展开更多
For hybrid-electric unmanned aerial vehicles(UAVs),the stable power supply from the onboard permanent magnet synchronous generator(PMSG)is critical.Overheating in the confined compartment can directly lead to power in...For hybrid-electric unmanned aerial vehicles(UAVs),the stable power supply from the onboard permanent magnet synchronous generator(PMSG)is critical.Overheating in the confined compartment can directly lead to power interruption and system failure.Therefore,proactively improving the thermal management is not only a key technical prerequisite for ensuring flight reliability and mission success,but also enhances the machine’s efficiency and the overall power density of the system.Targeting the stringent spatial constraints in UAV applications,novel self-air-cooling heat dissipation topologies are investigated and highlighted on the rotor sidewall for compact outer-rotor generators.A systematic optimization framework,centered on a multi-objective genetic algorithm,is developed to Pareto-optimize the fin geometries,balancing thermal performance against aerodynamic penalty.The proposed topologies are innovatively deployed on the rotor sidewall,uniquely combining the structural space of an outer-rotor machine with self-air-cooling to generate directed airflow of varying patterns that directly enhance the cooling efficiency of the stator.The parameters of the designed self-air-cooled heat dissipation topologies are optimized via a multi-objective genetic algorithm.A temperature rise test under windless conditions shows that the proposed self-air-cooled structure reduces the stator temperature of the generator by 37.1℃at 5000 r/min,confirming the effectiveness and engineering feasibility for practical applications.展开更多
Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency devia...Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
基金funded by National Natural Science Foundation of China(No.62474144,62501489,T2222025,and 62174053)Basic Research Program of Jiangsu(No.BK20250465)+4 种基金Centrally Guided Local Science and Technology Development Fund(2024ZYZX4025)Suzhou Industrial Park MEMS Advanced High-Performance Sensor Chip Technology Transfer Platform(CXZ2024201)Jiangsu Data Science and Cognitive Computational Engineering Research CentreXJTLU AI University Research CentreXJTLU Advanced Materials Research Center。
文摘Manipulating the expression of synaptic plasticity in neuromorphic devices provides essential foundations for developing intelligent,adaptive hardware systems.In recent years,advances have shifted from static emulation toward dynamic,network-oriented plasticity design,offering enhanced computational accuracy and functional relevance.This review highlights how diversified plasticity behaviors,including multilevel long-term potentiation and depression for spatial models,tunable short-term memory for temporal models,as well as wavelength-selective response,excitatory and inhibitory synergy,and adaptive threshold modulation,collectively support key tasks such as stable learning,temporal processing,and context-aware adaptation.Beyond behavioral innovations,strategies such as multifunctional single-device integration,multimodal fusion,and heterogeneous system assembly enable compact,energy-efficient,and versatile neuromorphic architectures.Recent developments at the array level further demonstrate high-performance scalability and system-level applicability.Despite notable progress,current modulation strategies remain constrained in flexibility,diversity,and large-scale coordination.Future research should focus on enriching the behavioral repertoire of plasticity,advancing crossmodal convergence,and improving array-level uniformity,paving the way toward deployable,high-efficiency neuromorphic intelligence.
文摘The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem;it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.
文摘The project analyzes the student＇s participation in complementary activities such as Education Tutorial Program, Junior Company and Academic Center. These organizations have the goal to improve, expand and connect knowledge learned during classes with several practical activities. They can provide a huge integration between the students and the professors in order to achieve better results in the pedagogical, structural and organizational parts of an engineering major degree. Therefore, the project goes through the impact of each entity in the student＇s life and the advantages to professional future, focusing the presence of these organizations in the Electrical Engineering Course of Federal University of Rio Grande do Norte.
基金financially supported by Natural Science Foundation of Guangdong province(2024A1515010228)CATARC Automotive Inspection Center Excellent Engineer Program(2023B0909050007).
文摘To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated salt composite phase change material(HSCPCM)with dual phase transition temperature zones has been proposed.This HSCPCM,denoted as SDMA10,combines hydrophilic modified expanded graphite,an acrylic emulsion coating,and eutectic hydrated salts to achieve leakage prevention,enhanced thermal stability,cycling stability,and superior phase change behavior.Battery modules incorporating SDMA10 demonstrate significant thermal control capabilities.Specifically,the cylindrical battery modules with SDMA10 can maintain maximum operating temperatures below 55°C at 4 C discharge rate,while prismatic battery modules can keep maximum operating temperatures below 65°C at 2 C discharge rate.In extreme battery overheating conditions simulated using heating plates,SDMA10 effectively suppresses thermal propagation.Even when the central heating plate reaches 300°C,the maximum temperature at the module edge heating plates remains below 85°C.Further,compared to organic composite phase change materials(CPCMs),the battery module with SDMA10 can further reduce the peak thermal runaway temperature by 93°C and delay the thermal runaway trigger time by 689 s,thereby significantly decreasing heat diffusion.Therefore,the designed HSCPCM integrates excellent latent heat storage and thermochemical storage capabilities,providing high thermal energy storage density within the thermal management and thermal runaway threshold temperature range.This research will offer a promising pathway for improving the thermal safety performance of battery packs in electric vehicles and other energy storage systems.
文摘Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to ten times more parameters than traditional sensors,making selection a challenging task even for experts.This study addresses this challenge,focusing on defensive guidance,navigation,and control(GNC)systems where precise and reliable angular velocity measurement is critical to overall performance.A comprehensive mathematical model is introduced to encapsulate all key MEMS parameters,accompanied by discussions on calibration and Allan variance interpretation.For six leading MEMS gyroscope applications,namely inertial navigation,integrated navigation,autopilot systems,rotating projectiles,homing guidance,and north finding,the most critical parameters are identified,distinguishing suitable and unsuitable sensor choices.Special emphasis is placed on inertial navigation systems,where practical rules of thumb for error evaluation are derived using six degrees of freedom motion equations.Rigorous simulations demonstrate the influence of various sensor parameters through real-world case studies,including static navigation,multi-rotor attitude estimation,gimbal stabilization,and north finding via a turntable.This work aims to be a beacon for practitioners across diverse fields,empowering them to make more informed design decisions.
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
基金funded by the Science and Technology Program of State Grid Corporation of China(5500-202356358A-2-1-ZX).
文摘Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−frequency arc discharge experiments encounter limitations in isolating pressure wave characteristics due to persistent gas generation and arc reignition.To circumvent these challenges,an oil-immersed impulse voltage discharge platform was conceived and engineered to investigate pressure wave propagation dynamics.A pressure numerical simulation model and theoretical model of oil−solid interface reflection and refraction were subsequently established to elucidate the pressure propagation mechanism.The experimental and simulation results show that the pressure wave generated by pulsed arc discharge in oil propagates radially in the form of spherical waves.Due to the viscous loss and wave front expansion of transformer oil,the peak pressure decays exponentially with distance,with a decay coefficientβ=1.15.When pressure waves encounter metal obstacles inside transformer oil,there are two propagation paths:direct transmission through and multiple reflections through,and a mode transformation of pressure waves occurs at the oil−solid interface,mainly propagating through obstacles in the form of transverse waves.This work quantitatively delineates the energy pressure wave coupling,propagation dynamics,and attenuation mechanisms,providing critical insights for assessing and mitigating arc fault-induced transformer explosion risks.
基金supported in part by the Fundamental Research Funds for the Central Universities(No.2020YJS162).
文摘This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg game is utilized to model the P2P energy trading process.Stackelberg equilibrium(SE)is regarded as a P2P optimal trading strategy.A two-stage privacy protection solution technique combining data-driven and model-driven is developed to obtain the SE.Specifically,energy storage scheduling problem in MGs is formulated as a Markov decision process with discrete periods,and a multi-action single-observation deep deterministic policy gradient(MASO-DDPG)algorithm is proposed to tackle optimal scheduling of energy storage in the first stage.According to optimal scheduling of energy storage,the closed-form expression for SE based on model-driven is derived,and distributed SE solution technique(DSET)is developed to obtain SE in the second stage.Case studies involving a 4-Microgrid demonstrate the P2P electricity price obtained by the two-stage method,as a novel pricing mechanism,can reasonably regulate microgrid operation mode and improve microgrid income participating in the P2P market,which verifies effectiveness and superiority of the proposed P2P energy trading model and two-stage solution method.
基金supported in part by the National Natural Science Foundation of China(No.52407115)State Key Laboratory of Power System Operation and Control(61011000223).
文摘Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.
文摘A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing to their partial coupling with the grid,are particularly vulnerable to voltage dips and excessive reactive power absorption during fault events.This study proposes an adaptive control strategy based on Model Reference Adaptive Control integrated with stator flux-oriented vector control to regulate active and reactive power of a DFIG-based wind farm connected to a standard IEEE 9-bus power system under fault conditions.The proposed control scheme is developed and validated using detailed MATLAB/Simulink modeling under normal operation,symmetrical three-phase fault conditions,and post-fault recovery scenarios.A three-phase-to-ground fault is applied at the wind farm interconnection bus for a duration of 150 ms to evaluate transient performance.Simulation results demonstrate that the adaptive controller ensures fast power tracking,effective reactive power support,and enhanced voltage recovery compared to a conventional proportional–integral controller.Quantitatively,the proposed method improves voltage recovery time by approximately 45%,reduces active power overshoot by 38%,and lowers total harmonic distortion by 52% following fault clearance.Furthermore,the adaptive controller maintains stable operation under variations in wind speed and machine parameters without requiring retuning,highlighting its robustness against system uncertainties.The results confirm that the proposed control strategy significantly enhances fault ride-through capability,power quality,and dynamic stability of grid-interfaced wind farms.These findings demonstrate the practical applicability of adaptive control techniques for improving the reliability and resilience of modern power systems with high wind energy penetration.
基金supported by the National Key R&D Program of China(No.2021YFB2401200)National Natural Science Foundation of China(72242105)Organized Research Support Program,Department of Electrical Engineering,Tsinghua University.
文摘With the increasing penetration of variable renewable energy,flexible resources are highly needed to hedge the growing uncertainty,and variability in the power system.Demand response has served as a cost-effective type of flexible resource in recent years.In order to balance the uncertainty of the system,it is crucial to assess how much flexibility demand response programs can provide.Thus,forecasting demand response potential is important for the operation of the bulk system.This paper proposes a modeling approach that can characterize the multi-timescale flexibility of demand response so that not only the power potential but also temporal-coupling characteristics can be considered.Furthermore,a day-ahead demand response potential forecasting method is proposed using deep convolutional generative adversarial networks.The proposed forecasting method is tested using data from 170 users in Pecan Street Dataport.The results show that the proposed method can forecast the multi-timescale flexibility of demand response with high accuracy.
文摘Grid-Forming(GFM)converters are prone to fault-induced overcurrent and power angle instability during grid fault-induced voltage sags.To address this,this paper develops a multi-loop coordinated fault ridethrough(FRT)control strategy based on a power outer loop and voltage-current inner loops,aiming to enhance the stability and current-limiting capability of GFM converters during grid fault conditions.During voltage sags,the GFM converter’s voltage source behavior is maintained by dynamically adjusting the reactive power reference to provide voltage support,thereby effectively suppressing the steady-state component of the fault current.To address the active power imbalance induced by voltage sags,a dynamic active power reference correction method based on apparent power is designed to mitigate power angle oscillations and limit transient current.Moreover,an adaptive virtual impedance loop is implemented to enhance dynamic transient current-limiting performance during the fault initiation phase.This approach improves the responsiveness of the inner loop and ensures safe system operation under various fault severities.Under asymmetric fault conditions,a negative-sequence reactive current compensation strategy is incorporated to further suppress negative-sequence voltage and improve voltage symmetry.The proposed control scheme enables coordinated operation of multiple control objectives,including voltage support,current suppression,and power angle stability,across different fault scenarios.Finally,MATLAB/Simulink simulation results validate the effectiveness of the proposed strategy,showcasing its superior performance in current limiting and power angle stability,thereby significantly enhancing the system’s fault ride-through capability.
文摘Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhancement characteristics of IGCTs,HCCs demonstrate superior performance at reduced extinction angles,thereby minimizing reactive power consumption.This study presents a comprehensive investigation into reactive power control strategies for HCCs operating at small extinction angles.First,the topological configuration and commutation principle of HCC are elucidated.Subsequently,the mechanism of HCC reactive power control is analyzed,and a reactive power control strategy is proposed by combining the converter transformer taps with extinction angles.Moreover,the relationship between transformer taps and reactive power exchange under different rated extinction angles is calculated,and the theoretically rated extinction angle is proposed.Finally,to validate the proposed control strategy,a four-terminal ultra-high voltage direct current power grid incorporating HCC technology is modeled and sim-ulated using PSCAD/EMTDC.The simulation results demonstrate that the proposed strategy effectively supports AC systems by reducing reactive power absorption in HCCs,while simultaneously exhibiting enhanced reli-ability and economic efficiency.
文摘The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.
基金supported in part by Northeast Branch of State Grid Corporation of China(52992624000W).
文摘Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transient stability.This paper investigates a common countermeasure involving a PI-based DC voltage controller for GFM-PV systems,revealing that their small-signal stability is sensitive to parameter tuning.The study develops a generalized DC voltage-dominated 2nd-order GFM model and successfully conducts complex torque analysis,showing that this approach can be effectively extended to other dynamics governed by DC voltage-dominated GFM systems.Subsequently,the paper establishes a stability criterion for GFM-PV systems and proposes a parameter tuning method for DC voltage controllers that incorporates damping margin considerations.The performance of the tuned single-machine-infinite-bus GFM-PV system is validated on the RT-LAB real-time simulation platform under scenarios involving solar irradiance fluctuations and grid frequency disturbances.The proposed method proves effective in ensuring the stability of the GFM-PV system,with robust theoretical support.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant number:82171965.
文摘Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.
基金supported in part by the State Key Laboratory of Robotics and System under Grant SKLRS202407B.
文摘For hybrid-electric unmanned aerial vehicles(UAVs),the stable power supply from the onboard permanent magnet synchronous generator(PMSG)is critical.Overheating in the confined compartment can directly lead to power interruption and system failure.Therefore,proactively improving the thermal management is not only a key technical prerequisite for ensuring flight reliability and mission success,but also enhances the machine’s efficiency and the overall power density of the system.Targeting the stringent spatial constraints in UAV applications,novel self-air-cooling heat dissipation topologies are investigated and highlighted on the rotor sidewall for compact outer-rotor generators.A systematic optimization framework,centered on a multi-objective genetic algorithm,is developed to Pareto-optimize the fin geometries,balancing thermal performance against aerodynamic penalty.The proposed topologies are innovatively deployed on the rotor sidewall,uniquely combining the structural space of an outer-rotor machine with self-air-cooling to generate directed airflow of varying patterns that directly enhance the cooling efficiency of the stator.The parameters of the designed self-air-cooled heat dissipation topologies are optimized via a multi-objective genetic algorithm.A temperature rise test under windless conditions shows that the proposed self-air-cooled structure reduces the stator temperature of the generator by 37.1℃at 5000 r/min,confirming the effectiveness and engineering feasibility for practical applications.
基金the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,for funding this research work through the project number“NBU-FFR-2025-3623-11”.
文摘Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
