Effects of drive imbalance on the particle emission from a Bose-Einstein condensate in a one-dimensional lattice

Time-periodic driving has been an effective tool in the field of nonequilibrium quantum dynamics,which enables precise control of the particle interactions.We investigate the collective emission of particles from a Bose-Einstein condensate in a one-dimensional lattice with periodic drives that are separate in modulation amplitudes and relative phases.In addition to the enhancement of particle emission,we find that amplitude imbalances lead to energy shift and band broadening,while typical relative phases may give rise to similar gaps.These results offer insights into the specific manipulations of nonequilibrium quantum systems with tone-varying drives.

Research on Driver’s Fatigue Detection Based on Information Fusion

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

Controlled thermally-driven mass transport in carbon nanotubes using carbon hoops

Controlling mass transportation using intrinsic mechanisms is a challenging topic in nanotechnology.Herein,we employ molecular dynamics simulations to investigate the mass transport inside carbon nanotubes(CNT)with temperature gradients,specifically the effects of adding a static carbon hoop to the outside of a CNT on the transport of a nanomotor inside the CNT.We reveal that the underlying mechanism is the uneven potential energy created by the hoops,i.e.,the hoop outside the CNT forms potential energy barriers or wells that affect mass transport inside the CNT.This fundamental control of directional mass transportation may lead to promising routes for nanoscale actuation and energy conversion.

A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance reliability.This study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive shafts.Initially,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python programming.This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process constraints.Case studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.

Uphill or downhill?Cropland use change and its drivers from the perspective of slope spectrum

The continuous decrease of low-slope cropland resources caused by construction land crowding poses huge threat to regional sustainable development and food security.Slope spectrum analysis of topographic and geomorphic features is considered as a digital terrain analysis method which reflects the macro-topographic features by using micro-topographic factors.However,pieces of studies have extended the concept of slope spectrum in the field of geoscience to construction land to explore its expansion law,while research on the slope trend of cropland from that perspective remains rare.To address the gap,in virtue of spatial analysis and geographically weighted regression(GWR)model,the cropland use change in the Yangtze River Basin(YRB)from 2000 to 2020 was analyzed and the driving factors were explored from the perspective of slope spectrum.Results showed that the slope spectrum curves of cropland area-frequency in the YRB showed a first upward then a downward trend.The change curve of the slope spectrum of cropland in each province(municipality)exhibited various distribution patterns.Quantitative analysis of morphological parameters of cropland slope spectrum revealed that the further down the YRB,the stronger the flattening characteristics,the more obvious the concentration.The province experienced the greatest downhill cropland climbing(CLC)was Shannxi,while province experienced the highest uphill CLC was Zhejiang.The most common cropland use change type in the YRB was horizontal expansion type.The factors affecting average cropland climbing index(ACCI)were quite stable in different periods,while population density(POP)changed from negative to positive during the study period.This research is of practical significance for the rational utilization of cropland at the watershed scale.

Heberlein jet technology drives innovations in demanding technical applications

Heberlein Technology AG,Wattwil,Switzerland,March 7,2024–Unique technology for creating advanced technical textiles with exquisite characteristics was featured at the Techtextil in Germany.Heberlein,the leading supplier of air interlacing and air texturing jets,plans to convince visitors to the event with critical components–such as the latest PolyJet-TG-3–that can influence both yarn properties and processes.

Nullmax纽劢 MaxDrive行泊一体方案

是目前市面上极少有的真融合行泊一体4.0形态方案,具有高准度、高稳定、强性能、易落地的产品优势,传感器深度复用、芯片资源共享、安全高效经济,可满足客户各类需求。立足于Nullmax平台化BEV-AI技术架构,兼顾技术性能和部署成本的双重需求,支持高中低不同算力平台,适配任意传感器,可根据需求进行定制化开发,快速完成全场景智能驾驶功能的量产应用,且向上拓展性极强。

毫末智行 自动驾驶大模型 DriveGPT雪湖·海若

行业首个自动驾驶生成式大模型DriveGPT(雪湖·海若)为大模型技术落地自动驾驶提供了新范式。DriveGPT是由毫末自研的自动驾驶认知大模型升级而来,最初目的主要是解决自动驾驶的认知决策优化问题,其基本过程是在认知模块引入真实人驾数据,通过人类反馈强化学习技术,对自动驾驶认知决策模型进行持续优化。当前毫末正在通过引入感知大模型,将DriveGPT升级为通用大模型架构,目标是实现端到端自动驾驶。

Dynamics of a three-level V-type atom driven by a cavity photon and microwave field

We discuss the dynamics of a three-level V-type atom driven simultaneously by a cavity photon and microwave field by examining the atomic population evolution. Owing to the coupling effect of the cavity photon, periodical oscillation of the population between the two upper states and the ground state takes place, which is the well-known vacuum Rabi oscillation. Meanwhile, the population exchange between the upmost level and the middle level can occur due to the driving action of the external microwave field. The general dynamic behavior is the superposition of a fast and a slow periodical oscillation under the cooperative and competitive effect of the cavity photon and the microwave field. Numerical results demonstrate that the time evolution of the population is strongly dependent on the atom-cavity coupling coefficient g and Rabi frequency Ωe that reflects the intensity of the external microwave field. By modulating the two parameters g and Ωe, a large number of population transfer behaviors can be achieved.

Quantum coherence in A-three-level system driven by bichromatic coupling field

We present a theoretical study of quantum coherent effects in a A-three-level system with a strong bichromatic coupling field and a weak probe field. When one component of the strong bichromatic coupling field is resonant with a corresponding transition and the other is detuning with an integer fraction of the Rabi frequency of the resonant field, the absorption spectrum exhibits a series of symmetrical doublets. While two frequencies of the strong bichromatic coupling field are symmetrically detuned from the transition, the position and the relative intensity of the absorption peak are both affected by the coupling field intensity and detuning. An explanation of the spectrum is given in term of the dressed-state formalism.

Three-level inverter configuration with common mode voltage elimination for induction motor drive (To continue)

The multiplicity of vector combinations for vectors of combined three-level inverters plays an important role, when deciding on the modulation scheme, to obtain minimum switching per inverter vector change, as described in the next Section. This is not possible with reduced common-mode three-level inverter structure, obtained with a five-level cascaded H-bridge configuration, as the space vectors locations do not exhibit multiplicity. Moreover, the proposed configuration requires only two power supplies, whereas the scheme with the five-level H-bridge configuration requires six isolated power supplies.

Three-level inverter configuration with common mode voltage elimination for induction motor drive

The multiplicity of vector combinations for vectors of combined three-level inverters plays an important role, when deciding on the modulation scheme, to obtain minimum switching per inverter vector change, as described in the next Section. This is not possible with reduced common-mode three-level inverter structure, obtained with a five-level cascaded H-bridge configuration, as the space vectors locations do not exhibit multiplicity. Moreover, the proposed configuration requires only two power supplies, whereas the scheme with the five-level H-bridge configuration requires six isolated power supplies.

Investigation of electron cyclotron wave absorption and current drive in CFETR hybrid scenario plasmas

The investigation of electron cyclotron(EC)wave absorption and current drive has been performed for the China Fusion Engineering Test Reactor(CFETR)hybrid scenarios using the TORAY code.To achieve the physics goal of the EC system in CFETR,a total of four wave frequency values and nine locations of launching antennas have been considered,and the injection poloidal and toroidal angles have been scanned systematically.The electron cyclotron current drive(ECCD)efficiency of the 170 GHz EC system is quite low due to the wave-particle interactions being located at the low-field side.To optimize the ECCD efficiency,the wave frequency is increased up to 221–250 GHz,which leads to the power being deposited at the high-field side.The off-axis ECCD efficiency can be significantly enhanced by launching EC waves from the top window and injecting them towards the high-field side.The optimized ECCD efficiency atρ=0.32 and atρ=0.4 is 2.9 and 2.2 times that of 170 GHz,respectively.

Optimization of X-mode electron cyclotron current drive in high-electron-temperature plasma in the EAST tokamak

A discharge with electron temperature up to 14 keV has been achieved in EAST.Analysis of the electron cyclotron current drive(ECCD)efficiency at high electron temperature under EAST parameters is presented using C3PO/LUKE code.Simulation results show that the ECCD efficiency of X-mode increases with central electron temperature up to 10 keV and then starts to decrease above 10 keV,at a specific magnetic field and toroidal angle.The efficiency degradation is due to the presence of the third harmonic extraordinary(X3)downshifted absorption at the low field side(LFS);even the cold resonance of X3 mode is located outside the plasma.As the electron temperature increases from 5 to 20 ke V,the X3 absorption increases from 0.9%to 96.4%.The trapping electron effect at the LFS produces a reverse Ohkawa current.The competition between the Fisch–Boozer current drive and the Ohkawa current drive results in a decrease in ECCD efficiency.ECCD efficiency optimization is achieved through two methods.One is to increase the toroidal angle,leading to X2 mode predominating again over X3 mode and the electron resonance domain of X2 mode moving far from the trapped/passing boundary.The second one is to increase the magnetic field to move away the X3 resonance layer from the plasma,hence less EC power absorbed by X3 mode.

Research on Energy Efficiency Characteristics of Mining Shovel Hoisting and Slewing System Driven by Hydraulic-Electric Hybrid System

Mining shovel is a crucial piece of equipment for high-efficiency production in open-pit mining and stands as one of the largest energy consumption sources in mining.However,substantial energy waste occurs during the descent of the hoisting system or the deceleration of the slewing platform.To reduce the energy loss,an innovative hydrau-lic-electric hybrid drive system is proposed,in which a hydraulic pump/motor connected with an accumulator is added to assist the electric motor to drive the hoisting system or slewing platform,recycling kinetic and potential energy.The utilization of the kinetic and potential energy reduces the energy loss and installed power of the min-ing shovel.Meanwhile,the reliability of the mining shovel pure electric drive system also can be increased.In this paper,the hydraulic-electric hybrid driving principle is introduced,a small-scale testbed is set up to verify the feasibil-ity of the system,and a co-simulation model of the proposed system is established to clarify the system operation and energy characteristics.The test and simulation results show that,by adopting the proposed system,compared with the traditional purely electric driving system,the peak power and energy consumption of the hoisting electric motor are reduced by 36.7%and 29.7%,respectively.Similarly,the slewing electric motor experiences a significant decrease in peak power by 86.9%and a reduction in energy consumption by 59.4%.The proposed system expands the application area of the hydraulic electric hybrid drive system and provides a reference for its application in over-sized and super heavy equipment.

Modulation of Respiratory Neural Drive by Physiological Loads in COVID-19 Patients with Dyspnea

COVID-19 patients often experience dyspnea due to several factors. The underlying unique pathophysiology of dyspnea in COVID-19 is not yet fully understood, but it is believed to be related to a combination of respiratory, cardiovascular, and neuromuscular factors. Hypoxemia is considered one of the key symptoms of COVID-19. This affects the respiratory drive, which determines the rate, depth, and pattern of breathing. The relationship between increased ventilatory neural drive and abnormal gas exchange, particularly in the context of ventilation/perfusion (V/Q) mismatches and chemosensitivity, has gained significant attention following the COVID-19 pandemic. The ACE2 receptors allow viral entry into the lungs, leading to the loss of surfactant, hypoxic vasoconstriction, and intrapulmonary shunting that may result in a V/Q mismatch. Additionally, acidosis, hypercapnia, elevated 2,3-diphosphogly-cerate levels and fever may shift the oxygen diffusion curve rightward, lowering arterial oxygen saturation levels and triggering ventilatory responses. This paper examines how physio pathological factors such as altered gas diffusion, chemosensory feedback, V/Q ratios, altered compliance, arterial blood gases, and respiratory muscle dysfunction in these patients affect ventilatory drive. A review of the published literature was also conducted to determine the mechanism of dyspnea. To ensure appropriate gas exchange, individuals need to augment their minute ventilation (VE) when physiological dead space is elevated. This serves as a compensatory mechanism to counteract the effects of compromised gas exchange and keep adequate oxygenation throughout the body. The respiratory centers may experience dysregulation due to the impact of the virus on the respiratory system, which could affect the rhythm-generating and pattern-generating signals that are vital for regulating the respiratory rate and depth of breathing effort. The cerebral cortex, in conjunction with the brain stem centers, plays a crucial role in regulating ventilation during prolonged hypoxemia. This interaction between these two components may help elucidate the conscious respiratory sensation (or dyspnea) experienced by patients. It is hypothesized that neuroventilatory decoupling acts as a mechanism to prevent sensory signals from translating into mechanical or ventilatory responses. This decoupling phenomenon is believed to have a notable impact on the intensity of breathlessness. By understanding the relationship between increased ventilatory neural drive and abnormal gas exchange, particularly in the context of ventilation/perfusion (V/Q) mismatches and altered chemosensitivity, healthcare professionals can develop strategies to optimize respiratory support for COVID-19 patients.

Three-Dimensional Conjugate Tooth Surface Design and Contact Analysis of Harmonic Drive with Double-Circular-Arc Tooth Profle

A three-dimensional conjugate tooth surface design method for Harmonic Drive with a double-circular-arc tooth profle is proposed. The radial deformation function of the fexspline (FS), obtained through Finite Element (FE) analysis, is incorporated into the kinematics model. By analyzing the FS tooth enveloping process, the optimization of the overlapping conjugate tooth profle is achieved. By utilizing the hobbing process, the three-dimensional machinable tooth surface of FS can be acquired. Utilizing the coning deformation of the FS, simulations are conducted to analyze the multi-section assembly and meshing motion of the machinable tooth surface. The FE method is utilized to analyze and compare the loaded contact characteristics. Results demonstrate that the proposed design method can achieve an internal gear pair consisting of a circular spline with a spur gear tooth surface and the FS with a machinable tooth surface. With the rated torque, approximately 24% of the FS teeth are engaged in meshing, and more than 4/5 of the tooth surface in the axial direction carries the load. The contact patterns, maximum contact pressure, and transmission error of the machinable tooth surface are 227.2%, 40.67%, and 71.24% of those on the spur gear tooth surface, respectively. It clearly demonstrates exceptional transmission performance.

Influences of Mixed Traffic Flow and Time Pressure on Mistake-Prone Driving Behaviors among Bus Drivers

Bus safety is a matter of great importance in many developing countries, with driving behaviors among bus drivers identified as a primary factor contributing to accidents. This concern is particularly amplified in mixed traffic flow (MTF) environments with time pressure (TP). However, there is a lack of sufficient research exploring the relationships among these factors. This study consists of two papers that aim to investigate the impact of MTF environments with TP on the driving behaviors of bus drivers. While the first paper focuses on violated driving behaviors, this particular paper delves into mistake-prone driving behaviors (MDB). To collect data on MDB, as well as perceptions of MTF and TP, a questionnaire survey was implemented among bus drivers. Factor analyses were employed to create new measurements for validating MDB in MTF environments. The study utilized partial correlation and linear regression analyses with the Bayesian Model Averaging (BMA) method to explore the relationships between MDB and MTF/TP. The results revealed a modified scale for MDB. Two MTF factors and two TP factors were found to be significantly associated with MDB. A high presence of motorcycles and dangerous interactions among vehicles were not found to be associated with MDB among bus drivers. However, bus drivers who perceived motorcyclists as aggressive, considered road users’ traffic habits as unsafe, and perceived bus routes’ punctuality and organization as very strict were more likely to exhibit MDB. Moreover, the results from the three MDB predictive models demonstrated a positive impact of bus route organization on MDB among bus drivers. The study also examined various relationships between the socio-demographic characteristics of bus drivers and MDB. These findings are of practical significance in developing interventions aimed at reducing MDB among bus drivers operating in MTF environments with TP.

DC–DC Converter with Pi Controller for BLDC Motor Fuzzy Drive System

The Brushless DC Motor drive systems are used widely with renewable energy resources.The power converter controlling technique increases the performance by novel techniques and algorithms.Conventional approaches are mostly focused on buck converter,Fuzzy logic control with various switching activity.In this proposed research work,the QPSO(Quantum Particle Swarm Optimization algorithm)is used on the switching state of converter from the generation unit of solar module.Through the duty cycle pulse from optimization function,the MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor)of the Boost converter gets switched when BLDC(Brushless Direct Current Motor)motor drive system requires power.Voltage Source three phase inverter and Boost converter is controlled by proportional-integral(PI)controller.Based on the BLDC drive,the load utilized from the solar generating module.Experimental results analyzed every module of the proposed grid system,which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics(PV)power is generated and the QPSO with Duty cycle switching state is determined.The Boost converter module is boost stage based on generation and load is obtained.Single Ended Primary Inductor Converter(SEPIC)and Zeta converter model is compared with the proposed logic;the proposed boost converter achieves the results.Three phase inverter control,PI,and BLDC motor drive results.Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures.Overall design model is done by using MATLAB/Simulink 2020a.

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults.In this paper,a deep learning-based observer,which combines the convolutional neural network(CNN)and the long short-term memory network(LSTM),is employed to approximate the nonlinear driving control system.CNN layers are introduced to extract dynamic features of the data,whereas LSTM layers perform time-sequential prediction of the target system.In terms of application,normal samples are fed into the observer to build an offline prediction model for the target system.The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs.Online fault detection can be realized by analyzing the residuals.Finally,an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme.Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.