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.

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.

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.

Systematic improvement in Tong's B-type water drive method

Tong's B-type water drive method was proposed as early as the 1970s and has been widely applied in the dynamic prediction and effective evaluation of oilfield development.Through extensive applications and studies,many researchers found that the statistical constants in the formula of the Tong's B-type water drive method(also referred to as the Tong's B-type formula)are not applicable to multiple types of reservoirs,especially low-permeability ones,due to the limited range of reservoir types when the formula was conceived.Moreover,they put forward suggestions to improve the Tong's B-type formula,most of which focused on the research and calculation of the first constant in the formula.For oilfields in the development stages of high or ultra-high water cuts,it is widely accepted that different types of reservoirs have different limit water cuts.This understanding naturally makes it necessary to further modify the Tong's B-type formula.It is practically significant to establish the water drive formula and cross plot considering that the two constants in the formula vary with reservoir type.By analyzing the derivation process and conditions of the Tong's B-type formula,this study points out two key problems,i.e.,the two constants 7.5 and 1.69 in the formula are not applicable to all types of reservoir.Given this,this study establishes a function between key reservoir parameters and the first constant and another function between key reservoir parameters and recovery efficiency.Based on the established two functions and considering that different types of oil reservoir have different limit water cuts,this study develops an improved Tong's B-type formula and prepares the corresponding improved cross plot.The results of this study will improve the applicability and accuracy of Tong's B-type water drive method in predicting the trend of water cut increasing for different types of oil reservoirs.

How to Achieve a Warp Drive

In this paper, we delve into the intrinsic nature of mass and gravity, as per the amplitude modulation interpretation of the quantum theory. We explore the idea that the elementary constituent is an electromagnetic configuration that interacts with the quantum field, leading to the emergence of inertia and gravity as a reaction to the exchange with the quantum field. While these two phenomena have a common origin, they are distinct. Our proposal suggests manipulating the connection between the quantum field and the particle using high-frequency electromagnetic fields, thereby making a warp drive possible.

T_(2)W-DRIVE-HR序列在钆造影核磁共振成像检测豚鼠实验性内耳内淋巴积水中的应用

目的研究驱动平衡自旋回波序列(T_(2)W-DRIVE-HR)在钆造影核磁共振成像(MRI)时检测豚鼠内耳内淋巴积水的效果。方法8只雌性豚鼠分为两组,包括对照组和内淋巴积水模型组(腹腔注射醋酸去氨加压素),各组相应处理17天后进行内耳磁共振成像和组织形态学检查。结果T_(2)W-DRIVE-HR序列在矢状位扫描时内耳成像效果好。内耳磁共振成像结果表明,与对照组相比,积水模型组豚鼠耳蜗内淋巴体积明显增加(t=3.594,P<0.05);组织形态学结果表明,与对照组相比,积水模型组豚鼠耳蜗每一转中阶面积/(前庭阶面积+中阶面积)的比值明显升高(t=3.105,P<0.05)。结论腹腔注射醋酸去氨加压素诱导豚鼠内耳内淋巴积水,使用T_(2)W-DRIVE-HR序列在钆造影磁共振成像检测的内耳内淋巴积水与组织形态学检测结果一致,说明该序列可用于豚鼠内耳内淋巴积水的检测。

Design of Damping Damping Design of New Boring Head Drive Structure

This paper takes the boring head of 20 roll mill as the research object,optimizes the vibration inside the boring head through a differential damping structure,and then conducts the 3 d model inside the boring head through SolidWorks,and checks the interference of the boring head model.Finally,Ansys workbench finite element analysis software is used to analyze and verify the vibration damping characteristics of the differential damping structure.

基于平行测试的认知自动驾驶智能架构研究

在大数据、云计算和机器学习等新一代人工智能技术的推动下,自动驾驶的感知智能在近年来得到显著的提升与发展.然而,与人类驾驶过程中隐含的以自我目的实现为引导的自探索性和自主性相比,现阶段自动驾驶技术主要以辅助驾驶功能为主,还停留在以被动感知、规划与控制为主的初级智能自动驾驶阶段.为实现车辆智能从数据驱动的环境感知、辅助决策、被动规划到知识驱动的场景认知、推理决策、主动规划的提升,亟需增强车辆自身对复杂外界信息归纳提炼、推理决策、评价估计等类人能力.首先回顾自动驾驶关键技术演化及其应用发展历程;随后分析测试对车辆智能评估的效用;然后基于平行测试理论,提出自动驾驶车辆认知智能训练、测试与评估空间的构建方法,并设计基于平行测试的认知自动驾驶智能训练框架.该项研究工作预期能为推动自动驾驶从感知智能向认知智能的升级提供可行的技术支撑与实现路径.

脑卒中患者重返驾驶能力评估的研究现状及进展

汽车驾驶已经成为人们日常生活中不可或缺的部分,也是脑卒中患者重返社会的最重要能力之一。安全驾驶和驾驶训练的基础是对驾驶能力的准确评估。根据国外研究,脑卒中后的驾驶能力评估主要包括路测、心理测验和驾驶模拟。目前,我国不仅缺少脑卒中患者安全驾驶的评定标准,也缺乏明确、统一的评估方案。近年来,医工结合技术和计算机技术的发展为驾驶能力的客观化、精准化、智能化评估提供了可能。本文旨在对脑卒中患者重返驾驶的评估研究及进展情况进行综述。

考虑工作记忆容量和时间压力影响的驾驶绩效分析

随着风险驾驶机理研究的不断完善,外部情境因素和驾驶人内部认知差异逐渐成为当前研究的热点和难点。本文为研究时间压力、工作记忆容量与驾驶绩效之间的关系,采用时间约束和动机等主客观相结合的方法实现时间压力的施加,采用复杂跨度范式测量驾驶人工作记忆容量。基于驾驶模拟器系统构建相关驾驶场景,开展心理学与模拟驾驶行为试验,采集驾驶人心理、操作和车辆运行数据,分别就时间压力、工作记忆容量及其组合对驾驶绩效的影响进行分析。结果表明:时间压力对超速比例、碰撞概率、制动反应时间、左转选择间隙皆有显著的影响效应,且时间压力越高,超速比例越大、碰撞概率越高、制动反应时间越快、左转选择间隙越小的频次越多;工作记忆容量仅对制动反应时间有影响,工作记忆容量越高,制动反应时间越快;时间压力与工作记忆容量对制动反应时间无交互影响,但随着时间压力的增大,高工作记忆容量人群与低工作记忆容量人群在制动反应时间上的差异逐渐变小。这些发现为研究时间压力下的驾驶绩效提供了驾驶人认知方面的新见解。

分布式驱动车辆操纵稳定性控制综述

为研究分布式驱动车辆的驱动控制问题,从转向控制、独立驱动控制及2个系统联合控制等3个角度入手,分析了分布式驱动车辆操纵稳定性控制策略的研究进展。首先,基于转向控制技术对影响操纵稳定性的方面进行分类;其次,分析了分层式与集中式控制结构的特点;然后,分析了基于控制结构的操纵稳定性研究;最后,结合分布式驱动车辆操稳性改善的研究现状和技术进展,展望了未来的发展趋势。