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Purpose This study aims to accurately calculate the magnetic field distribution, which is a prerequisite for pre-design and optimization of electromagnetic performance. Accurate calculation of magnetic field distribution is a prerequisite for pre-design and optimization. Design/methodology/approach This paper proposes an analytical model of permanent magnet machines with segmented Halbach array (SHA-PMMs) to predict the magnetic field distribution and electromagnetic performance. The field problem is divided into four subdomains, i.e. permanent magnet, air-gap, stator slot and slot opening. The Poisson’s equation or Laplace’s equation of magnetic vector potential for each subdomain is solved. The field’s solution is obtained by applying the boundary conditions. The electromagnetic performances, such as magnetic flux density, unbalanced magnetic force, cogging torque and electromagnetic torque, are analytically predicted. Then, the influence of design parameters on the torque is explored by using the analytical model. Findings The finite element analysis and prototype experiments verify the analytical model’s accuracy. Adjusting the design parameters, e.g. segments per pole and air-gap length, can effectively increase the electromagnetic torque and simultaneously reduce the torque ripple. Originality/value The main contribution of this paper is to develop an accurate magnetic field analytical model of the SHA-PMMs. It can precisely describe complex topology, e.g. arbitrary segmented Halbach array and semi-closed slots, etc., and can quickly predict the magnetic field distribution and electromagnetic performance simultaneously.

With minimal defects, few grain boundaries, and high-performance optoelectronic characteristics, organic semiconductor nanocrystals (OSNCs) have emerged as promising solutions for miniaturized orga...

Lateral driving behavior analysis is the foundation of freeway cross-section design and the focus of road safety research. However, the factors that influence vehicle lateral driving behavior have not been clearly explained. The dataset of the natural driving trajectory of freeways is used in this study to analyze vehicle lateral driving behavior and trajectory characteristics. As vehicle trajectory characteristic indicators, parameters such as preferred trajectory deviation and standard deviation are extracted. The effects of lane position, speed, road safety facilities, and vehicle types on freeway trajectory behavior are investigated. The results show that lane width and lane position significantly impact vehicle trajectory distribution. As driving speed increases, the lateral distance between vehicles in the inner lane and the guardrail tends to increase. In contrast, vehicles in the outside lane will stay away from the road edge line, and vehicles in the middle lane will stay away from the right lane dividing line when the speed increases. Statistical analysis shows that the preferred trajectory distribution of the same vehicle type in different lane positions is significantly different among groups (Cohen’s d > 0.7). In the same lane, the lateral position characteristics of the center of mass of different vehicle types are basically the same (Cohen’s d < 0.35). This work aims to explain what variables cause trajectory deviation behaviors and how to design traffic safety facilities (guardrail and shoulder) and lane width to accommodate various vehicle types and design speeds.

Bioskins possess a great ability to detect and deliver external mechanical or temperature stimuli into identifiable signals such as color changes. However, the integration of visualization with simultaneous detection of multiple complex external stimuli in a single biosensor device remains a challenge. Here we propose an all-solution-processed bioinspired stretchable electronic skin with interactive color changes and four-mode sensing properties. The fabricated biosensor demonstrates sensitive responses to various stimuli including pressure, strain, voltage, and temperature. Sensing visualization is realized by color changes of the e-skin from brown to green and finally bright yellow as a response to intensified external stimuli, suggesting great application potential in military defense, healthcare monitoring, and smart bionic skin.

Many small-spacing interchanges (SSI) appear when the density of the expressway interchanges increases. However, the characteristics of traffic accidents in SSI have not been explained clearly. Therefore, this paper systematically takes the G3001 expressway in Xi’an as the research object to explore the accident characteristics of SSI. Firstly, the expressway is divided into four sections. Furthermore, their safety can be evaluated by the number of accidents per unit distance of 100 million vehicles (NAP). Subsequently, eight indexes, such as mean spacing distance (MSD), are selected to explain the cause affecting expressway safety by developing the least square support vector machine (LSSVM). Secondly, the difference between SSI and normal-spacing interchanges (NSI) is clarified by statistical analysis. Finally, LSSVM, random forest, and logistic regression models are built using 12 indicators, such as the time spent exploring the causes of serious accidents. The results show that the inner ring NAP in Sections I and II with SSI is 27.2 and 33.7, higher than in other sections. The density, annual average daily traffic, and MSD adversely affect expressway traffic safety. The road condition mainly influences the serious traffic accidents in the SSI. This study can provide the theoretical basis for traffic management and accident prevention in the SSI of the expressway.

Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations between two sentences in the semantic meaning of a document. Moreover, most existing methods are not powerful enough to differentiate the importance of different document features. To address these problems, this paper proposes a new neural network model: AttBiLSTM-2DCNN, which entails two perspectives. First, a two-layer, bidirectional long short-term memory (BiLSTM) network is utilized to obtain the sentiment semantics of a document. The first BiLSTM layer learns the sentiment semantic representation from both directions of a sentence, and the second BiLSTM layer is used to encode the intrinsic relations of sentences into the document matrix representation with a feature dimension and a time-step dimension. Second, a two-dimensional convolutional neural network (2DCNN) is employed to obtain more sentiment dependencies between two sentences. Third, we utilize a two-layer attention mechanism to distinguish the importance of words and sentences in the document. Last, to validate the model, we perform an experiment on two public review datasets that are derived from Yelp2015 and IMDB. Accuracy, F1-Measure, and MSE are used as evaluation metrics. The experimental results show that our model can not only capture sentimental relations but also outperform certain state-of-the-art models.

Neuromorphic electronics, which use artificial photosensitive synapses, can emulate biological nervous systems with in-memory sensing and computing abilities. Benefiting from multiple intra/interactions and strong light-matter coupling, two-dimensional heterostructures are promising synaptic materials for photonic synapses. Two primary strategies, including chemical vapor deposition and physical stacking, have been developed for layered heterostructures, but large-scale growth control over wet-chemical synthesis with comprehensive efficiency remains elusive. Here we demonstrate an interfacial coassembly heterobilayer films from perylene and graphene oxide (GO) precursors, which are spontaneously formed at the interface, with highly uniform bilayer structure of single-crystal perylene and well-stacked GO over centimeters in size. The planar heterostructure device exhibits an ultrahigh specific detectivity of 5.01×1016 Jones and ultralow energy consumption of 10− 9 W as well as broadband photoperception from 365 to 1550 nm. Moreover, the device shows extraordinary photonic synaptic behaviors with a paired-pulse facilitation (PPF) index of 214% in neuroplasticity, and the heterosynapse array has the capability of information reinforcement learning and recognition.

This study proposes a simultaneous optimization model that considers flow assignment and vehicle capacity for the problem of transit network design to determine the route structure and frequencies simultaneously. The problem is focused on reducing the total travel time and the number of transfers. A heuristic algorithm is developed to solve this problem. In the proposed algorithm, the initial routes are generated according to a changing demand matrix, which can reflect the real-time demand with transfers and ensure that the direction of route generation maximizes the percentage of direct service. A regulating method for a sequence of stops is used during route generation to guarantee the shortest trip time for a formed route. Vehicles are allocated to each route according to the flow share. The concept of vehicle difference is introduced to evaluate the distinction between actual allocated vehicles and required vehicles for each route. The optimization process of frequencies based on vehicle difference can ensure that the solution meets the constraints. Two scale networks are used to illustrate the performances of the proposed method. Results show that route structure and frequencies can be optimized simultaneously through the proposed method. Different scenarios are created to test the algorithm properties via various parameter values. The test result indicates that the upper bound is a key parameter to balance the proportion of direct service and average in-vehicle travel time (AIVTT), and the increased number of planning routes can improve the proportion of direct service.

Flexible electronics is an interdisciplinary research field. By employing the intrinsically soft materials, the resulting devices feature outstanding mechanical flexibility and conformability, and expected lightweight configuration, functional reconfigurability, large-area compatibility. Here we explore the current status, including materials, fabrication and applications, of flexible electronics from intrinsically soft materials. The future applications and challenges of this pervading technology are also considered.

Electronic textiles (e-textiles) typically comprise fabric substrates with electronic components capable of heating, sensing, lighting and data storage. In this work, we rationally designed and fabricated anisotropic light/thermal emitting e-textiles with great mechanical stability based on a sandwich-structured tri-electrode device. By coating silver nanowire network/thermal insulation bilayer on fabrics, an anisotropic thermal emitter can be realized for smart heat management. By further covering the emissive film and the top electrode on the bilayer, light emitters with desirable patterns and colors are extracted from the top surface via an alternative current derived electroluminescence. Both the light and thermal emitting functions can be operated simultaneously or separately. Particularly, our textiles exhibit reliable heating and lighting performance in water, revealing excellent waterproof feature and washing stability.

Permanent magnet synchronous motors are widely used and have sufficient development prospects in the drive systems of electric vehicles. Traditional model predictive control (MPC) methods are shown to achieve good control performance by tracking the d- and q-axis current as well as limiting the current amplitude. However, the dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Therefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. The state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. A reinforcement learning (RL) algorithm is used to obtain the weight coefficient of the transition error term in the constraint module for automatically determining the best switch state for the next control period using the cost function. Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. The torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%.

Suppressing the operating current in resistive memory devices is an effective strategy to minimize their power consumption. Herein, we present an intrinsic low-current memory based on two-dimensional (2D) hybrid heterostructures consisting of partly reduced graphene oxide (p-rGO) and conjugated microporous polymer (CMP) with the merits of being solution-processed, large-scale, and well patterned. The device with the heterostructure of p-rGO/CMP sandwiched between highly reduced graphene oxide (h-rGO) and aluminum electrodes exhibited rewritable and nonvolatile memory behavior with an ultralow operating current (∼1 μA) and efficient power consumption (∼2.9 μW). Moreover, the on/off current ratio is over 103, and the retention time is up to 8 × 103 s, indicating the low misreading rate and high stability of data storage. So far, the value of power is about 10 times lower than those of the previous GO-based memories. The bilayer architecture provides a promising approach to construct intrinsic low-power resistive memory devices.

It is generally assumed that the stator core and rotor core are infinitely permeable in conventional analysis methods. Therefore, magnetic saturation is easily overlooked and the magnetic flux density is overestimated. This article introduces the analysis model of a permanent magnet (PM) machine with a segmented Halbach array (PMMSHA). It considers the finite permeability of the stator teeth and tooth tips and can study the saturation effect of the teeth. Therefore, the electromagnetic performance is calculated, including the magnetic field distribution, excitation force density, cogging torque, and electromagnetic torque in all areas. The results of finite element analysis (FEA) show that the analysis model is highly accurate.

In this paper, a novel 3-axis torsion quasi-zero-stiffness (TQZS)-based sensor system is proposed for absolute rotation measurement in vibration systems. Analysis on the applied torque and structural parameters shows that the proposed mechanism with two concentric spheres connected by six symmetry distributed springs could approximately achieve TQZS features in three rotational degrees of freedom simultaneously, thus a remarkable torsional vibration measurement performance can be guaranteed. Although due to high stiffness, the mechanism also has high anti-interference ability about force or vibration in translational directions. Mathematical modeling and numerical simulations are carried out to show the effectiveness of the proposed 3-axis sensor system in different base vibrations. In consideration of its feasibility and high performance, it can be foreseen that the proposed TQZS-based sensor will get more and more implementations in various engineering practices with torsional vibration measurements.

In order to improve the working performance and service life of electric vehicles, the temperature field of the in-wheel motor is optimized based on the finite element analysis model. The winding copper loss, stator core loss, permanent magnet eddy current loss and stray loss are used as heat sources for the temperature field analysis of the in-wheel motor. Each component of the in-wheel motor is analyzed for transient temperature changes using a magneto-thermal coupling method. The temperature variation of the components of the in-wheel motor with time and the spatial distribution characteristics are studied in detail. Finally, the joint simulation model including the in-wheel motor body and the external drive control circuit is established, which fully considers the transient changes of the motor loss distribution during the working process.

A permanent magnet motor with auxiliary slots in the stator teeth is taken as the research object in this article. In the two-dimensional polar coordinate system, the Fourier series method is used to establish an analytical model of the electromagnetic field of surface-mounted permanent magnet motors. The motor solution domain is divided into five sub-domains: permanent magnet, air gap, stator slot, stator slot opening, and auxiliary slot. The electromagnetic properties such as air gap magnetic density, no-load back-EMF, cogging torque, and electromagnetic torque of the motor are calculated. The analytic model is used to analyze the electromagnetic characteristics of surface-mounted permanent magnet motors under no load and load.

Convolutional neural networks (CNNs) have been widely used in computer vision applications and achieved great success. However, large-scale CNN models usually consume a lot of computing and memory resources, which makes it difficult for them to be deployed on embedded devices. An efficient block-floating-point (BFP) arithmetic is proposed in this paper. compared with 32-bit floating-point arithmetic, the memory and off-chip bandwidth requirements during convolution are reduced by 50% and 72.37%, respectively. Due to the adoption of BFP arithmetic, the complex multiplication and addition operations of floating-point numbers can be replaced by the corresponding operations of fixed-point numbers, which is more efficient on hardware. A CNN model can be deployed on our accelerator with no more than 0.14% top-1 accuracy loss, and there is no need for retraining and fine-tuning. By employing a series of ping-pong memory access schemes, 2-dimensional propagate partial multiply-accumulate (PPMAC) processors, and an optimized memory system, we implemented a CNN accelerator on Xilinx VC709 evaluation board. The accelerator achieves a performance of 665.54 GOP/s and a power efficiency of 89.7 GOP/s/W under a 300 MHz working frequency, which outperforms previous FPGA based accelerators significantly.

In today's world, determining how to improve the quality of the economy, technology innovation and human well-being within resource and environmental limits is a global sustainable development goal. Thus, research on eco-efficiency (EE), eco-technology innovation (ETI) and eco-well-being performance (EWP) has great significance for improving the efficiency of converting ecological consumption into economic benefits, technological innovation and human well-being. This paper presents a two-stage Super-slack-based measure (SBM) model to examine the EE, ETI and EWP (3E) of 102 countries. The indicator system for the 3E network process is constructed by using inputs, intermediate products and outputs. The empirical results show that the highest 3E performers are Singapore, the United States and Iceland. The results also indicate that countries with high levels of economic development have much better EE, ETI and EWP levels than low-income countries. After comparing the values of the 3E in different countries with the global average, the results further show that overall global 3E performance is relatively low. These findings provide significant references to help governments generate different strategies for improving national 3E performance.

Haustoria, one of the fundamental characteristics of obligate parasite, is a micro-branch produced by biotrophic fungi and oomycetes, which is composed of haustorial body, extrahaustorial matrix and extrahaustorial membrane. It is an abnormal structure that can invade the host cell interaction with the plant. Haustoria is not only the key factor of biotrophic fungi carrying on the living specimen nutrition way but also represents significant roles in the nutrition biosynthesis and inhibiting the defense reaction of host. The deeper understanding of haustoria will favor us acquaint obligate parasite enormously, so that we can control the corresponding diseases better. This paper summarized the function on nutrition and pathogenicity of haustoria, and the problems and the research trend in this area were discussed.

Wheat leaf rust (Puccinia triticina) is one of the most important plant diseases in the world, and growing resistant cultivars is the most economical, effective and environmentally safe method for controlling this disease. With the aim of evaluating leaf rust resistance, 10 uncharacterized wheat genotypes, as well as 35 lines with known leaf rust resistance genes (Lr genes), were exposed to 12 Chinese P. triticina pathotypes at both seedling and adult stages. Two wheat genotypes, 09p205 and 09p200, showed high resistance to all the tested pathotypes at both seedling and adult stages. Three accessions, Xinmai 16, 00-225-2-1-2-2, and 21941-6, showed resistance at the adult stage. Four genotypes, 21898-3, 21924-4, 21915-3, and 21982-1, showed slow rusting resistance. In addition, 24 molecular markers were used to assess the presence of 20 known Lr genes. In the 10 genotypes analyzed Lr2c, Lr3ka, Lr9, Lr14a, Lr26, Lr34, and Lr37 were detected, either alone or in combination.