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Myocardial infarction (MI) remains the most common cause of death worldwide. Many MI survivors will suffer from recurrent heart failure (HF), which has been recognized as a determinant of adverse prognosis. Despite the success of improved early survival after MI by primary percutaneous coronary intervention, HF after MI is becoming the major driver of late morbidity, mortality, and healthcare costs. The development of regenerative medicine has brought hope to MI treatment in the past decade. Mesenchymal stem cell (MSC)-derived exosomes have been established as an essential part of stem cell paracrine factors for heart regeneration. However, its regenerative power is hampered by low delivery efficiency to the heart. We designed, fabricated, and tested a minimally invasive exosome spray (EXOS) based on MSC exosomes and biomaterials. In a mouse model of acute myocardial infarction, EXOS improved cardiac function and reduced fibrosis, and promoted endogenous angiomyogenesis in the post-injury heart. We further tested the feasibility and safety of EXOS in a pig model. Our results indicate that EXOS is a promising strategy to deliver therapeutic exosomes for heart repair.

At present, medical images have played a more and more important role in clinical treatment. Lung images provide an important reference for doctors to make a diagnosis. Especially for surgical patients, a tumor can be accurately removed based on the full cognition about its size, position, and quantity. Therefore, computer-aided diagnosis for the analysis and treatment of a lot of lung tumor images is very important. Aiming at complexity and self-adaption of image segmentation in lung tumors, this article proposed a parallel deep learning algorithm with hybrid attention mechanism for image segmentation. First, lung parenchyma was extracted via preprocessing images. Then, images were input into hybrid attention mechanism and densely connected convolutional networks (DenseNet) module, respectively, where hybrid attention mechanism consisted of a spatial attention mechanism and a channel attention mechanism. Finally, four feasible solutions were proposed for the verification through changing the convolution quantity of dense block in DenseNet. The network structure with the better performance was achieved. The experimental results prove the parallel deep learning algorithm with hybrid attention mechanism performed well in image segmentation of lung tumors, and its accuracy can reach 94.61%.

Solar steam generation has attracted interest in water purification and seawater desalination as an efficient solar energy conversion method. However, the complex preparation process and limited efficiency restrict the practical applications of solar-generated steam. Here, we report a three-dimensional wood membrane inside coated with MoS2 (WM-H, with S defects) for efficient seawater desalination. The vertically aligned mesoporous wood channels and their inner microstructures increase the contact interface between water and MoS2 in wood. The formation of S defects allows the surface temperature of the membrane to reach ∼53 °C under 1 sun illumination. The MoS2-coated wood with S defects exhibited an excellent evaporation flux (1.46 kg m−2 h−1, one sun), with a solar thermal conversion efficiency of 82.5%. The adsorption peak values of Na+ on the two sides of MoS2 with defects are 2.17 times and 1.49 times higher than those of MoS2 without defects. The wood-based design provides an inexpensive way to capture solar energy and generate steam.

With the advancement of Internet of Things technology, we are able to collect massive people's trajectory data from various GPS services. These large amounts of trajectory records enable us to better understand human mobility patterns. Meanwhile, we are able to extract social relationships based on these digital records to provide personalized recommendation services, such as points of interests (POI) recommendation and friend recommendation. In this paper, we propose to recommend friends for taxi drivers based on vehicular trajectory records. For this purpose, we propose to construct a vehicular ad hoc network based on co-occurrence phenomenon. Furthermore, we take advantages of the network representation learning technique on the vehicular ad hoc network for learning driver vectors. Finally, potential friends are recommended based on the similarity of driver vectors. Extensive experimental results on two real-world datasets demonstrate that our proposed method has the best performance on friend recommendation compared with several state-of-the-art methods. To the best of our knowledge, this is the first attempt to recommend friends for taxi drivers based on vehicular ad hoc network representation learning.

Although promising results have been achieved in the area of traffic sign detection, little attention has been paid to text detection on traffic signs. In fact, in today’s popular driver-less automobile industry, traffic sign text which brings abundant and valuable traffic information plays an important and indispensable role. In this work, we design an effective detector for traffic sign text, whose pipeline only consists of a preprocessing module to tackle with some complex situations, a Fully Convolutional Network (FCN) in which a Scale-transfer layer is proposed to speed up the network and a simple post-processing step. Extensive experiments on the Chinese traffic sign text dataset (CTST-1600), ICDAR 2013 and MSRA-TD500 show that the proposed method has achieved the state-of-the-art results, which proves the ability of our detector on both particularity and universality applications. We collect the Chinese text-based traffic sign dataset named CTST-1600, and it can be found at https://github.com/pummi823/test/blob/master/CTST-1600 .

Based on the requirement of industrial automation, this paper proposes a three-dimensional reconstruction algorithm of the dynamic environment for industrial operation environment perception, which can be applied to many different industrial fields. According to the actual situation of the inner wall of the rolling mill, a method of lidar raw data preprocessing is proposed in this paper. The original lidar data is tagged and the detailed local point cloud information of interest is isolated. Loop detection is introduced into the algorithm to establish a globally consistent point cloud graph. The overall environment modeling algorithm is constructed to realize the real-time dynamic modeling of indoor environment in the actual industrial environment operation. The method has been evaluated by real-time reconstruction of the three-dimensional model of the specified area, which provides prior information for the operation of the robotic manipulator. We compare the performance of three-dimensional with loam, using the actual industrial environment, and show that our algorithm achieves better accuracy with reduced computational expense.

This paper builds an optimal carbon-energy combined-flow (OCECF) model to optimize the carbon emission and energy losses of power grids simultaneously. A novel multi-agent cooperative reduced-dimension Q(λ) (MCR-Q(λ)) is proposed for solving the model. Firstly, on the basis of the traditional single-objective Q(λ) algorithm, the solution space is reduced effectively to shrink the size of Q-value matrices. Then, based on the concept of ant cooperative cooperation, multi-agents are used to update the Q-value matrices iteratively, which can significantly improve the updating rate. The simulation in the IEEE 118-bus system indicates that the proposed technique can decrease the convergence speed by hundreds of times as compared with conventional Q(λ), keeping high global stability, which is very suitable for dynamic OCECF in a large and complex power grid compared with other algorithms.

Regarding the continuous development of high-speed trains and the increase of running speeds, the aerodynamic design of high-speed trains has become significantly important, while reduction of drag and noise comprises a significant challenge in order to optimize aerodynamic design of high-speed trains. The design form factor of a high-speed train is highly influenced by aerodynamic aspects including aerodynamic drag, lift force, and noise. With the high-speed train as the object, the paper aims to take bionic concept as the entry point, selecting the hummingbird as the bionic prototype and extracting bionic elements to establish a bionic train model. Then, the finite volume method was used for numerical simulation and analysis of the aerodynamic performance and aerodynamic noise of the bionic high-speed train. Computational results prove that drag and noise of the bionic head type were lower than those of the original train; drag of the head train of the bionic model was reduced by 2.21% in comparison with the original model, while the whole-train drag was reduced by 3.53%, indicating that drag reduction effects are available and implying that the bionic head type could reduce drag and noise. Noise sources of the bionic train are mainly located at positions with easy airflow separation and violent turbulence motion. Large turbulence energy is in bogie areas and mainly exists at the leeward side of the bogie area. Obviously, the bogie area is the major noise source of the train. Aerodynamic noise of the bionic train in far-field comprises a wide-frequency range. Noises were concentrated within 613 Hz~3150 Hz. When the bionic high-speed train ran at 350 km/h, through comparative analysis of total noise levels at observed points of the high-speed train, it is found that this position with the maximum noise level was 25 m away from the head train nose tip, with the maximum value of 88.4 dB (A). When the bionic train ran at 600 km/h, the maximum sound pressure level at the longitudinal point was 99.7 dB (A) and the average noise level was 96.6 dB (A). When the running speed increased from 350 km/h to 600 km/h, the maximum noise level increased by 11.3 dB (A) and the average noise level increased by 11.6 dB (A). Computation results of aerodynamic noise at the point which is 7.5 m away from the rail center show that the maximum aerodynamic noise level existed at the first-end bogie of the head train, while the noise level was larger at the position closer to the ground.

With the world development of high-speed railways and increasing speeds, aerodynamic forces and moments acting on trains have been increased further, making trains stay at a “floated” state. Under a strong crosswind, the aerodynamic performance of a train on the embankment is greatly deteriorated; lift force and horizontal force borne by trains will be increased quickly; trains may suffer derailing or overturning more easily compared with the flat ground; train derailing will take place when the case is serious. All of these phenomena have brought risks to people’s life and properties. Hence, the paper establishes an aerodynamic model about a high-speed train passing an air barrier, computes aerodynamic forces and moments, and analyzes pulsating pressures on the train surface as well as those of unsteady flow fields around the train. Computational results indicate that when the train passed the embankment air barrier, the head wave of air pressure full wave is more than the tail wave; the absolute value of negative wave is more than that of the positive wave, which is more obvious in the head train. When the train is passing the air barrier, pressure pulsation values at head train points are more than those at other points, while pressure changes most violently at the train bottom, and pressure values close to the air barrier are more than those points far from the air barrier. Pressure values at the cross section 1 were larger than those of other points. Pressure values at measurement points of the tail train ranked the second place, with the maximum negative pressure of 1253 Pa. Pressure change amplitudes and maximum negative pressure on the train surface are increased quickly, while pressure peak values on the high-speed train surface are in direct ratio to the running speed. With the increased speed of the high-speed train, when it is running in the embankment air barrier, the aerodynamic force and moment borne by each train body are increased sharply, while the head train suffers the most obvious influences of aerodynamic effects.

The hierarchical porous heterostructured MIL-125/TiO2 @SiO2 composites are successfully obtained by biogenic-templated method using rice husk both as biomorphic. The products are characterized by X-ray diffraction, N2 -adsorption-desorption analysis, scanning electron microscopy and Fourier transform infrared spectroscopy. The results reveal that MIL-125/TiO2 @SiO2 possesses a hierarchical porous structure in scale from micrometer to nanometer with high Brunauer-Emmett-Teller (BET) surface area (385.7-902.7 m2 g-1 ). The photocatalytic mechanism was discussed in detail. Meanwhile, the activity of MIL-125/TiO2 @SiO2 for the photocatalytic degradation of rhodamine B in aqueous medium is significantly higher than that of P25. The current work provides some concept and methods in the development of MOF-based photocatalysts.

Bogies are an important component of high-speed trains. The level of mechanical performance of bogies has a major influence on the safety and reliability of high-speed train. Therefore, conducting fault diagnoses on bogies with big data is very important. Fault mechanisms of bogies are very complex, and feature signals are nonobvious. For these reasons, fault information of bogies cannot be effectively extracted using the traditional signal processing method. Therefore, this paper adopted the deep neural network to recognize faults in bogies. The deep neural network offers numerous benefits in this context. Using deep neural networks, fault information in a signal spectrum can be extracted in a selfadaptive method. This technique is free of dependence on extensive signal processing knowledge and diagnostic experience. Compared with the traditional intelligent diagnosis method, the deep neural network can obtain a higher diagnostic accuracy. Additionally, the deep neural network does not depend on the sample size, and it can obtain high diagnostic accuracy even when the sample size is relatively small. It also achieves very high diagnostic accuracy applied to high-speed trains with different speeds and different faults, which shows that the method is extensively applicable. Furthermore, the recognition accuracy rate of the deep neural network under normal conditions can reach 100%. This method provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.

Firstly, the paper established a finite element model for a steel motor shell and computed related modals, vibration velocities, stress and strain respectively. Computational results show that the flange and end shield of the motor shell had the maximum vibration velocities and strain because these locations lacked the reinforcing ribs, while the maximum stress was mainly at joints between different structures. Secondly, the steel material was replaced by the aluminum alloy. Mechanical parameters of the motor shell were recomputed and compared with those of the steel structure. Results show that modal frequency on each order increased, which is good for avoiding the structural resonance. In addition, the maximum stress of the structure decreased by 4.4 MPa, and the maximum strain decreased by 0.27 mm, which could effectively improve the fatigue characteristics of the motor shell under long-term excitation. Then, the boundary element method was used to compute radiation noises of the motor shell in far field, where the radiation noise presented an obvious directivity. Finally, the paper proposed a GA-BP neural network model to predict the radiation noise of the motor and compared the prediction results with the boundary element. In the whole analyzed frequency band, the maximum difference between the neural network prediction and the real values did not exceed 5 dB, indicating that it is feasible to predict radiation noises of the motor by the neural network. Additionally, experiments were also conducted and compared with two kinds of numerical methods. Methods proposed in this paper provide some references for realizing the rapid noise reduction and light weight of motors.

This paper presents computation of structural sound power and sound radiation modes, combined with structural dynamic equations to obtain the coupling relationship between sound and structures. As a result, the relationship between sound radiation modes of structures and structural vibration modes is established. The influence of the number and position of optimal secondary force sources on control of sound radiation modes is considered. Results show that sound radiation efficiency of sound radiation modes at the first order was more than that of sound radiation modes at other orders. The main diagonal element of coupling matrix between modes and sound radiation impedances was more than elements at other positions. Sound radiation modes at the first order were dominant sound radiation modes. When the number of secondary force sources was 4, the sound radiation power of structures was the lowest. Four force sources were taken as the basis to conduct on the related experiments in the anechoic chamber and compare with the computational result. Their results had a good consistency, which showed that the mentioned theory method was effective. Finally, the control strategy was applied to roofs of the vehicle. Experiments verified that sound pressure level of the driver in the low frequency was obviously improved, which remedied the defect of other optimization strategies for solving noises in the low frequency.

Synthesized MoS2 nanosheets have disadvantages such as low purity (phase conversion), low stability (charge density), limited active sites and complex preparation procedures, severely hindering their applications, such as in water treatment. Herein, a facile method was designed to synthesize high-phase-purity and high-negative-charge-density solar-induced atomic-defect-rich 1T-MoS2 (S-ADR-MoS2) nanosheets. Sunlight irradiation drove the formation of atomic vacancies (diameters between approximately 0.4 and 0.9 nm; S and Mo were codeleted) in the 1T-MoS2 nanosheets by attacking ·OH and ·O2–. Consequently, S-ADR-MoS2 exhibited high selectivity and affinity for toxic metals, and 1 g S-ADR-MoS2 under 1 and 4 sun illuminations captured 638 mg and 902 mg Pb2+, respectively, which is better than the performance of most graphene-based and sulfur-functionalized nanomaterials. We demonstrated that the ionic capture of defect-rich features via strong Lewis soft − soft interactions and electrostatic attractions was the dominant mechanism for the improved Pb2+ removal capacity under sunlight irradiation. Furthermore, the layer-stacked S-ADR-MoS2 membrane achieved high water fluxes (181–345 L m−2h−1 bar−1) and a high separation performance for Pb2+ (84.5–99.6%), resulting in a Pb2+ concentration in treated water that was below the U. S. Environmental Protection Agency limits for drinking water. S-ADR-MoS2 was also able to efficiently capture other toxic metals, such as Al3+ and Cd2+, thus exhibiting high potential for the treatment of contaminated drinking water.

Techniques based on the elasto-magnetic (EM) effect have been receiving increasing attention for their significant advantages in cable stress/force monitoring of in-service structures. Variations in ambient temperature affect the magnetic behaviors of steel components, causing errors in the sensor and measurement system results. Therefore, temperature compensation is essential. In this paper, the effect of temperature on the force monitoring of steel cables using smart elasto-magneto-electric (EME) sensors was investigated experimentally. A back propagation (BP) neural network method is proposed to obtain a direct readout of the applied force in the engineering environment, involving less computational complexity. On the basis of the data measured in the experiment, an improved BP neural network model was established. The test result shows that, over a temperature range of approximately &minus, 10 &deg, C to 60 &deg, C, the maximum relative error in the force measurement is within &plusmn, 0.9%. A polynomial fitting method was also implemented for comparison. It is concluded that the method based on a BP neural network can be more reliable, effective and robust, and can be extended to temperature compensation of other similar sensors.

The parallel plate capacitive humidity sensor based on the grid upper electrode is considered to be a promising one in some fields which require a humidity sensor with better dynamic characteristics. To strengthen the structure and balance the electric charge of the grid upper electrode, a strip is needed. However, it is the strip that keeps the dynamic characteristics of the sensor from being further improved. The numerical method is time- and cost-saving, but the numerical study on the response time of the sensor is just of bits and pieces. The numerical models presented by these studies did not consider the porosity effect of the polymer film on the dynamic characteristics. To overcome the defect of the grid upper electrode, a new structure of the upper electrode is provided by this paper first, and then a model considering the porosity effects of the polymer film on the dynamic characteristics is presented and validated. Finally, with the help of software FLUENT, parameter effects on the response time of the humidity sensor based on the microhole upper electrode are studied by the numerical method. The numerical results show that the response time of the microhole upper electrode sensor is 86% better than that of the grid upper electrode sensor, the response time of humidity sensor can be improved by reducing the hole spacing, increasing the aperture, reducing film thickness, and reasonably enlarging the porosity of the film.

International audience; Systems are built by connecting different components (e.g., sensors, actuators, process components) that are, in turn, organized to achieve system objectives. But, when a system component fails, the system's objectives can no longer be achieved. For many years, numerous studies have proposed efficient fault detection and isolation (FDI) and fault-tolerant control (FTC) algorithms. This paper considers faults that lead to the complete failure of actuators. In this specific case, the system's physical structure changes, and the system model thus becomes incorrect. The potential that the system has to continue to achieve its objectives has to be re-evaluated from a qualitative point of view, before recalculating or modifying the control algorithms. To this end, this paper proposes a self-updating system model to reflect the current system potential, a formulation of system objectives using temporal logic, and a verification method based on model checking to verify if the objectives can still be achieved by the faulty system. The systems considered are discrete-continuous systems.

International audience; This paper continues to present the framework of multi-faults diagnosis from the preceding paper. There are two main contributions in this part. One is aimed at developing a new diagnostic process for continuous and dynamic system and its corresponding consistency-checking module. This diagnostic process and its consistency-checking module are all based on the models defined by STRIPS actions. The other is that the fault models are introduced into the consistency-checking module for preventing the impossible diagnoses. Moreover, the STRIPS can qualitatively define the fault models without requiring detail and precise knowledge about faulty components.

International audience; This paper is part I of a two part effort that is intended to present a framework of multi-faults diagnosis. Reiter [14] has proposed a consistency-based approach for multi-faults diagnosis. We extend his theory to deal with the dynamic and continuous systems and offer a necessary assumption and a formal demonstration. Multi-faults diagnosis is a partially observable problem because there is usually not enough information about faults. So the STRIPS, a classic technique of automated planning, is chosen to build the system model. It provides the reasoning ability for the multi-faults diagnosis when diagnosis is formalized as reasoning from effects to causes with causal knowledge.

This paper proposes a formal framework for reconfigurable control, based on model checking. This framework first generates a flexible model (i.e., an execution structure) according to the diagnosis, then defines a temporal specification language to deal with the problems due to infinite execution cycles and non-determinism, and finally provides the algorithms that will automatically verify whether the updated model satisfies the desired specification.

This paper proposes a generic component model (GCM) to give a functional representation of large scale systems. The GCM rests on the notion of services provided by components and their organization into operating modes. To each operating mode corresponds a behavior model uses as reference to diagnose fault. Faults are detected by local diagnosers built on subsystems using a merging procedure without any global model and global diagnoser.