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Improper torque division strategy in vehicles leads to unnecessary energy consumption as well as poor driving comfort and safety. Previous research ignores the time-delay phenomenon of vehicle power systems, hindering the further improvement of related performance. In this work, an efficient two-layer torque split strategy with the consideration of the time-delay phenomenon is proposed to improve tracking accuracy, reduce fuel consumption, and avoid battery electrical abuse. In the upper layer controller, an optimal energy management strategy (EMS) is developed to coordinate between the battery state and engine fuel consumption rate by solving a nonlinear optimization problem, generating the reference torque split scheme. The bottom layer controller is based on Time-delay Model Predict Control (TMPC), comprehensively considering the reference torque split scheme, future information, and vehicle power system information. This TMPC strategy reduces both the torque tracking error and velocity tracking error by exploiting the characteristics that hydraulic braking system (HBS), motor and engine have different response times. Event-trigger (ET) mechanism is introduced in the frame of TMPC for the trade-off between control performance and computation efficiency. Stability and feasibility are theoretically guaranteed by the invariant set method. Simulation results verify the effectiveness of the proposed strategy.

As satellite and communication technology advances, terrestrial-satellite communications and broadcasting (TSCB) provide uninterrupted services, meeting the demand for seamless communication and broadcasting interconnection. The evolving TSCB technology faces challenges in handling dynamic time-frequency features of wireless signals. Stable satellite-ground interaction is crucial, as co-channel interference can disrupt communication, causing instability. To address this, the TSCB system needs an effective mechanism to eliminate signal interference. Current methods often overlook complex domain features, resulting in suboptimal outcomes. Leveraging deep learning’s computational power, we introduce WSIE-Net, an encoder-decoder model for TSCB signal interference elimination. The model learns an effective separation matrix for robust separation amidst wireless signal interference, comprehensively capturing orthogonal features. We analyze time-frequency diagrams, bit error rates, and other parameters. Performance assessment involves similarity coefficients and Kullback-Leibler Divergence, comparing the proposed algorithm with common blind separation methods. Results indicate significant progress in signal interference elimination for TSCB.

Semantic segmentation is an important research topic in the environment perception of intelligent vehicles. Many semantic segmentation networks based on bilateral architecture have been proven effective. However, semantic segmentation networks based on this architecture has the risk of pixel classification errors and small objects being overwhelmed. In this paper, we solve the problem by proposing a novel three-branch architecture network called LCFNets. Compared to existing bilateral architecture, LCFNets introduce compensation branch for the first time to preserve the features of original images. Through two efficient modules, Lightweight Detail Guidance Fusion Module (L-DGF) and Lightweight Semantic Guidance Fusion Module (L-SGF), detail and semantic branches are allowed to selectively extract features from this branch. To balance the three-branch features and guide them to fuse effectively, a novel aggregation layer is designed. Depth-wise Convolution Pyramid Pooling module (DCPP) and Total Guidance Fusion Module (TGF) enable the aggregation layer to extract the global receptive field and realize multi-branch aggregation with fewer calculation complexity. Extensive experiments on Cityscapes and CamVid datasets have shown that our family of LCFNets provide a better trade-off between speed and accuracy. With the full resolution input and no ImageNet pre-training, LCFNet-slim achieves 76.86% mIoU at 114.36 FPS and LCFNet achieves 77.96% mIoU at 92.37 FPS on Cityscapes. On the other hand, LCFNet-super achieves 79.10% mIoU at 47.46 FPS.

Theincreasing number of smart vehicles is leading to an increasing scarcity of spectrum resources for the internet of vehicles (IoV), which has given rise to an urgent requirement for automatic modulation classification (AMC) in cognitive radio (CR) systems. Meanwhile, for the flexibility of unmanned aerial vehicles (UAVs), the AMC implemented based on UAVs is considered an effective method to achieve reliable communication between intelligent vehicles. However, for distributed UAV task implementation, real-time radio data needs to be transmitted between UAVs and a cloud server. This process requires maintaining a high-capacity, secure channel environment, which is difficult to accomplish. In this article, we propose a federated transfer learning framework to implement AMC in a distributed scenario, which avoids radio data transmission in each UAV. To reduce data dependence, the pre-trained deep learning (DL)-based model is sent to each UAV node and performs transfer learning, which brings more focused learning of the channel environment in which various UAVs are located. The simulation results show that federated transfer learning-based AMC offers better recognition accuracy than centralized approach. Compared to the centralized training methods, the federated transfer learning algorithm achieves an improvement of 1.04% to 12.05% in classification accuracy for each node with less training data. Besides, the effect of different fine-tuning layers on the accuracy is investigated, showing that fine-tuning three layers could achieve optimal accuracy. Additionally, different numbers of UAVs are employed to verify the impact on the results. The experimental results show that the number of UAVs can improve the results but to a limited extent. Furthermore, we evaluate the proposed method by various measurements, such as accuracy, precision, and F1-score. Accordingly, compared with the baseline methods, the proposed scheme achieves an improvement of 1% to 14% over them.

With the advancement of multimedia technology, 5G MBMS has become a promising technology that makes full and effective use of network resources to provide services. Mobile edge computing (MEC) is a key technology that brings important improvements to MBMS by utilizing contextual information such as multimedia, virtual reality, etc. However, the deployment of MEC may bring the latency to the network, which requires the deployment consideration of computing nodes. Moreover, mobile network operators need to consider the number of base stations required to accommodate the upcoming MBMS data streams. In this paper, we propose a computational offloading strategy and a co-optimization mechanism of video resource assignment in an edge cloud server enabled MBMS service system. Our approach aims to maximize computation speed while satisfying limited resource constraints, constrained energy consumption, and minimum latency time requirements, in order to provide better MBMS services. Additionally, our approach offers an advanced solution for achieving optimal computational offloading and resource allocation in edge cloud server enabled MBMS service systems. Extensive simulation results demonstrate that the proposed mechanism outperforms current state-of-the-art schemes. Specifically, the total calculation rate of the large-scale cloud server is significantly reduced under energy constraints, total delay requirements, and different network parameters.

This article proposes an improved channel reconstruction method to increase the accuracy of Doppler emulations when measuring directional multi-antenna devices in over-the-air (OTA) testing at millimeter wave (mmWave) bands. In real radio channels, the Doppler spectrum depends on incoming ray angles. In OTA testing, the target Doppler spectrum is accurately created when assessing omni-directional antenna terminals using the conventional prefaded signal synthesis (PFS) method. However, the devices under test (DUTs) may consist of directional antennas that produce different gains in impinging angles. Since the PFS method ignores the dependence of the Doppler spectrum on the DUT antenna element pattern, it will lead to inaccurate channel emulation, thus affecting the link performance of the DUT. Therefore, an improved PFS method is proposed by considering the impact of the DUT antenna element pattern. Specifically, the rays within a cluster mapped to active probes are divided into multiple subsets, each of which is weighted to synthesize the cluster. The objective function with constraints between the target temporal covariance and emulated one is established to obtain all subset weights. Simulation results verify that the proposed channel emulation method is superior to the existing PFS technique for Doppler emulation while not degrading the emulated spatial characteristics using the proposed optimization strategy.

Signal phase optimization at signalized intersection is of great importance to urban traffic control and management yet is very challenging. The traditional approaches for signal phase optimization heavily rely on the traffic engineering practitioners’ experience. To tackle these challenges, a novel data-driven method is proposed to realize signal phase optimization and recommendation solely using limited amount of real signalized intersection samples. Firstly, all of discrete features related to signal phase design, encoded by one-hot representation, are sampled by the Gumbel-SoftMax distribution, which is a continuous approximation to a multinomial distribution. With this approximation distribution, the generative adversarial network (GAN) is applied to produce the most acceptable signal phase samples among all acceptable choices, dealing with the problem of insufficient samples and uneven sample distribution in real word. Thirdly, a decision-tree based classifier is established to realize signal phase recommendation automatically. We conducted extensive experiments to evaluate our proposed method on three cities in China, including Beijing, Tongxiang and Chaozhou. The experimental results showed that the proposed method could effectively improve the signalized intersection operation efficiency. Moreover, the proposed method has already been deployed in several cities, and it successfully keeps serving hundreds of signalized intersections. This confirms that SPSRec is a practical and robust solution for large-scale real-world signal control services.

Controlling a non-statically bipedal robot is challenging due to the complex dynamics and multi-criterion optimization involved. Recent works have demonstrated the effectiveness of deep reinforcement learning (DRL) for simulation and physical robots. In these methods, the rewards from different criteria are normally summed to learn a scalar function. However, a scalar is less informative and may be insufficient to derive effective information for each reward channel from the complex hybrid rewards. In this work, we propose a novel reward-adaptive reinforcement learning method for biped locomotion, allowing the control policy to be simultaneously optimized by multiple criteria using a dynamic mechanism. The proposed method applies a multi-head critic to learn a separate value function for each reward component, leading to hybrid policy gradients. We further propose dynamic weight, allowing each component to optimize the policy with different priorities. This hybrid and dynamic policy gradient (HDPG) design makes the agent learn more efficiently. We show that the proposed method outperforms summed-up-reward approaches and is able to transfer to physical robots. The MuJoCo results further demonstrate the effectiveness and generalization of HDPG.

Currently, sensing and communication are two indispensable functions in the UAV platform while these two modules are independent in hardware. However, the similarities between them seem a growing trend, such as hardware, channel characteristics, signal processing methods, etc. Integrated sensing and communication (ISAC) has been the hot topic to alleviate the issue of low spectrum efficiency and pursuit the hardware gain and integration gain as far as possible. As the vital platform for the sensing and communication functions, UAV is facing great potential for development when meeting ISAC. If ISAC load replaces the independent communication load and sensing load, UAV will be more flexible due to the reduction of load weight. Moreover, the capacity of sensing and communication functions will be mutually beneficial, gradually deriving a new information theory. Motivated by this, this article focuses on the possible challenges and future directions of ISAC-empowered UAV. More specifically, ISAC between UAV and base station is firstly discussed from the angles of base station for UAV and UAV for base station. Then, ISAC with UAV swarm is exploited, including ISAC for swarm internal, ISAC for swarm external and ISAC-enabled UAV swarm. Finally, UAV-assisted sensing for ground targets is analyzed. This article aims at providing our superficial opinions for ISAC-enabled UAV and UAV-assisted ISAC, promoting the further progress of ISAC, UAV and their integration.

Due to the rising cost of human resources in road maintenance and the pursuit of efficiency, autonomous road inspection vehicles are developed for intelligent detection of road disease to prevent severe traffic disasters in the early stages. Nevertheless, as a prevalent road disease, road cracks are diverse and susceptible to shadows, weather changes, and noise in data acquisition. Moreover, they usually appear with thin shapes that are hard to detect correctly by existing methods. To handle this problem, more details of the road cracks need to be better analyzed. In this article, we propose a refined road crack detection method named locally enhanced cross-shaped windows transformer (LECSFormer), which adopts a delicate design of the encoder-decoder structure. The encoder employs window-based transformer blocks to model long-range dependencies. Each transformer block ensembles the locally enhanced module to enrich the local contextual information, and token shuffle operation is applied to build cross-windows connections. The decoder uses dense connections to fuse multi-scale information, and the feature fusion module fuses hierarchical features and reweights them by the channel attention mechanism. The proposed method outperforms other state-of-the-art methods with ODS of 0.963, 0.917, 0.952, and 0.953 on four challenging datasets, CrackTree260, CrackLS315, Stone331, and CRKWH100. It can accurately detect cracks in road surfaces and support intelligent preventive maintenance of roads.

This paper conducted series fire tests without load for glulam bolted joints with slotted-in steel plates, where the effects of fire exposure time, fire-retardant coating, fire exposure side and connecting steel bolts number for specimens were considered. Meanwhile, after fire tests, post-fire-performance (PFP) tests were conducted to explore and compare the residual load-carrying capacity and post-fire semi-rigid behavior of the glulam bolted joints with slotted-in steel plates. As a benchmark and comparison, two specimens were also conducted for bending tests without fire exposure. The test results showed that the steel inside the joints including steel box, slotted-in steel plate and steel bolts had a significant effect on the internal temperature distributions and the charring of outer timber. Moreover, the post-fire strength and ductility of the tested specimens decreased with the increase of the fire exposure time, while painting with fire-retardant coating, reducing fire exposure sides and increasing connecting steel bolts for specimens could appropriately increase their residual bending resistance and ductility deformation capacity. Finally, considering the different residual sections for timber after charring under different fire exposure times, a semi-rigid numerical model for the prediction of moment-rotation of the joints after fire exposure was established for the glulam bolted joints with slotted-in steel plates, and the numerical results demonstrated that the predicted moment-rotation curves were in good agreement with the tested curves. The test results could provide a reference for the performance-based fire protection design for timber structures.

Existing deep reinforcement learning (RL) are devoted to research applications on video games, e.g., The Open Racing Car Simulator (TORCS) and Atari games. However, it remains under-explored for vision-based autonomous urban driving navigation (VB-AUDN). VB-AUDN requires a sophisticated agent working safely in structured, changing, and unpredictable environments; otherwise, inappropriate operations may lead to irreversible or catastrophic damages. In this work, we propose a deductive RL (DeRL) to address this challenge. A deduction reasoner (DR) is introduced to endow the agent with ability to foresee the future and to promote policy learning. Specifically, DR first predicts future transitions through a parameterized environment model. Then, DR conducts self-assessment at the predicted trajectory to perceive the consequences of current policy resulting in a more reliable decision-making process. Additionally, a semantic encoder module (SEM) is designed to extract compact driving representation from the raw images, which is robust to the changes of the environment. Extensive experimental results demonstrate that DeRL outperforms the state-of-the-art model-free RL approaches on the public CAR Learning to Act (CARLA) benchmark and presents a superior performance on success rate and driving safety for goal-directed navigation. [ABSTRACT FROM AUTHOR]

On the basis of the finite element theory, in this study, a theoretical design and simulation practice to carry out steady-state static analysis, structural dynamics analysis, and electromagnetic interferencecharacteristics analysis on smart light poles, is combined. On this basis, a design of modular weight reduction and cost reduction was proposed, which realized the multi-objective parameter optimization of the smart light pole. The simulation results show that the mass of the optimized light pole can be reduced by 14.2%, and the material cost can be reduced by 14.7%. At the same time, the comprehensive performance of the optimized smart light pole can still meet the design requirements of the industry standards. The research results provide a reference for the lightweight design and the comprehensive analysis of the smart light pole in the future.

It is well-known that optimizing the wheel system of lunar rovers is essential. However, this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers. In this study, an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance. First, a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension, considering the complex terrain of the moon. Next, the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption. This was achieved via analysis of the wheel force during movement. Finally, the effectiveness of the proposed method was demonstrated by several simulation experiments. The newly designed wheel can protrude on demand and reduce energy consumption; it can be used as a reference for lunar rover development engineering in China.

With the development of electric vehicles, more and more high requirements for the lightweight design on EV body are put forward. This paper aims to study a new structure, aluminum alloy mortise-and-tenon structure, which is used for lightweight EVs. Two new types on aluminium alloy MT-T joint specimens are designed and tested under low-cycle reciprocating loading. And the failure characteristics, hysteretic curve, skeleton curve, restoring force model and energy dissipation curve are obtained by analyzing the experimental data. Moment and rotation angle relationship is conducted by joint geometric deformation and equilibrium relation. Based on theoretical model, a simplified bilinear model has been proposed for engineering calculation. When the beam bending performance is qualitatively analyzed, the theoretical model, simulation model and bilinear model are verified through experiments and finite element method simulations. The results show that mortise-and-tenon T joint main failure modes are plastic deformation caused by the upper contact area squeezed each other and the lower contact area divorced each other. Furthermore, it can be obtained from the theoretical model that MT-T joint deformation process consists of elastic segment and elastoplastic segment. Theoretical model, simulation model and bilinear model agree well with the experimental results. This fruit gives a useful reference to composite material used in EVs.

Background: For life, cars have brought convenience to people, besides causing environmental pollution, traffic congestion, and road safety. One of the main reasons for traffic accidents is that drivers cannot respond to road state and information timely. Intelligent vehicle and safety driving assistance technology are one of the ways to reduce traffic accidents. Many papers and patents are studying how to reduce the rate of traffic accidents. Objective: Traffic sign recognition is an important part of the intelligent vehicle system and advanced driver assistance system. To reduce the rate of traffic accidents in weak illumination, this paper proposes a detection and recognition method for intelligent vehicle traffic sign based on machine vision. Methods: Firstly, the traffic signs are reclassified from the color and shape characteristics of traffic signs. Then, the concept of color shape pairs is put forward, and the color-geometric model is established. To achieve the enhancement effect, the image is preprocessed by the uniformity of the histogram. We mainly use the fast algorithm of invariant moments and Zernike moments to extract the sign features. Finally, template matching and support vector machine are used to traffic sign image recognition. Results: The experimental results show that the technology has good robustness in complex environments such as weak light, occlusion and shadow and it can improve the image recognition rate effectively. Conclusion: Compared with the traditional method, it provides a method for intelligent vehicle traffic sign identification under weak illumination conditions.

Inconel 718, a nickel-based superalloy widely used in the aviation field, is difficult to machine using traditional methods such as milling. Electrochemical machining (ECM) is a recommended manufacturing method forInconel 718 due to its effective and economical processing characteristics. This paper investigates the dissolution effects on surface integrity in ECM of Inconel 718. Two types of materials with different heat treatments, solid solution, and solution and aging were studied to analyze their electrochemical behavior. They have different metallographic microstructures and precipitate phases. There are many Laves phase (Fe2Nb), d phase (Ni3Nb), and carbides in material of solid solution state, while there are fewer in material of solution and aging state. Experiments were then performed on these materials under identical ECM conditions. The results show that the surface roughness of the solid-solution material was about Ra 1.378 um while it was Ra 0.188 um for the solution and aging material. A dissolution model was proposed to illustrate the electrochemical behavior of solid solution Inconel 718, revealing that the uneven distribution of the metallographic phase and the rich content of the element niobium (Nb) will influence the surface integrity of the ECMed sample.

This paper presents a constrained hybrid optimal model predictive control method for the mobile energy storage system of Intelligent Electric Vehicle. A novel adaptive cruise control system is designed to optimize mobile energy storage management, active safety control, and fuel economy. A hierarchical control structure is proposed for active safety control and energy flow management. The main-loop is proposed to analyze and optimize active cruise safety control and energy management index using non-linear constrained hybrid optimal model predictive control method. The inner loop is used to chase the aim signal from the main loop using hysteresis current control method. Then, an electronic longitudinal control system is designed to avoid the collision and optimize energy management between the IEV and cruise following vehicles. At last, the simulations with typical driving conditions are built to justify the performance of the proposed controller. The results illustrate that the IEV with the designed hybrid controller can adaptively tracking the following vehicles, reduce the possibility of collision with optimal energy flow management.

Lane changing is a significant research topic about micro transportation simulation and safety assistant driving. It has massive influence on transportation operation and safety. Lane changing and warning model researches can highlight operational laws of intelligent vehicles. So such researches are meaningful forintellectualization of vehicles and realization of automatic driving. This text analyzes lane changing models, impacts and lane changing warning methods thoroughly and discusses performances of existing lane changing models by addressing recent patents and scientific papers. Analysis of basic process in lane changing safety warning methods is also conducted. Merits and drawbacks of extant lane changing safety warning methods are discussed. Finally, a conclusion for universal problems in lane changing models, impacts and safety warning methods is drawn by this text, moreover current research trends and situation in lane changing research field are shown, in order to enhance these researches.

Under weak acidic conditions, with haemoglobin (Hb) as a mimetic peroxidase, the oxidation reaction between H2O2and KI was catalysed by haemoglobin to form I2, which combined with excessive I−to form I3−. I3−then reacted with gold nanoparticles (AuNPs) to produce AuI2−, which decreased the resonance light scattering intensity of the gold nanoparticles at 575 nm. The resonance light scattering (RLS) intensity linearly decreased as the hydrogen peroxide concentration increased. On this basis, a new method for the resonance light scattering determination of hydrogen peroxide has been developed, and the optimum reaction conditions, influential factors and applications were investigated. Under the optimal reaction conditions, hydrogen peroxide concentrations of 3.25 × 10−7–2.60 × 10−5mol L−1were linearly correlated with the reduction in the resonance light scattering intensity (ΔI) with a correlation coefficient of 0.9992 and a detection limit of 2.48 × 10−7mol L−1. The new method has been applied to the analysis of hydrogen peroxide in milk samples with satisfactory results.

This paper presents a time domain generalized semi-analytical solution for the class-E outphasing power amplifier (OPA). Instead of assuming ideal voltage sources for active devices, the proposed class-E OPA model obtains the circuit elements by analytically solving the class-E equations for two mutually loaded active devices and numerical optimization. The analysis takes into account the dc-feed inductor, duty-cycle, switch resistances and the class-E conditions at certain outphasing angle. Three different topologies are deduced from the proposed generalized model. The design equations for the circuit elements are derived analytically using the given class-E conditions. The overall outphasing performance is obtained numerically, and the optimum design settings are determined by the desired output power and efficiency characteristics. It will be shown that the proposed class-E OPAs can ideally maintain high efficiency (>80%) at 12-dB power back-off with a static square-wave drive signal. With 5-MHz UMTS downlink signal, the ideal average efficiency is more than 80%. The spectral and modulation requirements are satisfied when the predistortion is applied to the class-E OPA. [ABSTRACT FROM AUTHOR]

•Event-triggered control method for the hybrid power supply of fuel-cell heavy-duty truck•Maintain the satisfactory regulation performance and reduce the redundant control actions significantly.•The critical load of the hydraulic system in the heavy-duty truck has been considered.•Comparison of event numbers and switching actions and operation in different variations.

With the deployment of vehicle-to-everything(V2X) communication technology, Denial-of-Service(DoS) attacks gradually pose potential threats for the truck platooning cyber–physical systems(TPCPS) due to disruption of information exchange in vehicular networks, resulting in instability of truck platooning and even traffic accidents. Motivated by this, the study proposes a resilient event-triggered control strategy to maintain the performance or stability of the TPCPS when DoS attacks happen. First, a resilient event-triggered mechanism is proposed to ensure that the onboard controller can receive and update status information in time after attack intervals, mitigating effect of the vehicle-to-vehicle(V2V) communication disruptions. Subsequently, the sufficient condition is derived which is to confine DoS attacks and makes a key role in maintaining the platoon's internal stability. To guarantee the consensus control performance of the TPCPS, the switched event-triggered controller is designed by the Lyapunov approach. The controller is expected to output corresponding control based on the updated status information in communication interval. Ultimately, the proposed strategy's effectiveness is validated through simulations. The proposed resilient event-triggered control strategy is shown to be able to effectively mitigate abnormalities in the TPCPS under DoS attacks, thus ensuring safe and comfortable driving. Compared with event-triggered sliding mode control, the proposed method achieves smaller inter-vehicle distances while ensuring stability, enhancing traffic efficiency. • A resilient event-triggered mechanism is proposed to handle DoS attacks. • Sufficient conditions to confine DoS attacks are derived by the Lyapunov method. • Simulations for DoS attacks are designed based on sufficient conditions. • The switched event-triggered controller is designed by the Lyapunov approach. [ABSTRACT FROM AUTHOR]

With the gradual development of autonomous driving and connected communication technology, urban road networks will be shared by a combination of Human-driven vehicles (HDVs) and Connected vehicles (CVs) for a long era. Besides, as the actual vehicle operator, the driver regulates the vehicle state with an accelerator, gear lever, or brake, whereas the stepwise acceleration process is overlooked in the literature. Motivated by this, comprehensive kinetic difference between the two types of vehicles, we propose a novel heterogeneous multi-phase traffic flow accounting for the HDVs and CVs to close this hole. In the section on linear stability analysis, the reductive perturbation approach was applied to figure out the stability norm of the new model. The findings show that, on the one hand, inhibiting traffic bottlenecks is positively impacted by the penetration rate of CVs and the number of preceding vehicles taken into account; on the other hand, by varying the headway between following vehicles, the multiple-phase transition occurs; consequently, the number of turning points in the optimal velocity function is the sole factor that affects the number of stages in multi-phase transitions. Subsequently, the modified Korteweg-de Vries (mKdV) equation corresponding to the new model is deduced to investigate the nonlinear phenomenon of traffic flow at the vicinity of the critical point, and the kink-antikink soliton wave solution by solving the above characteristic equation can describe the "stop-and-go" phenomenon in real traffic flow. Finally, the conclusion of the numerical experiments is consistent with the above theoretical analysis. This research can provide insight into the dynamic evolution of road traffic flow during the transition from HDVs to semi-CVs and eventually to CVs. [ABSTRACT FROM AUTHOR]

Objective: To study the effect of Modified Dachengqi decoction and Dachengqi decoction on gastrointestinal motility in dyspepsia mice. Method: Mice were randomly divided into 5 groups: control group (normal saline), model group (normal saline), Dachengqi decoction group (0.054 g kg -1), Modified Dachengqi decoction group (0.084g kg -1) and Motilium group (0.17 ml kg -1).Result:Compared with the normal group, the myenteric tension of small intestine increased significantly, and the small intestine propulsion rate decreased significantly (P < 0.05).Compared with the model group, the myenteric tension of small intestine in Dachengqi decoction group decreased significantly (P < 0.05), the myenteric tension of small intestine muscle in Modified Dachengqi decoction group decreased significantly, and the small intestinal propulsion rate increased significantly (P < 0.01). And in these two groups, the serum MDA levels decreased significantly, and the serum SOD activity increased significantly (P < 0.01).Conclusion: Modified Dachengqi decoction can improve gastrointestinal stagnation in dyspepsia mice, and its mechanism may be related to the regulation of oxidative stress in dyspepsia mice.