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Morphology and structure of plants are the results from the joint action of itself and the external environment. In order to analyze the correlation between tree shape structure and leaf trait of Bombax ceiba, and to explore the influence of topographic, soil, and meteorological elements on its growth shape, and to reveal its growth strategy and adaptation mechanism, we studied 230 B. ceiba trees in Xishuangbanna National Nature Reserve of Yunnan Province and measured six tree shape structure indexes, eight leaf trait indexes, and habitat elements of regional topographic, meteorology, and soil. The effects and explantation of habitat elements on tree shape structure and leaf trait of B. ceiba were analyzed by structural equation model and variation decomposition. The results were as follows: (1) There were correlations among the indexes of tree shape structure and among the indexes of leaf trait (P < 0.05). (2) The observed indexes of habitat elements had a strong influence on the growth indexes of B. ceiba leaves. The mean annual precipitation, slope and average temperature had a higher interpretation rate on the growth indexes of B. ceiba leaves, and were the dominant habitat indexes affecting the growth of B. ceiba. (3) According to the standardized path coefficient, the three habitat elements had the same effects on tree shape structure and leaf trait of B. ceiba, and all of them were in the order of topographic element>meteorological element>soil element. (4) The superposition of three habitat elements explained that tree shape structure and leaf trait were 43.5% and 12.3%, respectively, indicating that the response of tree shape structure to habitat environment was greater than that of leaf trait to environment. The results of this study preliminarily explore the adaptation strategies of different growth indexes to environmental elements of B. ceiba, which provides theoretical basis and practical reference for the cultivation and propagation of B. ceiba in heterogeneous habitats and efficient management of B. ceiba plantation.

Abstract [Objective] This study aims to explore the mechanism of flower color formation in rose and to provide references and receptor cultivars for flower breeding by quantitative evaluation and correlation analysis of the physicochemical factors affecting coloration of rose petals. There is of great significance for exploring the mechanism of flower color formation and germplasm innovation. [Methods] 8 cultivars of rose with different flower colors were used as experimental materials to measure and compare the physicochemical indexes such as petal color parameter, pH value of petal cells, metal ion content, total anthocyanin content, total flavonoids content, and total chlorophyll content, etc., and the anthocyanin components were quantitatively analyzed. [Results] (1) There were significant differences between the physicochemical factors of different flower color of roses, among which the factors such as pH value of petal cells, Fe3+ , Ca2+ and Al3+ content, total anthocyanin content, and total flavonoid content were closely related to the formation of petal color, but the changes in the content of total flavonoids and total anthocyanin played direct roles, and the factors such as metal ions and pH value of petal cells affected the flower color by changing the structure of anthocyanidin. (2) The components of anthocyanins in petals of different roses were different. Among them, cyanidin-3,5-diglucoside dominated in the roses and mainly regulated the formation of purplish-red flowers; followed by pelargonidin-3,5-diglucoside, which mainly regulated the formation of orange and red flowers. Yellow flowers contained very little anthocyanin and were mainly regulated by carotenoids. Orange flowers were dually regulated by both anthocyanin and carotenoids, while white flowers contained almost no anthocyanin. Rutin was abundant in all 8 rose cultivars, but there was no significant correlation with petal formation. (3) Cluster analysis of anthocyanin components in 8 rose petals revealed that the pink-purple cultivar ‘Lavender Flower Circus’ was always clustered in a separate group with high rutin content and small anthocyanin content. [Conclusion] The type and content of anthocyanins directly affect flower color of rose, and flavonoids assist the formation of anthocyanin. Factors such as pH value of petal cells, Fe3+ , Ca2+ and Al3+ content indirectly affect flower color. Among the 8 selected cultivars of rose, the pink-purple cultivar ‘Lavender Flower Circus’ is the most suitable recipient cultivar for flower color improvement due to its excellent anthocyanin composition and high pH value in petal cells.

The exothermic behaviors of butanol, butyraldehyde, butanone, and butyric acid with nitric acid were studied by adiabatic accelerating rate calorimeter(ARC), and the test results were compared and analyzed, and the system risk was evaluated by THI index method. The results show that under the condition of the zero oxygen balance, butyraldehyde-HNO3 system has the most obvious self-exothermic. When the butyraldehyde content is only 0.14 g, the adiabatic temperature of the system rises to 118.4 ℃ and the heat release amount is 776.6 J/g; butanol-HNO3 system begins to emit heat at about 40.2 ℃, and the highest temperature in the exothermic stage of self-reaction reaches 145.7 ℃ and the maximum pressure reaches about 34 bar(1 bar=105 Pa). Butanone-HNO3 system releases heat from 50 ℃ to 136 ℃, and the amount of heat released is 564.0 J/g. No exothermic point is observed in butyric acid-HNO3 system in the temperature range of less than 140.0 ℃. With the increase of nitric acid concentration(2.0→6.0 mol/L), the risk of butanol-HNO3 system increases from medium to high level. The risks of reaction systems of butanol, butyraldehyde, butanone and butyric acid with 4.0 mol/L HNO3 are at higher, higher, middle and middle levels.

The digital and programmable metasurfaces, as opposed to conventional metasurfaces, offer a more sophisticated method of collaborating information and physics, showcasing several real-time controls to electromagnetic (EM) ways in succinct ways. In this work, we propose a multi-frequency amplitude-programmable (MFAP) metasurface with multiple frequency channels to enhance the presentation and manipulation of EM data. With this metasurface, the reflected amplitudes can be simultaneously and independently encoded between high (digit “1”) and low (digit “0”) levels. The amplitude code is unique, which exhibits both reflection coefficients and radiation patterns to allow for flexible multi-functional EM operations with frequency. For instance, the MFAP metasurface can be used to design innovative communication systems by transmitting various EM signals individually across the channels in time domain. It is also possible to carry out multi-bit transmissions by mixing these frequency channels. By introducing complex coding patterns in space domain, it is possible to manipulate EM powers with greater precision. A square-split ring meta-atom that can achieve stable single-frequency amplitude control and multi-frequency 1 bit amplitude-programmable features is described as a proof-of-concept. Varactors loaded on metallic structures of various sizes are switched between operating states to modify the amplitude codes at each frequency channel. The suggested MFAP metasurface’s validity is confirmed by simulations and measurements from a dual-channel MFAP metasurface prototype.

In this article, the authors propose a method to identify the bridge damage using a backpropagation (BP) neural network. It uses bridge vibration response to solve the accuracy of bridge damage. A particle swarm optimization algorithm based on chaotic mutation is adopted to perform chaotic mutation operations and make the group jump out of the local optimum. CPSO (particle swarm optimization algorithm based on chaotic variation) algorithm can make up for the BP neural network model, easy to fall into the shortcomings of local optima, so the author will combine the two algorithms and discuss the environmental data of the bridge. Establishing a finite element model of the bridge through actual analysis, through data comparison, comparing the frequencies of the intact stages with the frequencies of the damaged stages, and verifying the neural network with random samples, for the degree of bridge damage, we get the root mean square error msemse and the correlation coefficient r. The result shows that the root mean square error mse=0.003196mse=0.003196, and the correlation coefficient r=0.9654r=0.9654. There are only a few individual points; it seems that the relative error is relatively large. The rest of the fit is basically the same; it can meet the factors of vibration through the environment and perform damage identification for the structural damage monitoring of the bridge. Using the BP neural network model optimized by chaotic particle swarms, combined with the modal analysis of environmental vibration, it can be used in the monitoring of the health structure of the bridge, plays a certain recognition effect, and provides a new technical idea.

ABSTRACTMaking medication prescriptions in response to the patient's diagnosis is a challenging task. The number of pharmaceutical companies, their inventory of medicines, and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload. To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient, researchers have exploited electronic health records (EHRs) in automatically recommending medication. In recent years, medication recommendation using EHRs has been a salient research direction, which has attracted researchers to apply various deep learning (DL) models to the EHRs of patients in recommending prescriptions. Yet, in the absence of a holistic survey article, it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges. To fill this research gap, this survey reports on state-of-the-art DL-based medication recommendation methods. It reviews the classification of DL-based medication recommendation (MR) models, compares their performance, and the unavoidable issues they face. It reports on the most common datasets and metrics used in evaluating MR models. The findings of this study have implications for researchers interested in MR models.

CO2 emissions from heavy-duty vehicles are an important source of greenhouse gases in the transportation sector. However, there is a lack of in-depth research on the characteristics of CO2 emission factors of multiple types of heavy-duty vehicles. The paper conducts a study on the characteristics of carbon dioxide emission factors of multi-type heavy-duty vehicles, with a focus on tractors, heavy-duty trucks, and dump trucks. A method based on C-WTVC for VSP-Bin interval division is proposed to establish a unified bin interval for heavy-duty vehicles, and calculate the CO2 emission factors of different types of heavy-duty vehicles. The study indicates that the carbon dioxide emission factors of tractors are mainly distributed in suburb and highway intervals. The carbon dioxide emission factors of heavy-duty trucks are distributed across three intervals. The CO2 emission factors of dump trucks are concentrated in suburb intervals.

The emission factors of greenhouse gases from motor vehicles are of significant importance for low-carbon and environmentally friendly initiatives. This paper focuses on greenhouse gas emission factors of heavy-duty vehicles. The multi-dimensional Bin interval mode are proposed, which considers multiple parameters such as vehicle speed, acceleration, torque, and rotational speed. Based on this, the emission characteristics of greenhouse gases from heavy-duty vehicles. are investigated under different parameter conditions. The results indicate that as the VSPA value or the engine torque increases, the CO2 emission factor also increases accordingly. The CO2 emission factor is lower at medium to high rotational speeds, while it tends to increase at low rotational speeds. The research findings provide support for monitoring and evaluating greenhouse gas emissions and offer scientific guidance for formulating specific carbon reduction policies, emission targets, and plans.

This paper proposes a multi-scale emission factor dataset constructing method. Firstly, actual road driving and pollutant emission data of heavy-duty trucks are obtained. Subsequently, a method for dividing multi-scale Bin intervals based on engine load, vehicle speed, and acceleration is proposed to establish suitable multi-scale Bin intervals for the driving characteristics of heavy-duty trucks. Finally, based on the proposed method, an emission factor dataset highlighting typical driving characteristics of heavy-duty trucks is constructed. The research findings will provide government regulatory authorities with more microscopic and multi-scale guidance on motor vehicle emissions.

In order to solve the problem of roadway surrounding rock support caused by roadway floor heave along the filling body, taking Lu’an Changcun Coal Mine as the engineering background, the advantages and disadvantages of two control schemes such as floor reinforcement and floor pressure relief are analyzed by using FLAC3D numerical simulation software, as well as the deformation law and support effect of roadway surrounding rock under different parameters. The research shows that under the reinforcement scheme, when the floor and slope angle are reinforced together, the floor heave in the excavation process of the working face (i.e. 5 m ahead of the “secondary roadway excavation” and “secondary mining” working face) is reduced by 2.48 times and 2.60 times respectively. Therefore, joint reinforcement of the floor and slope angle is an effective way to control the floor heave. When the pressure relief groove controls the roadway floor heave, it has a great correlation with the slotting depth. When the slotting depth is 2.0 m, compared with the case without slotting, the floor heave in the excavation and mining process of the working face is reduced by 1.82 and 2.49 times respectively. However, floor slotting has the opposite effect on the deformation control of roadway roof and solid coal wall. According to the actual engineering situation of S511 fully mechanized top coal caving face in Changcun Coal Mine, Lu’an, it is suggested to control the bottom drum by means of bottom plate pressure relief and grooving with a grooving depth of 1.5 m.

Full-space metasurfaces demonstrate powerful abilities in manipulating electromagnetic (EM) waves, but most of them are usually single band. Here, a multiband bidirectional digital coding metasurface is proposed for multiple tasks, which can achieve completely different functions in up to six frequency bands when the EM waves are incident from the front and back of the metasurface. As a proof-of-concept, we design and fabricate a dual-band full-space metasurface with integrated functions of reflection, transmission, holographic imaging, and vortex-beam forming, and a six-band full-space metasurface with completely independent holographic imaging functions at different frequency bands. Simulated and measured results are in good agreements with the theoretical predictions, verifying the good performance of the proposed multitask bidirectional digital coding metasurface.

Hypoxia is a common pathological condition after spinal cord injury. Oestrogen-related receptor alpha (ERRα), as a key regulator of energy metabolism and mitochondrial functions, plays an important role in maintaining cell homeostasis. However, its role in hypoxic spinal microglia has not been fully elaborated. This study investigated the receptor’s activity when these cells are hypoxic and used as an in vitro model.

Peaking carbon dioxide emissions and achieving carbon neutrality is a major strategic decision taken by China and it brings significant pressure and challenges to the transport sector. Peaking carbon emissions is an important direction for the highquality development and green transformation of the transport sector. This study analyzes the status quo of green development and carbon emission in China’s transport sector and identifies the challenges for achieving the carbon peak and carbon neutrality goals in the transport sector. The overall idea is to peak carbon emissions actively and steadily by implementing categorized policies, combining short- and long-term goals, controlling carbon emission increment, and adjusting the current emission structure. An overall path for carbon reduction in the transport sector at different stages is proposed. Furthermore, we summarize several key measures to achieve carbon peak and carbon neutrality in the transport sector: (1) optimizing the transport structure, (2) promoting the energy efficiency of transport equipment, (3) popularizing low-carbon transport equipment, (4) improving the traffic organizing efficiency, and(5) encouraging low-carbon travel modes.

The influence of acoustic radiation is considered in the prediction of noise attenuation effect of sound barrier, which provides a theoretical reference for further improving the insertion loss of sound barrier. Based on the theory of thin plate vibration, the vibration mode and natural frequencies of sound barrier under arbitrary boundary conditions are established by using two-dimensional beam function method, and the forced vibration response of the sound barrier is calculated based on the modal superposition method. MATLAB software (MathWorks Company, Natick, Massachusetts, USA) is used to calculate the natural frequencies and the radiated sound power level of the sound barrier, which indicated that the sound radiation caused by external excitation would significantly increase the sound pressure level at the received point, which should be considered as one of the influencing factors in the prediction of noise attenuation effect. The influence of diverse structural parameters on the radiated acoustic power is compared, providing an excellent reference for the design of sound barrier with low noise.

【Background and objective】 Concentration of mobile phosphorus (P) in soil affects not only plant growth but also water quality because of its potential leaching. Most studies on soil P focused on topsoil while overlooking subsoil which is equally important. The objective of this paper is to study the dynamics of mobile P at different soil depths and its driving forces in attempts to help improve P uptake by plants while in the meantime reducing its leaching. 【Method】 Column experiments filled with soil taken from Sanjiang plain in China were conducted with average volumetric soil water content controlled at 50% (M1), 75% (M2) and 100% (M3) respectively. In each treatment, we measured the changes in mobile phosphorus in the depths of 0~10 cm (topsoil) 10~20 cm (middle soil) and 20~30 cm (subsoil). We also analyzed its relationship with total organic carbon (TOC), total Fe, total Al and total Ca in soil solution; the experiments lasted 84 days. 【Result】 When soil water content was 50%, soil solution was not extractable from the topsoil and the middle soil, and only was limited water extracted from the subsoil. When soil water content exceeded 75%, soil solution was more mobile for extraction in which the P concentration in the middle soil was found to be the highest, 1.3~1.7 times that in the topsoil and 5.2~8.2 times that in the subsoil. The impact of soil water on P concentration varied with soil depth and was ranked in the following order: In the topsoil, M3 (1.27 mg/L) > M2 (0.82 mg/L), in the middle soil M3 (1.60 mg/L) > M2 (1.40 mg/L), and in the subsoil M3 (0.31 mg/L) > M2 (0.17 mg/L) >M1 (0.13 mg/L). A stepwise regression analysis indicated that P concentration in the soil solution was related to TOC, total Fe, total Al, and total Ca in soil solution, with the correlations varying with soil water and soil depth. These suggested that improving soil water management could shift dissolution and/or decomposition of P in different forms in the soil, and can thus bed used to manage soil P. Regardless of soil water content, P concentration in the soil was much higher than the critical P concentration, 0.1 mg/L, which could trigger eutrophication in surface water. 【Conclusion】 Increasing soil water could result in a quick release of P from soil to the overlying surface water, triggering eutrophication as a result. Care thus must be taken when temporally returning dry farmland to wetland.

A high-power three-level PWM rectifier predictive control algorithm without weight coefficients is proposed for the problem of complicated design of the weight coefficients of the cost function in the traditional predictive control algorithm. The Pareto optimal solution set for multi-objective satisfactory optimal control of current command tracking and midpoint potential regulation is calculated based on the introduction of a tolerance interval for the target tracking error. When the Pareto optimal solution set is an empty set, the current command tracking based on the control deviation degree measurement function and the midpoint potential are used to adjust the target cost function so as to eliminate the weight coefficient of the cost function in the traditional predictive control. When the Pareto optimal solution set is not an empty set, a new switching loss cost function is designed to achieve low switching frequency control of the rectifier. The simulation results have verified the effectiveness and dynamic performance of the algorithm.

To date, there have been no panoramic studies of the serum metabolome in feline mammary carcinoma. As the first such study, metabolomics techniques were used to analyse the serum of cats with these tumours. Three important metabolic pathways of screened differential metabolites closely related to feline mammary carcinomas were analysed to lay a theoretical basis for further study of the pathogenesis of these carcinomas.

An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions. Most existing approaches aim to learn new representations of samples in source and target domains by reducing the distribution discrepancy between domains while maximizing the covariance of all samples. However, they ignore subspace discrimination, which is essential for classification. Recently, some approaches have incorporated discriminative information of source samples, but the learned space tends to be overfitted on these samples, because they do not consider the structure information of target samples. Therefore, we propose a feature reduction approach to learn robust transfer features for reducing the distribution discrepancy between domains and preserving discriminative information of the source domain and the local structure of the target domain. Experimental results on several well-known cross-domain datasets show that the proposed method outperforms state-of-the-art techniques in most cases.

With the advent of 3D cameras, getting depth information along with RGB images has been facilitated, which is helpful in various computer vision tasks. However, there are two challenges in using these RGB-D images to help recognize RGB images captured by conventional cameras: one is that the depth images are missing at the testing stage, the other is that the training and test data are drawn from different distributions as they are captured using different equipment. To jointly address the two challenges, we propose an asymmetrical transfer learning framework, wherein three classifiers are trained using the RGB and depth images in the source domain and RGB images in the target domain with a structural risk minimization criterion and regularization theory. A cross-modality co-regularizer is used to restrict the two-source classifier in a consistent manner to increase accuracy. Moreover, an L2,1 norm cross-domain co-regularizer is used to magnify significant visual features and inhibit insignificant ones in the weight vectors of the two RGB classifiers. Thus, using the cross-modality and cross-domain co-regularizer, the knowledge of RGB-D images in the source domain is transferred to the target domain to improve the target classifier. The results of the experiment show that the proposed method is one of the most effective ones.

Reinforcement learning (RL) constitutes an effective method of controlling dynamic systems without prior knowledge. One of the most important and difficult problems in RL is the improvement of data efficiency. Probabilistic inference for learning control (PILCO) is a state-of-the-art data-efficient framework that uses a Gaussian process to model dynamic systems. However, it only focuses on optimizing cumulative rewards and does not consider the accuracy of a dynamic model, which is an important factor for controller learning. To further improve the data efficiency of PILCO, we propose its active exploration version (AEPILCO) that utilizes information entropy to describe samples. In the policy evaluation stage, we incorporate an information entropy criterion into long-term sample prediction. Through the informative policy evaluation function, our algorithm obtains informative policy parameters in the policy improvement stage. Using the policy parameters in the actual execution produces an informative sample set; this is helpful in learning an accurate dynamic model. Thus, the AEPILCO algorithm improves data efficiency by learning an accurate dynamic model by actively selecting informative samples based on the information entropy criterion. We demonstrate the validity and efficiency of the proposed algorithm for several challenging controller problems involving a cart pole, a pendubot, a double pendulum, and a cart double pendulum. The AEPILCO algorithm can learn a controller using fewer trials compared to PILCO. This is verified through theoretical analysis and experimental results.

The large increase in the on-road vehicle population in China has raised sustainability concerns regarding air pollution prevention, energy conservation, and climate change mitigation. Vehicle emission inventory is an irreplaceable tool to characterize the temporal and spatial distribution of the air pollutant and provide guidance to the policy makers with effective vehicle emission controls. This review paper reviewed two kinds of typical vehicle emission inventories. The top-down vehicle emission inventories is calculated based on the static datasets (e.g., vehicle population, vehicle kilometer traveled, and fuel consumption). These inventories could track historical emissions abatement progress and examine potential benefits from future regulations. The technological evolution in intelligent transportation systems have facilitated emission inventories to satisfy the increasing sophisticated management demand. The bottom-up link-level vehicle emission inventories are development based on the availability of the real-world traffic profiles. To simulate the temporal and spatial patterns with high-resolution, traffic demand model and machine learning methods are employed to elucidate traffic emissions.

Transportation is one of the important fields of carbon emission. Promoting the Carbon Peak in transportation industry is a key point to accelerate the green and low-carbon transformation and promote the high-quality development of the transportation industry. The transportation sector covers a wide range of fields, and there are many coordination departments. The local transportation authorities still lack a strong grasp in carrying out the Carbon Peak work. It is urgent to further improve the Carbon Peak system and mechanism in the transportation sector. This paper refines the current situation and characteristics of carbon emission in the transportation field of Inner Mongolia Autonomous Region, summarizes the progress of Carbon Peak in the transportation field, analyzes the constraints of the system and mechanism of Carbon Peak in the transportation field, and puts forward suggestions to further improve the system and mechanism of carbon emission reduction in the transportation field of Inner Mongolia Autonomous Region.

Due to the lack of air and traffic monitoring data in China, this study employed a remote sensing (RS) system and air quality monitoring sensors to investigate the air pollutant concentrations, traffic profiles and meteorological data in order to characterize roadside pollutant concentrations and identify their influence factors for a major road in Lanzhou. The results suggested that the temporal variations of concentrations of SO2, NO2, PM2.5, PM10 and CO peaked at 5:00-7:00 while O3 peaked at 17:00. The concentrations of roadside air pollutants were lower on weekdays than weekends except O3. The concentrations of PM2.5 and PM10 declined with the height of the monitoring sites increased, peaking at 2-meter height whereas SO2, NO2, CO, and O3 reached their highest concentrations at 4-meter height. The concentrations of roadside NO2 and CO show positive correlations with the volume of heavy-duty trucks (HDTs) and light-duty passenger vehicles (LDPVs), respectively. This study provided scientific support to the establishment of roadside air quality monitoring systems and the assessment of the environmental and health impact from vehicle emissions.

By considering the symbiosis between rest behavior with charging behavior during intercity traveling, the inconvenience of battery electric vehicles (BEVs) confined by the battery capacity can be relieved. This paper proposed a quantitative analysis method for revealing this phenomenon from an accessibility perspective. This proposed method involved a calculation model of intercity travel time for electric vehicle by considering the current configuration of electric vehicle charging facilities and energy consumption with travel speed. Based on the highway network and charging pile network in Beijing-Tianjin-Hebei region, a case study has been analyzed to verify the feasibility of the model. The results show that the accessibility performance of BEVs and fuel vehicles is close in Beijing Tianjin Hebei region, which has relatively perfect charging facilities. And by joint the charging behavior and rest behavior, there is almost no difference between BEV models with a large range and advanced charging technology and fuel vehicles in intercity travel. In addition, the charging facilities along the expressway in the region can basically satisfy the experience of intercity travel for BEVs, and further increasing the number of charging stations cannot significantly improve the accessibility of electric vehicles.

CFD, comprising multiple types of softwares, involves complex technological theories and is used to solve profound issues with a huge amount of calculation. Given the complexity and profundity, this study selects Fluent, OpenFOAM and Star-CCM+ as representatives to review the application of the CFD simulation technology in the vehicle exhaust dispersion models. Fluent, a commercial CFD software, started relatively early and has so far been the most widely used in the field of motor vehicle exhaust dispersion research; OpenFOAM, is open source and has a large number of models and algorithms. Apart from supporting pure model research, OpenFOAM has broad application prospects in coupling with the mesoscale model WRF to improve the accuracy of pollutant dispersion simulation; Star-CCM+, less applied in the research field of vehicle exhaust dispersion, focuses more on vehicle air conditioning system, vehicle radiator, refueling, vehicle aerodynamic noise source simulation and other related fields. The research provides theoretical basis and scientific reference for the application of CFD in the field of urban block-scale air quality research. In the future, with the rapid development of computer technology and the introduction of new theories and intelligent algorithms, CFD technology is very likely to achieve new breakthroughs and continue to enjoy even wider application in the research of motor vehicle exhaust dispersion.

According to characteristics of various modulation and demodulation modes, bit error rate of modulation modes including binary amplitude shift keying, binary frequency shift keying and binary differential phase shift keying under different demodulation modes were simulated and compared on the basis of characteristics of mine through-the-earth communication channel. The results show that the binary frequency shift keying coherent demodulation mode can reduce the bit error rate in transmission process of voice signal effectively, and improve reliability and accuracy of the through-the-earth communication with certain ability to resist noise and frequency selective fading, which is more suitable for modulation and demodulation of voice signal in mine through-the-earth communication.