Differentiation in stem and leaf traits among sympatric lianas, scandent shrubs and trees in a subalpine cold temperate forest

The scandent shrub plant form is a variant of liana that has upright and self-supporting stems when young but later becomes a climber. We aimed to explore the associations of stem and leaf traits among sympatric lianas, scandent shrubs and trees, and the effects of growth form and leaf habit on variation in stem or leaf traits. We measured 16 functional traits related to stem xylem anatomy, leaf morphology and nutrient stoichiometry in eight liana, eight scandent shrub and 21 tree species co-occurring in a subalpine cold temperate forest at an elevation of 2600–3200 m in Southwest China. Overall, lianas, scandent shrubs and trees were ordered along a fast-slow continuum of stem and leaf functional traits, with some traits overlapping. We found a consistent pattern of lianas > scandent shrubs > trees for hydraulically weighted vessel diameter, maximum vessel diameter and theoretical hydraulic conductivity. Vessel density and sapwood density showed a pattern of lianas = scandent shrubs < trees, and lianas < scandent shrubs = trees, respectively. Lianas had significantly higher specific leaf area and lower carbon concentration than co-occurring trees, with scandent shrubs showing intermediate values that overlapped with lianas and trees. The differentiation among lianas, scandent shrubs and trees was mainly explained by variation in stem traits. Additionally, deciduous lianas were positioned at the fast end of the trait spectrum, and evergreen trees at the slow end of the spectrum. Our results showed for the first time clear differentiation in stem and leaf traits among sympatric liana, scandent shrub and tree species in a subalpine cold temperate forest. This work will contribute to understanding the mechanisms responsible for variation in ecological strategies of different growth forms of woody plants

Fractional Order Adaptive Fast Super-Twisting Sliding Mode Control for Steer-by-Wire Vehicles with Time-Delay Estimation

It is difficult to model and determine the parameters of the steer-by-wire (SBW) system accurately, and the perturbation is variable with complex and changeable tire–road conditions. In order to improve the control performance of the vehicle SBW system, an adaptive fast super-twisting sliding mode control (AFST-SMC) scheme with time-delay estimation (TDE) is proposed. The proposed scheme uses TDE to acquire the lumped dynamics in a simple way and establishes a practical model-free structure. Then, a fractional order (FO) sliding mode surface and a fast super-twisting sliding mode control structure were designed on the basic super-twisting sliding mode to ensure fast convergence and high control accuracy. Since the uncertain boundary information of the actual system is unknown, a novel adaptive algorithm is proposed to regulate the control gain based on the control errors. Theoretical analysis concerning system stability is given based on the Lyapunov theory. Finally, the effectiveness of the method is verified through comparative experiments. The results show that the proposed TDE-AFST-FOSMC control scheme has the advantages of model-free, fast response and high accuracy

An Indoor Navigation Algorithm Using Multi-Dimensional Euclidean Distance and an Adaptive Particle Filter

The inertial navigation system has high short-term positioning accuracy but features cumulative error. Although no cumulative error occurs in WiFi fingerprint localization, mismatching is common. A popular technique thus involves integrating an inertial navigation system with WiFi fingerprint matching. The particle filter uses dead reckoning as the state transfer equation and the difference between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall; if so, the weight is set to zero. For particles that do not cross the wall, considering the distance between current and historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of less trusted particles. This paper also proposes a multidimensional Euclidean distance algorithm to reduce WiFi fingerprint mismatching. Experimental results indicate that the proposed algorithm achieves high positioning accuracy

Research on Automatic Emergency Braking System Based on Target Recognition and Fusion Control Strategy in Curved Road

To address the issue of incorrect recognition in the automatic emergency braking (AEB) systems on curved roads, a target recognition model is proposed to obtain the road curvature and to calculate the relative lateral distance. Based on the information from the ego vehicle and the preceding vehicles, the accurate selection of the hazardous target is accomplished. After identifying the dangerous target, a control strategy based on the fusion algorithm is proposed, because the safety distance model and the Time-to-Collision (TTC) model both have their limitations and cannot ensure driving safety and comfort simultaneously. The TTC model is optimized according to the actual relative distance between two vehicles on curved roads, the graded warning strategy and braking intervention time are established by the TTC model. And then the graded braking strategy is designed according to the safety distance model. The simulation platform is built based on Carsim and Simulink for verification and analysis. The results demonstrate that the proposed AEB control strategy on curved roads can accurately and efficiently identify the target vehicles on curved roads, avoid false triggering issues, and improve the AEB system’s reliability. And effectively avoid collisions with target vehicles that are in the same lane, improving driving safety and comfort

Cross-Language End-to-End Speech Recognition Research Based on Transfer Learning for the Low-Resource Tujia Language

To rescue and preserve an endangered language, this paper studied an end-to-end speech recognition model based on sample transfer learning for the low-resource Tujia language. From the perspective of the Tujia language international phonetic alphabet (IPA) label layer, using Chinese corpus as an extension of the Tujia language can effectively solve the problem of an insufficient corpus in the Tujia language, constructing a cross-language corpus and an IPA dictionary that is unified between the Chinese and Tujia languages. The convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) network were used to extract the cross-language acoustic features and train shared hidden layer weights for the Tujia language and Chinese phonetic corpus. In addition, the automatic speech recognition function of the Tujia language was realized using the end-to-end method that consists of symmetric encoding and decoding. Furthermore, transfer learning was used to establish the model of the cross-language end-to-end Tujia language recognition system. The experimental results showed that the recognition error rate of the proposed model is 46.19%, which is 2.11% lower than the that of the model that only used the Tujia language data for training. Therefore, this approach is feasible and effective

Spatiotemporal Patterns in pCO2 and Nutrient Concentration: Implications for the CO2 Variations in a Eutrophic Lake

Lakes are considered sentinels of terrestrial environmental change. Nevertheless, our understanding of the impact of catchment anthropogenic activities on nutrients and the partial pressure of carbon dioxide (pCO2, an important parameter in evaluating CO2 levels in water) is still restrained by the scarcity of long-term observations. In this study, spatiotemporal variations in nutrient concentrations (total nitrogen: TN, total phosphorus: TP, nitrate: NO3&minus;&ndash;N, and ammonium: NH4+&ndash;N) pCO2 in Taihu Lake were analyzed from 1992 to 2006, along with the gross domestic product (GDP) and wastewater discharge (WD) of its catchment. The study area was divided into three zones to characterize spatial heterogeneity in water quality: the inflow river mouth zone (Liangxi River and Zhihugang River), transition zone (Meiliang Bay), and central Taihu Lake, respectively. It is abundantly obvious that external nutrient inputs from the catchment have a notable impact on the water parameters in Taihu Lake, because nutrient concentrations and pCO2 were substantially higher in the inflow river mouth zone than in the open water of Meiliang Bay and central Taihu Lake. The GDP and WD of Taihu Lake&rsquo;s catchment were significantly and positively correlated with the temporal variation in nutrient concentrations and pCO2, indicating that catchment development activities had an impact on Taihu Lake&rsquo;s water quality. In addition, pCO2 was negatively correlated with chlorophyll a and the saturation of dissolved oxygen, but positively correlated with nutrient concentrations (e.g., TN, TP, and NH4+&ndash;N) in inflow river mouth zone of Taihu Lake. The findings of this study reveal that the anthropogenic activities of the catchment not only affect the water quality of Taihu Lake but also the CO2 concentrations. Consequently, catchment effects require consideration when modeling and estimating CO2 emissions from the extensively human-impacted eutrophic lakes

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder

As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features

Input Flux and the Risk of Heavy Metal(Loid) of Agricultural Soil in China: Based on Spatiotemporal Heterogeneity from 2000 to 2021

Identifying the current status of the heavy metal(loid) input of agricultural soils is vital for the soil ecological environment of agricultural-producing areas. Most previous studies have typically carried been out in small regions with limited sampling sites, which is insufficient to reveal the overall status of China. This study reviewed publications from over the past 20 years and calculated the input fluxes of heavy metal(loid)s in agricultural soil via atmospheric deposition, fertilizer, manure, and irrigation in different regions of China based on spatiotemporal heterogeneity using a meta-analysis, providing more accurate and reliable results. It was found that the heavy metal(loid) input flux of atmospheric deposition in China is large, while that of fertilizer and manure is relatively low compared to Europe. The major sources of As, Cd, Cr, Ni, and Pb entering the soil was atmospheric deposition, which accounted for 12% to 92% of the total input. Manure was responsible for 19% to 75% of the Cu and Zn input. Cd is the element presenting the most significant risk to the environment of agricultural soils in China and its safety limit will be reached within 100 years for most regions. The region we need to be concerned about is Huang-Huai-Hai due to its comprehensive pollution