As a raw material for sugar, ethanol, and energy, sugarcane plays an important role in China’s strategic material reserves, economic development, and energy production. To guarantee the sustainable growth of the sugarcane industry and boost sustainable energy reserves, it is imperative to forecast the yield in the primary sugarcane production regions. However, due to environmental differences caused by regional differences and changeable climate, the accuracy of traditional models is generally low. In this study, we counted the environmental information and yield of the main sugarcane-producing areas in the past 15 years, adopted the LSSVM algorithm to construct the environmental information and sugarcane yield model, and combined it with WCA to optimize the parameters of LSSVM. To verify the validity of the proposed model, WCA-LSSVM is applied to two instances based on temporal differences and geographical differences and compared with other models. The results show that the accuracy of the WCA-LSSVM model is much better than that of other yield prediction models. The RMSE of the two instances are 5.385 ton/ha and 5.032 ton/ha, respectively, accounting for 7.65% and 6.92% of the average yield. And the other evaluation indicators MAE, R², MAPE, and SMAPE are also ahead of the other models to varying degrees. We also conducted a sensitivity analysis of environmental variables at different growth stages of sugarcane and found that in addition to the main influencing factors (temperature and precipitation), soil humidity at different depths had a significant impact on crop yield. In conclusion, this study presents a highly precise model for predicting sugarcane yield, a useful tool for planning sugarcane production, enhancing yield, and advancing the field of agricultural production prediction.

Force information is crucial to teleoperation systems, which allows experts to perform tasks safely and reduces potential risks. However, due to the limitations of robot size and working environment, it is difficult to obtain contact force information through traditional force sensors. Therefore, this article proposes a graph robot network force observer, which can achieve sensorless force estimation without solving the robot dynamics. First, different association graphs are constructed according to the relative spatial position of the robot joints, establishing information transmission channels between each joint. Second, a neural reinforcement factor is introduced from robot joint motion information to enhance the feature information of different association subgraphs. Finally, a spatial and temporal feature fusion module is developed to extract and fuse both temporal and spatial information, which outputs the force information on the robot end effector along axes X, Y, and Z. Experiments in different environments and ablation experiments are carried out to verify the stability and generalization ability of the model. The experimental results show that the proposed force observer can accurately achieve the contact force between the robot and the object, and the proposed force observer achieves state-of-the-art performance compared with other algorithms.

Surgical robots are increasingly utilized in medicine for their reliability and convenience. An accurate kinematic model is essential for precise robot control and enhanced surgical safety. However, the high nonlinearity and computational complexity of kinematics pose significant challenges to traditional numerical methods. This study designs a surgical robotic arm and establishes the motion mapping relationship between the joint space and the end-effector workspace. Subsequently, a hybrid kinematic estimation model based on deep pyramid convolutional neural network (DPCNN) is proposed, which integrates data sampling and an attention mechanism to improve computational accuracy. The Latin hypercube sampling technique is used to improve the uniformity of dataset sampling, and the triplet-fusion self-attention mechanism (TFSAM) is employed for multi-scale feature information. Experimental results show that the TFSAM-DPCNN model achieves coefficient of determination (R²) values exceeding 0.99 across all testing scenarios. Compared with other models, the proposed model reduced the root mean square error (RMSE) by up to 81.34%, exhibiting superior performance. Furthermore, the developed 3D simulation platform validates the effectiveness of the proposed model. This study offers a robust solution for multi-degree-of-freedom robot modeling, with potential applications across a range of robotic motion control systems.

The high cetane number (CN), low viscosity, and low surface tension of n-butyl ether (DBE) are considered an attractive alternative fuel to diesel. The pre-injection strategy is effective in reducing NO X emissions from oxygenated fuels. Therefore, this paper studies the effect of DBE-diesel mixture with coupled pre-injection rate on the combustion and emission characteristics of diesel engines. The effects of DBE and pre-injection rate on engine in-cylinder pressure (ICP) and heat release rate (HRR), maximum in-cylinder temperature (MICP), CA50, brake thermal efficiency (BTE), conventional emissions, and particulate emissions were mainly analyzed. The results showed that, compared with D100, with the increase of DBE blending ratio, the peak ICP increased, the peak HRR advanced and increased in the pre-injection stage, the peak HRR increased in the main injection stage, the MICP increased, the CA50 shortened, and the BTE decreased. In terms of conventional emissions, compared with D100, BD40 under single-injection (SI) conditions reduces CO emissions by 17.9% but increases THC emissions by 11.8% and NO X emissions by 16%. The total particle number concentration (TPNC) and accumulation particle number concentration (APNC) increased gradually with increasing pre-injection rate and the particle number concentration of D_p<20nm was minimized at a 10% pre-injection rate. Compared with D100, blending DBE helps to reduce the TPNC, APNC, and nucleation particle number concentration (NPNC), but increases the particle number concentration at D_p<20nm. Blending of DBE and coupling with a small pre-injection rate was effective in reducing TPNC and the geometric mean diameter of particles (GMD) compared to D100.