A bilevel optimal motion planning (BOMP) model with application to autonomous parking

In this paper, we present a bilevel optimal motion planning (BOMP) model for autonomous parking. The BOMP model treats motion planning as an optimal control problem, in which the upper level is designed for vehicle nonlinear dynamics, and the lower level is for geometry collision-free constraints. The significant feature of the BOMP model is that the lower level is a linear programming problem that serves as a constraint for the upper-level problem. That is, an optimal control problem contains an embedded optimization problem as constraints. Traditional optimal control methods cannot solve the BOMP problem directly. Therefore, the modified approximate Karush-Kuhn-Tucker theory is applied to generate a general nonlinear optimal control problem. Then the pseudospectral optimal control method solves the converted problem. Particularly, the lower level is the $J_2$-function that acts as a distance function between convex polyhedron objects. Polyhedrons can approximate vehicles in higher precision than spheres or ellipsoids. Besides, the modified $J_2$-function (MJ) and the active-points based modified $J_2$-function (APMJ) are proposed to reduce the variables number and time complexity. As a result, an iteirative two-stage BOMP algorithm for autonomous parking concerning dynamical feasibility and collision-free property is proposed. The MJ function is used in the initial stage to find an initial collision-free approximate optimal trajectory and the active points, then the APMJ function in the final stage finds out the optimal trajectory. Simulation results and experiment on Turtlebot3 validate the BOMP model, and demonstrate that the computation speed increases almost two orders of magnitude compared with the area criterion based collision avoidance method

The Measurement of rho‐independent Transcription Terminator Efficiency

The purpose of this RFC is to provide standard methodology for the measurement of the absolute strength of terminators in bacteria. Because we have characterized the performance of terminator in E. coli and used a simple equation model, it can be expressed in PoPS

Research on Metadata Management System of Linkage Service of Scientific Data and Scientific Literature

In the data-intensive scientific research environment, the linkage of scientific data and scientific literature forms a complete body of scientific content. The literature and data serve scientific research together, which have become a hot issue of scientific research organizations. Starting from the metadata description elements of scientific data and scientific literature, this paper summarizes and analyses the association models of author association, keyword association and subject category association based on metadata description. On this basis, this paper describes the metadata management system architecture and system functions of linkage service of scientific data and scientific literature, providing some references for the relevant researchers

Preparation of high-resolution micro/nano dot array by electrohydrodynamic jet printing with enhanced uniformity

Abstract The high-resolution array is the basic structure of most kinds of microelectronics. Electrohydrodynamic jet (E-Jet) printing technology is widely applied in manufacturing array structures with high resolution, high material compatibility and multi-modal printing. It is still challenging to acquire high uniformity of printed array with micro-nanometer resolution, which greatly influences the performance and lifetime of the microelectronics. In this paper, to improve the uniformity of the printed array, the influence of each parameter on the uniformity of the E-jet printed dot array is studied on the cobuilt NEJ-E/P200 experimental platform, finding the applied voltage plays the most important role in maintaining the uniformity of the printed array. By appropriately adjusting the printing parameters, the dot arrays with different resolutions from 500 pixels per inch (PPI) to 17,000 PPI are successfully printed. For arrays below and over 10,000 PPI, the deviations of the uniformity are within 5% and 10% respectively. In this work, the dot array over 15,000 PPI is first implemented using E-jet printing. The conclusions acquired by experimental analysis of dot array printing process are of great importance in high resolution array printing as it provides practical guidance for parameters adjustment

Prediction modelling framework comparative analysis of dissolved oxygen concentration variations using support vector regression coupled with multiple feature engineering and optimization methods: A case study in China

Dissolved oxygen (DO) is an essential indicator for assessing water quality and managing aquatic environments, but it is still a challenging topic to accurately understand and predict the spatiotemporal variation of DO concentrations under the complex effects of different environmental factors. In this study, a practical prediction framework was proposed for DO concentrations based on the support vector regression (SVR) model coupling multiple intelligence techniques (i.e., four data denoising techniques, three feature selection rules, and four hyperparameter optimization methods). The holistic framework was tested using a data matrix (17,532 observation data in total) of 12 indicators from three vital water quality monitoring stations of the longest inter-basin water diversion project in the world (i.e., the Middle-Route of the South-to-North Water Diversion Project of China), during the year 2017 to 2020 period. The results showed that the framework we advocated for could successfully and accurately predict DO concentration variations in different geographical locations. The model used the “wavelet analysis–LASSO regression–random search–SVR” combination of the Waihuanhe station has the best prediction performance, with the Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2) values of 0.251, 0.063, 0.190, and 0.911, respectively. The combined methods using feature selection and hyperparameter optimization techniques can significantly promote the robustness and accuracy of the prediction model and can provide a new universal and practical way of investigating and understanding the environmental drivers of DO concentration variations. For the water quality management department, this proposed comprehensive framework can also identify and reveal the key parameters that should be concerned and monitored under different environmental factors change. More studies in terms of assessing potential integrated water quality risk using multi-indicators in mega water diversion projects and/or similar water bodies are required in the future

Rice Identification Under Complex Surface Conditions with CNN and Integrated Remote Sensing Spectral-Temporal-Spatial Features

Accurate and effective rice identification has great significance for the sustainable development of agricultural management and food secu-rity. This paper proposes an accurate rice identification method that can solve the confused problem between fragmented rice fields and the surroundings in complex surface areas. The spectral, temporal, and spatial features extracted from the created Sentinel-2 time series were integrated and collaboratively displayed in the form of visual images, and a convolutional neural network model embedded with integrated information was established to further mine the key information that distinguishes rice from other types. The results showed that the overall accuracy, precision, recall, and F1-score of the proposed method for rice identification reached 99.4%, 99.5%, 99.5%, and 99.5%, respectively, achieving a better performance than the support vector machine classifier. Therefore, the proposed method can effectively reduce the confusion between rice and other types and accurately extract rice distribution information under complex surface conditions. © 2023 American Society for Photogrammetry and Remote Sensing

The effect of corticosteroids on mortality of patients with influenza pneumonia: a systematic review and meta-analysis

Abstract Background The effect of corticosteroids on clinical outcomes in patients with influenza pneumonia remains controversial. We aimed to further evaluate the influence of corticosteroids on mortality in adult patients with influenza pneumonia by comparing corticosteroid-treated and placebo-treated patients. Methods The PubMed, Embase, Medline, Cochrane Central Register of Controlled Trials (CENTRAL), and Information Sciences Institute (ISI) Web of Science databases were searched for all controlled studies that compared the effects of corticosteroids and placebo in adult patients with influenza pneumonia. The primary outcome was mortality, and the secondary outcomes were mechanical ventilation (MV) days, length of stay in the intensive care unit (ICU LOS), and the rate of secondary infection. Results Ten trials involving 6548 patients were pooled in our final analysis. Significant heterogeneity was found in all outcome measures except for ICU LOS (I 2 = 38%, P = 0.21). Compared with placebo, corticosteroids were associated with higher mortality (risk ratio [RR] 1.75, 95% confidence interval [CI] 1.30 ~ 2.36, Z = 3.71, P = 0.0002), longer ICU LOS (mean difference [MD] 2.14, 95% CI 1.17 ~ 3.10, Z = 4.35, P < 0.0001), and a higher rate of secondary infection (RR 1.98, 95% CI 1.04 ~ 3.78, Z = 2.08, P = 0.04) but not MV days (MD 0.81, 95% CI − 1.23 ~ 2.84, Z = 0.78, P = 0.44) in patients with influenza pneumonia. Conclusions In patients with influenza pneumonia, corticosteroid use is associated with higher mortality. Trial registration PROSPERO (ID: CRD42018112384)