Wind energy is considered as one of the most prominent sources of energy for sustainable development. This technology is of interest owing to its capability to produce clean, eco-friendly, and cost-effective energy for small-scale users and rural areas where grid power availability is insufficient. Wind power generation has developed rapidly in the past decade and is expected to play a vital role in the economic development of countries. Therefore, studying dominant economic factors is crucial to properly approach public and private financing for this emerging technology, as industrial growth and energy demands may outpace further economic studies earlier than expected. In this study, a strategy-focused method for performing economic analysis on wind energy based on financial net present value, levelized cost of energy, internal rate of return, and investment recovery period is presented. Numerical and simulation results depict the most optimal and economical system from a 3 and a 10 kW wind energy conversion system (WECS). Moreover, the aforementioned criteria are used to determine which WECS range is the most suitable investment with the shortest payback period. Finally, an economically viable and profitable wind energy system is recommended.

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Background Over the past 20 years, excessive antibiotic use has led to serious antimicrobial resistance (AMR) worldwide, and the phenomenon is particularly serious in China. To this end, the Chinese health sector took a series of measures to promote rational antibiotic use. In this study, to reveal the impact of policies on antibiotic use, we explored the long-term trend and patterns of antibiotic use at public health care institutions from 2012 to 2020 in northwest China, taking Gansu Province as an example. Methods Antibiotic procurement data were obtained from the provincial centralized bidding procurement (CBP) platform between 2012 and 2020. Antibiotic use was quantified using the Anatomical Therapeutic Chemical (ATC)/defined daily doses (DDD) methodology and standardized using the DDD per 1000 inhabitants per day (DID). Twelve relevant quality indicators were calculated for comparison with the European Surveillance of Antimicrobial Consumption (ESAC) project monitoring results. Results Total antibiotic use increased from 18.75 DID to 57.07 DID and then decreased to 19.11 DID, a turning point in 2014. The top three antibiotics used were J01C (beta-lactam antibacterials, penicillins), J01F (macrolides, lincosamides and streptogramins), and J01D (other beta-lactam antibacterials, cephalosporins), accounting for 45.15%, 31.40%, and 11.99% respectively. The oral antibiotics used were approximately 2.5 times the parenteral antibiotics, accounting for 71.81% and 28.19%, respectively. Different use preferences were shown in public hospitals and primary health care centres (PHCs), and the latter accounted for more than half of total use. The absolute use of all classes of antibiotics in Gansu is almost higher than any of the 31 European countries included in the ESAC, but the relative use of some focused antibiotics is lower than theirs. Conclusions The intervention policies of the health department reduced antibiotic use in Gansu Province, but the proportion of broad-spectrum and parenteral antibiotics was still high. It is necessary to further improve the quality of antibiotic prescriptions and pay more attention to the rationality of antibiotic use in PHCs.

Aiming at the problem of alignment deviation between shaft and hole caused by accumulated error in the assembly process of brake disc shaft hole, a trajectory planning method for compensating accumulated error is proposed. First, the path before the error is planned, and then the second compensation path is planned between the error position and the actual target point, so as to realize the accurate assembly of the brake disc. In this paper, the IRB 1410 robot is taken as the research object, and its kinematic model is established by using the improved D-H parameter method. The joint space quintic B-spline interpolation method was used to carry out trajectory planning, and the improved particle swarm optimization algorithm was introduced to solve the optimization. The optimal time and smooth trajectory were expected to be obtained. Then, using MATLAB software to simulate, compared with the nonoptimized trajectory, not only is the trajectory running time reduced from 8.6 s to 6.7 s but also the maximum joint change angle of each joint is reduced, which proves that the algorithm has optimization effect on trajectory running time and stability. Finally, the accuracy verification experiment of the algorithm is carried out, and the error between simulation and experiment is less than 6%, which shows the effectiveness of the method. This research provides a theoretical basis for improving the responsiveness and stability of brake disc assembly.

Autonomous navigation in narrow indoor environments such as indoor factory, warehouse and laboratory environments, and so on requires higher flexibility and navigation accuracy of the vehicle. This article presents an autonomous navigation method for four-wheel steering vehicle which combines extended Kalman filtering (EKF) and rapidly-exploring random tree (RRT) to improve the precision and flexibility of autonomous navigation of the vehicle in narrow indoor environments. The four-wheel steering model was established by the key parameters such as shape size and minimum angle of rotation of the experimental vehicle. Considering the problem that the uncertainty of pose estimation increases with time during autonomous navigation, an error model is schemed by adding noise to the output terminal of the analog odometer sensor. In order to suppress the accumulation of the uncertainty and keep it stable for a long time, the prediction and update steps of Kalman filter are introduced to filter the error. Then, the simultaneous positioning and mapping are established. Based on accurate positioning, a set of driving paths to reach the target is generated by RRT sampling algorithm. The simulation results show that positioning uncertainty remains stable over time, which verifies the effectiveness of the method. The overall positioning percentage error is 0.21%. Compared with traditional dead reckoning algorithm, the positioning accuracy is improved by 73.1% and the vehicle flexibility is increased by 68.6%. The four-wheel steering vehicle can find an ideal trajectory in narrow indoor environments, which assures the efficiency of the autonomous navigation and the traveling quality of the navigation route. Finally, the experimental results are consistent with the simulation results, which further verifies the effectiveness of the proposed algorithm.