Heat‐treated rapeseed was supplemented to indoor fed yaks in winter to test the effect on dry matter intake (DMI), body mass change, and meat quality. Sixteen 3‐year‐old yak steers (124 ± 15.3 kg) were divided randomly into two groups and were offered either heat‐treated rapeseed (HTR) or rapeseed meal (CONT). The yaks were allowed 14 days for adjustment and measurements were made over 120 d. There was no difference in DMI between groups (p = 0.67), but average daily gain tended to be higher (p < 0.056) and feed to gain ratio tended to be lower (p = 0.050) in HTR than in CONT yaks. Meat from HTR yaks was more tender (p = 0.006), had higher intramuscular fat (p = 0.013), and had lower cholesterol content (p = 0.009) than from CONT yaks. In addition, the atherogenic index was lower (0.37 vs. 0.43; p = 0.049), the PUFA:SFA ratio was higher (0.55 vs. 0.37; p = 0.049), and the n‐6:n‐3 (n‐6 PUFA to n‐3 PUFA) ratio was lower (2.76 vs. 4.78; p = 0.003) in HTR than in CONT yaks, which all favoured the HTR yaks. Meat from HTR yaks met human health standards of a PUFA:SFA ratio of above 0.4 and n‐6:n‐3 ratio of less than 4, whereas meat from CONT yaks just missed these standards.
Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.
A novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) method was used to establish and predict single oil contents in samples. To validate the method, 102 edible blend oil samples containing soybean oil, sunflower oil, peanut oil and sesame oil were measured by an ultraviolet-visible (UV-Vis) spectrophotometer. The results demonstrated that GWO-PLS models can provide best prediction accuracy with least variables compared with full-spectrum PLS, Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). The determination coefficients (R2) of GWO-PLS were all above 0.95. Therefore, the research indicates the feasibility of using discretized GWO for variable selection in rapid determination of quaternary edible blend oil.
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