Conversion of the abundant agricultural residual cotton stalk (CS) into useful chemicals or functional materials could alleviate the fossil fuels caused energy shortages and environmental crises. Although some advances have been achieved, less attention has been paid to the plant tissues effect. In this study, the plant tissue of CS was changed by part degradation of some components (hemicelluloses and lignin, for example) with the aid of acid/base (or both). The pretreated CS was transformed into hydrochar by hydrothermal carbonization (HTC) method. Morphological and chemical compositions of CS hydrochar were analyzed by various techniques, including elemental analysis, Fourier transform infrared (FTIR), BET analysis, X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD). Methylene blue (MB) removal of prepared CS hydrochar was used to evaluate CS hydrochar pollutions adsorption capacity. Results reveal acid/base (or both) pretreatment is beneficial for CS raw material to prepare high-quality CS hydrochar. The effects of some parameters, such as initial MB concentration, temperature, pH value and recyclability on the adsorption of MB onto both acid and base-pretreated CS hydrochar (CS-H₂SO₄ + NaOH-HTC) were studied. The present work exhibits the importance of agricultural waste biomass material plant tissues on its derived materials, which will have a positive effect on the direct utilization of waste biomass.

This paper presents a micromachined monocrystalline silicon piezoresistive temperature sensor fabricated by a surface micromachining CMOS (Complementary Metal Oxide Semiconductor) MEMS (Micro-Electro-Mechanical System) process. The design of the temperature sensor is based on the structure of the multi-layer cantilever beam and the bimetallic effect. The temperature change of the cantilever beam is translated into the change of the piezoresistance’s value. The test results show that the sensitivities of the sensors are 27.9 mV/°C with 100 Ω/▯ piezoresistance between −40 °C to 60 °C and 7.4 mV/°C with 400 Ω/▯ piezoresistance between −90 °C to 60 °C. The temperature sensor proposed in this paper can be used in radiosondes for its low operating temperature (as low as −90 °C), small size (below 1 mm²) and low heat capacity.

Background: The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict phenotypic values since their advantages in processing high dimensional data. While, the existing researches have not indicated which ML methods are suitable for most pig genomic prediction. Therefore, it is necessary to select appropriate methods from a large number of ML methods as long as genomic prediction is performed. This paper compared the performance of popular ML methods in predicting pig phenotypes and then found out suitable methods for most traits. Results: In this paper, five commonly used datasets from other literatures were utilized to compare the performance of different ML methods. The experimental results demonstrate that Stacking performs best on the PIC dataset where the trait information is hidden, and the performs of kernel ridge regression with rbf kernel (KRR-rbf) closely follows. Support vector regression (SVR) performs best in predicting reproductive traits, followed by genomic best linear unbiased prediction (GBLUP). GBLUP achieves the best performance on growth traits, with SVR as the second best. Conclusions: GBLUP achieves good performance for GP problems. Similarly, the Stacking, SVR, and KRR-RBF methods also achieve high prediction accuracy. Moreover, LR statistical analysis shows that Stacking, SVR and KRR are stable. When applying ML methods for phenotypic values prediction in pigs, we recommend these three approaches.

Using computer vision technology to estimate pig live weight is an important method to realize pig welfare. But there are two key issues that affect pigs’ weight estimation: one is the uneven illumination, which leads to unclear contour extraction of pigs, and the other is the bending of the pig body, which leads to incorrect pig body information. For the first one, Mask R-CNN was used to extract the contour of the pig, and the obtained mask image was converted into a binary image from which we were able to obtain a more accurate contour image. For the second one, the body length, hip width and the distance from the camera to the pig back were corrected by XGBoost and actual measured information. Then we analyzed the rationality of the extracted features. Three feature combination strategies were used to predict pig weight. In total, 1505 back images of 39 pigs obtained using Azure kinect DK were used in the numerical experiments. The highest prediction accuracy is XGBoost, with an MAE of 0.389, RMSE of 0.576, MAPE of 0.318% and $R^2$ of 0.995. We also recommend using the Mask R-CNN + RFR method because it has fairly high precision in each strategy. The experimental results show that our proposed method has excellent performance in live weight estimation of pigs.