Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms

Monitoring cow body weight is crucial to support farm management decisions due to its direct relationship with the growth, nutritional status, and health of dairy cows. Cow body weight is a repeated trait, however, the majority of previous body weight prediction research only used data collected at a single point in time. Furthermore, the utility of deep learning-based segmentation for body weight prediction using videos remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction. A total of 40,405 depth images and depth map files were obtained from 10 lactating Holstein cows and 2 non-lactating Jersey cows. Three approaches were investigated to segment the cow's body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric features, such as dorsal length, abdominal width, height, and volume, were estimated from the segmented images. On average, the Mask-RCNN approach combined with a linear mixed model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.98 and 2.03%, respectively, in the forecasting cross-validation. The Mask-RCNN approach was also the best in the leave-three-cows-out cross-validation. The prediction coefficients of determination and mean absolute percentage error of the Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%, respectively. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data

Interfacial electronic structure at the CH3NH3PbI3/MoOx interface

Interfacial electronic properties of the CH3NH3PbI3 (MAPbI3)/MoOx interface are investigated using ultraviolet photoemission spectroscopy and X-ray photoemission spectroscopy. It is found that the pristine MAPbI3 film coated onto the substrate of poly (3,4-ethylenedioxythiophene) poly(styrenesulfonate)/indium tin oxide by two-step method behaves as an n-type semiconductor, with a band gap of ~1.7 eV and a valence band edge of 1.40 eV below the Fermi energy (EF). With the MoOx deposition of 64A ° upon MAPbI3, the energy levels of MAPbI3 shift toward higher binding energy by 0.25 eV due to electron transfer from MAPbI3 to MoOx. Its conduction band edge is observed to almost pin to the EF, indicating a significant enhancement of conductivity. Meanwhile, the energy levels of MoOx shift toward lower binding energy by ~0.30 eV, and an interface dipole of 2.13 eV is observed at the interface of MAPbI3/MoOx. Most importantly, the chemical reaction taking place at this interface results in unfavorable interface energy level alignment for hole extraction. A potential barrier of ~1.36 eV observed for hole transport will impede the hole extraction from MAPbI3 to MoOx. On the other hand, a potential barrier of ~0.14 eV for electron extraction is too small to efficiently suppress electrons extracted from MAPbI3 to MoOx. Therefore, such an interface is not an ideal choice for hole extraction in organic photovoltaic devices

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

The Bohai Sea and its surrounding areas are rich in oil and natural gas and play an important role in industry, agriculture and the economy. However, the Bohai Sea suffers severely from sea ice in the winter. While previous research has predominantly focused on methods for retrieving sea ice parameters in the Bohai Sea, analyses of their long-term statistical patterns have been limited. The Geostationary Ocean Color Imager (GOCI) is the first geostationary satellite for ocean color remote sensing, offering high spatial and temporal resolution, which greatly facilitates the extraction of Bohai Sea ice parameters. Utilizing GOCI data, we systematically extracted relevant sea ice parameters for the Bohai Sea region from 2011 to 2021. These parameters include sea ice concentration, sea ice thickness, and sea ice drift. We conducted a comprehensive statistical analysis of the long-term sea ice changes in the Bohai Sea and found that the development process of winter sea ice area is different from the sea ice thickness, and the direction of sea ice drift is basically unchanged. Then we developed statistical models linking sea ice parameters with ocean dynamic factors such as temperature, wind, and drift currents. Among them, the correlation coefficient between the predicted value and the measured value of the sea ice area model is the highest, reaching 0.8382. Furthermore, we examined the previously unexplored relationship between daily sea ice area, sea ice thickness, and accumulated temperature with their respective starting temperatures and accumulation periods. This study provides critical data to support Bohai Sea ice monitoring and marine environmental research. The results of this study contribute to a better understanding of sea ice change trends in the Bohai Sea and inform the development of disaster prevention and mitigation measures

Pavement Performance Investigation of Nano-TiO₂/CaCO₃ and Basalt Fiber Composite Modified Asphalt Mixture under Freeze‒Thaw Cycles

The objective of this research is to evaluate the pavement performance degradation of nano-TiO2/CaCO3 and basalt fiber composite modified asphalt mixtures under freeze‒thaw cycles. The freeze‒thaw resistance of composite modified asphalt mixture was studied by measuring the mesoscopic void volume, stability, indirect tensile stiffness modulus, splitting strength, uniaxial compression static, and dynamic creep rate. The equal-pitch gray prediction model GM (1, 3) was also established to predict the pavement performance of the asphalt mixture. It was concluded that the high- and low-temperature performance and water stability of nano-TiO2/CaCO3 and basalt fiber composite modified asphalt mixture were better than those of an ordinary asphalt mixture before and after freeze‒thaw cycles. The test results of uniaxial compressive static and dynamic creep after freeze‒thaw cycles showed that the high-temperature stability of the nano-TiO2/CaCO3 and basalt fiber composite modified asphalt mixture after freeze‒thaw was obviously improved compared with an ordinary asphalt mixture

Investigation on Preparation and Properties of Crack Sealants Based on CNTs/SBS Composite-Modified Asphalt

Crack is the main distress of asphalt pavement. Sealant is one of the most commonly used crack repair materials, and its performance is the key to affect the service life of asphalt pavements. In order to find an efficient modifier and optimize the performances of crack sealants. In this paper, carbon nanotubes (CNTs) and styrene-butadiene-styrene (SBS) were used as modifiers to prepare CNTs/SBS composite-modified asphalt crack sealant. The properties of the sealant were tested to evaluate its suitability for crack repair, which included the viscosity, softening point, resilience recovery, cone penetration, flow value, penetration, aging resistance, and fatigue resistance. The results showed that the conventional properties of the sealants meet the requirements of the specification. In addition, after heating aging, the elastic recovery rate of the sealant containing more CNTs decreased only slightly. The sealant containing 1 wt% CNTs exhibited a higher viscosity, fatigue resistance, thermal aging resistance