The time’s arrow of macroscopic physical phenomenon is reflected by irreversible physical process, which essentially occurs from small probability state to high probability state. In this paper, simplified models are proposed to understand the macroscopic physical process. In order to describe the information of a physical system, we defined the full self-information as "information height" to describe the complexity or difficulty of a macrostate of physical system. In this way, the direction of macroscopic physical process is from high information height to low information height. We can judge the direction of physical process by the information height. If we want the macroscopic physical process to evolve from the low information height state to the high information height state, the system need to add extra information and corresponding energy to increase the information height.

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials, and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 2 kg/m3 in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.

Aim: Sca-1+CD31− cells were shown to be endothelial stem/progenitor cells, found in many mammalian tissues, including heart, were able to differentiate into cardiomyocytes in vitro and in vivo. Our previous work indicated that heart derived Sca-1+CD31− cells increased Nr1d1 mRNA level and decreased cells plasticity with aging. However, little is known about how NR1D1 affects Sca-1+CD31− cells plasticity. Methods: Lentiviral vector was used to stably overexpress Nr1d1 in young Sca-1+CD31− cells and to knockdown Nr1d1 in aged Sca-1+CD31− cells. Cell differentiation, proliferation, apoptosis, and cell cycle were evaluated. Results and Conclusions: The overexpression of Nr1d1 in young Sca-1+CD31− cells inhibited cell proliferation and promoted apoptosis. Knockdown of Nr1d1 in aged Sca-1+CD31− cells promoted cell proliferation and inhib-ited apoptosis. Using mouse cardiac myocytes cell line, confirmed the effect of Nr1d1 and indi-cated that Nr1d1 induce Serpina3 expression via Nr1d1 interaction with Nr4a3. Nr1d1 may there-fore be identified as a potent anti-aging receptor and be a therapeutic target for aging relative diseases.

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigations. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy, and to address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. Firstly, the fruit tree trunk is identified based on the improved YOLOv4 model; secondly, the camera coordinates of the tree trunk are calculated from the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined and the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall of the improved YOLOv4 model for fruit tree trunk detection are 97.05% and 95.42%, respectively, which are 5.92 and 7.91 percentage points higher than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows, and can provide a reference for autonomous visual navigation of orchard robots.