An Image Segmentation Algorithm for Gradient Target Based on Mean-Shift and Dictionary Learning

In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method

Optimization of Agricultural Machinery Allocation in Heilongjiang Reclamation Area Based on Particle Swarm Optimization Algorithm

Aiming at the imbalance of seasonal agricultural machinery operations in different regions and the low efficiency of agricultural machinery, an experiment is proposed to use particle swarm algorithm to plan agricultural machinery paths to solve the current problems in agricultural machinery operations. Taking the harvesting of autumn soybeans at Jianshan Farm in Heilongjiang Reclamation Area as the experimental object, this paper constructs the optimization target model of the maximum net income of farm machinery households, and uses particle swarm algorithm to carry out agricultural machinery operation distribution and path planning gradually. In this paper, by introducing 0 - 1 mapping, the improved algorithm adopts continuous decision variables to solve the optimization of discrete variables in agricultural machinery operations. The test results show that the particle swarm algorithm can realize the optimal allocation of agricultural machinery path, and the particle swarm algorithm is scientific and explanatory to solve the agricultural machinery allocation problem. This research can provide a scientific basis for farm agricultural machinery allocation and decision analysis

Research on the Optimized Management of Agricultural Machinery Allocation Path Based on Teaching and Learning Optimization Algorithm

With the adjustment of agricultural industry structure and the acceleration of land transfer in our country, the appropriate management level of continuous, large scale and intensive farming has remarkably improved, which has put forward a higher requirement for the level of agricultural machinery. According to statistics, the comprehensive mechanization rate of farming, planting and harvesting of major crops in China has exceeded 70% in 2020. However, the development level of mechanization in different provinces and regions is still unbalanced, so is the demand and supply of seasonal agricultural machinery operation. Cross regional operation of agricultural machinery is still a common occurrence. So a scientific, efficient and low-cost agricultural machinery allocation scheme is to be constructed so as to solve the imbalance of development level between regions of agricultural mechanization and the contradiction between supply and demand of agricultural machinery in operation seasons and to realize the rational allocation of agricultural machinery in cross regional operation. The allocation scheme can increase the machinery owners\u27 income by improving the utilization rate and allocation cost of agricultural machinery.This study systematically analyzes and summarizes the allocation mode of agricultural machinery in the reclamation area, constructs relevant data models and solution methods, and ultimately comes to the effective solution of the operation relationship allocation model and path optimization model under different allocation modes of agricultural machinery based on TLBO optimization theory and method, which effectively solves the problem of rational and efficient allocation of agricultural machinery in cross regional operation of agricultural machinery

Identification and experimental validation of key m6A modification regulators as potential biomarkers of osteoporosis

Osteoporosis (OP) is a severe systemic bone metabolic disease that occurs worldwide. During the coronavirus pandemic, prioritization of urgent services and delay of elective care attenuated routine screening and monitoring of OP patients. There is an urgent need for novel and effective screening diagnostic biomarkers that require minimal technical and time investments. Several studies have indicated that N6-methyladenosine (m6A) regulators play essential roles in metabolic diseases, including OP. The aim of this study was to identify key m6A regulators as biomarkers of OP through gene expression data analysis and experimental verification. GSE56815 dataset was served as the training dataset for 40 women with high bone mineral density (BMD) and 40 women with low BMD. The expression levels of 14 major m6A regulators were analyzed to screen for differentially expressed m6A regulators in the two groups. The impact of m6A modification on bone metabolism microenvironment characteristics was explored, including osteoblast-related and osteoclast-related gene sets. Most m6A regulators and bone metabolism-related gene sets were dysregulated in the low-BMD samples, and their relationship was also tightly linked. In addition, consensus cluster analysis was performed, and two distinct m6A modification patterns were identified in the low-BMD samples. Subsequently, by univariate and multivariate logistic regression analyses, we identified four key m6A regulators, namely, METTL16, CBLL1, FTO, and YTHDF2. We built a diagnostic model based on the four m6A regulators. CBLL1 and YTHDF2 were protective factors, whereas METTL16 and FTO were risk factors, and the ROC curve and test dataset validated that this model had moderate accuracy in distinguishing high- and low-BMD samples. Furthermore, a regulatory network was constructed of the four hub m6A regulators and 26 m6A target bone metabolism-related genes, which enhanced our understanding of the regulatory mechanisms of m6A modification in OP. Finally, the expression of the four key m6A regulators was validated in vivo and in vitro, which is consistent with the bioinformatic analysis results. Our findings identified four key m6A regulators that are essential for bone metabolism and have specific diagnostic value in OP. These modules could be used as biomarkers of OP in the future

Effect of the shelterbelt along the Tarim Desert Highway on air temperature and humidity

The temperature and humidity of the shelterbelt micro-climate on both horizontal and vertical scales in the extremely drought area were measured with multiple HOBO temperature and humidity automatic observation equipments in the hinterland of the Taklimakan Desert. The results show that the shelterbelt ecosystem of the desert highway plays typical micro-climate adjustment rolesin stabilizing surface air temperature and increasing air humidity, and so on. Solar radiation significantly affects both temperature and humidity of surface layers, and it has a positive correlation with the temperature but a negative correlation with the air humidity. When it is cloudy, the weather has a great impact on keeping temperature and humidity in the shelterbelt. The shelterbelt also significantly influences the environment, and the micro-climate in the belt has an obvious characteristic of cooling and humidification: compared with the original sand area, the temperature in the shelterbelt is always lower and the humidity is always higher. Moreover, the temperature range at the shelterbelt edge is greater than that in the sand area, but the humidity is always higher. Our conclusion is that the vertical-effect range of temperature of the shelterbelts is 4-10 m, and the humidity range is 6 to 8 m; the horizontal-effect range of temperature is 16 m and the humidity range is about 24 m

The Global Search for Liquid Water on Mars from Orbit: Current and Future Perspectives

Due to its significance in astrobiology, assessing the amount and state of liquid water present on Mars today has become one of the drivers of its exploration. Subglacial water was identified by the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) aboard the European Space Agency spacecraft Mars Express through the analysis of echoes, coming from a depth of about 1.5 km, which were stronger than surface echoes. The cause of this anomalous characteristic is the high relative permittivity of water-bearing materials, resulting in a high reflection coefficient. A determining factor in the occurrence of such strong echoes is the low attenuation of the MARSIS radar pulse in cold water ice, the main constituent of the Martian polar caps. The present analysis clarifies that the conditions causing exceptionally strong subsurface echoes occur solely in the Martian polar caps, and that the detection of subsurface water under a predominantly rocky surface layer using radar sounding will require thorough electromagnetic modeling, complicated by the lack of knowledge of many subsurface physical parameters. Higher-frequency radar sounders such as SHARAD cannot penetrate deep enough to detect basal echoes over the thickest part of the polar caps. Alternative methods such as rover-borne Ground Penetrating Radar and time-domain electromagnetic sounding are not capable of providing global coverage. MARSIS observations over the Martian polar caps have been limited by the need to downlink data before on-board processing, but their number will increase in coming years. The Chinese mission to Mars that is to be launched in 2020, Tianwen-1, will carry a subsurface sounding radar operating at frequencies that are close to those of MARSIS, and the expected signal-to-noise ratio of subsurface detection will likely be sufficient for identifying anomalously bright subsurface reflectors. The search for subsurface water through radar sounding is thus far from being concluded

Effects of natural covers on soil evaporation of the shelterbelt along the Tarim Desert Highway

The control of soil evaporation is one of important approaches to save water. The artificially simulated evaporation experiments have been conducted in the hinterland of the Taklimakan Desert to reveal the effects of the natural covers on the soil evaporation of the Tarim Desert Highway shelterbelt as well as provide some insights in the efficient utilization of water resources and optimization of irrigation systems. The results showed that (1) All the covers, including the sand deposit, the salt crust, the litter, the sand-litter mixed layer and so on, can significantly inhibit the soil water evaporation. Specifically, the daily evaporation, the total evaporation, and the evaporation rate in covered sands were much smaller than that of sands without cover. The cover inhibition effects increased with the cover thickness. Particularly, the soil evaporation of the covered sands was less affected by external and internal factors than that of the bare sands. Moreover, the variation of daily evaporation of covered sands was smaller than that of bare sands. The cumulative evaporation varied linearly with time in the covered sands whereas it varied logarithmically in the bare sands. In addition, the soil evaporation in the bare sands showed significantly different characteristics in the early and late stages of the evaporation. (2) All the covers exhibited the significant inhibiting effect on the soil evaporation, and the inhibition efficiency increased logarithmically with the cover thickness. However, as the cover thickness was above a certain value, the increase in the inhibition efficiency was slow. Particularly, at a cover thickness of 2 cm, there was no obvious difference in the inhibition efficiency among all kinds of covers. The maximum inhibition efficiency as calculated from the daily evaporation on the first day of irrigation was: sand-litter mixed layer (79.92%) > litter layer (78.96%) > salt crust (75.58%) > sand bed (74.11%), whereas the average inhibiting efficiency as calculated from the cumulative soil evaporation at the end of an irrigation cycle (the fourth day) was: salt crust (67.78%) > sand-litter mixed layer (66.72%) > sand deposit (63.28%) > litter layer (61.74%)