To gain a deeper understanding of the magnetohydrodynamic characteristics of liquid metal jets, this study conducted experimental research on the breakup process of a liquid metal GaInSn jet under a flow-aligned magnetic field condition. Based on a self-built liquid metal jet experimental platform, the jet breakup characteristics were experimentally analyzed from four aspects: jet morphology, breakup length, surface disturbance, and droplet distribution. In the absence of a magnetic field, the breakup length increased with the increasing We number. The surface wavelength of the jet approached the critical wavelength under the action of surface tension, and the volume diameter of the jet breakup droplets showed a bimodal structure. When a flow-aligned magnetic field was applied, the droplets formed after jet breakup were subject to vibration attenuation under the action of the flow-aligned magnetic field and exhibited equally sized spherical droplets downstream of the breakup position. The surface wavelength decreased with increasing magnetic field, while the breakup length increased. Three types of influences of the flow-aligned magnetic field on the breakup length of the jet were summarized.
In EUV(extreme ultraviolet) lithography machines, multilayer mirrors may be contaminated by carbon deposition when exposed to high-energy EUV radiation. The reflectivity of the mirror is therefore reduced, thereby reducing the service life of the lithography machine. The EUV induced plasma produced by the ionization of the background gas by EUV light has a good cleaning effect on the deposited carbon. In this paper, molecular dynamics method is used to simulate the interaction process between Ar ions of plasma and graphitic deposited carbon. A comprehensive study has been carried out from the adsorption of Ar on the graphite surface to the cumulative irradiation of independent Ar and a large amount of Ar on the graphite surface. The results show that Ar has the most stable adsorption structure at the Hollow site on the graphite surface. When Ar diffuses on the graphite surface, it tends to diffuse through the Bridge site in the middle of the C-C bond to the adjacent Hollow site. When a single independent energetic Ar impinges on the surface of graphite, there would be three phenomena: reflection, adsorption and diffusion. It mainly depends on the site of incident Ar on the graphite. When a large amount of Ar accumulatively irradiates graphite, a variety of defects will occur and continue to develop depending on the amount and energy of incident Ar. As a result, the strength of the graphite layer is greatly reduced and even physical sputtering effects occur.
Fe-MOF materials MIL-53 (Fe) and MIL-53 (Fe)-10FA were prepared by solvothermal method, and their adsorption properties for diclofenac sodium (DCF) in aqueous solution were investigated. The adsorption behavior of the two MOF materials to DCF follows the pseudo-second-order kinetic model, and the adsorption process belongs to chemical adsorption. The adsorption equilibrium isotherm conforms to the Langmuir adsorption isotherm model and is monolayer adsorption. The surface morphology of MIL-53 (Fe)-10FA modified by formic acid was different from that of MIL-53 (Fe). The crystal size was larger than that of MIL-53(Fe). The pore size, specific surface area and total pore volume increased after modified. The content of oxygen-containing functional groups also increased. The adsorption capacity of MIL-53 (Fe)-10FA for DCF was significant, and the maximum adsorption capacity of Langmuir isotherm fitting was 652.29 mg/g. The adsorption mechanism mainly includes hydrogen bonding and π-π stacking between benzene rings and benzene rings.
Ecosystem services (ES) are the bridge connecting ecosystems and social systems, and have gradually become an important basis for decision-making. To this end, this paper reviews the progresses of applying ecosystem service in decision-making by randomly selecting 1 000 Chinese papers on the topic of ecosystem service from 1997 to 2021. According to the four-element process chain from ecosystem service to decision making, this paper quantitatively and qualitatively analyzes the main types of process chains that the existing research focuses on, analyzes the content of process chains from two aspects of salience and legitimacy of ES information, and discusses the future research challenges. We found that although ecosystem services are highly sought after, there are few studies that fully integrate them into the process chain of decision-making. More than two thirds of the papers focused on one or two elements of the process chain, and 2% of the papers included a complete process chain for applying ecosystem services to decision-making. Regarding the salience of ES information, there are few studies at the national scale. In terms of legitimacy, the focus on stakeholders is weak. In terms of ecological regulatory factors, grain yield, biomass, and net primary productivity of vegetation are high-frequency considerations. In terms of socio-economic regulatory factors, food prices, GDP, and population are high-frequency considerations. In the future, in the process of applying ecosystem services to decision-making, it is necessary to further strengthen the standardization and local optimization of ecosystem service assessment methods, spatial and temporal scale selection, enhance stakeholder and public participation, and adopt scenario analysis to provide strong support for guiding practice and policy formulation.
The production and supply of food are core components of sustainable development. Ensuring the sustainability of global food production and supply is crucial for maintaining human survival and socioeconomic stability, and it holds significant importance in advancing the “Zero Hunger” goal within the framework of global sustainable development. This paper selects the five key cereal crops, including wheat, barley, maize, oats, and rice, as the subjects of study, focusing on the Central Asian region. It analyzes the variations in yield per hectare, total production, and cultivated area for these cereals from 1992 to 2021, investigates regional disparities in food production fluctuations within Central Asia, and employs the ARIMA model to forecast future grain production in Central Asia. The results showed that: 1) From 1992 to 2021, the grain yield, total output and sown area in Central Asia showed a trend of first decreasing and then increasing, and the three changes ranged from 0.79~1.96 t/hm2, (0.14~0.37)×108 t and (0.14~0.23)×108 hm2, respectively. Grain yield and total production reached their peaks in 2011 at 1.96 t/hm2, and 0.37×108 t, respectively, while the cultivated grain area peaked in 1993 at 0.23×108 hm2. 2) The grain volatility in Central Asia is characterized by frequent fluctuations in grain production, with a significant proportion of years experiencing fluctuations exceeding 5%. The amplitude of these fluctuations is substantial, and the average fluctuation cycle is 2-4 years, indicating a short-term cyclical pattern dominated by classical rather than growth-oriented fluctuations. 3) In the coming years, Central Asia is projected to experience an upward trend in wheat, barley, maize, and oats production, while rice production is expected to decline. Compared to the year 2021, by 2030, Central Asia’s wheat, barley, maize, and oats production is estimated to increase by (410.15, 91.6, 795.26, and 8.91)×104 t, respectively, representing growth rates of 20.1%, 31%, 299.2%, and 37.1%. Conversely, rice production may decrease by 15.99×104 t, with a decline of 15.5%.
Based on the perspective of human-earth relationships, firstly, this article analyzes the theoretical connotation of “Lucid waters and lush mountains are invaluable assets” from the perspectives of philosophical meaning, geographical logic, and economic thinking. Secondly, from the three aspects of serving the rural revitalization, building a beautiful China, and building a community of human destiny, the era value of “Lucid waters and lush mountains are invaluable assets”. Subsequently, from the perspective of coordinated development of human-earth relations, from the four aspects of highlighting the participation of people, the value of excavation land, maintaining human-land balance, and the standardized human and ground behavior, it proposed the development of the transformation from “lucid waters and lush mountains” to “gold and silver mountains”. Finally, according to the key issues that have not been resolved in the practical path, it is pointed out that future research should be strengthened on the selection of transformation path and model, the valuation of ecological products, and the evaluation of transformation efficiency of the “Two Mountains”.
Urban street view imagery, as crucial forms of spatial data, has a wide range of applications in mapping services, urban 3D reconstruction, and cartography. However, since the collected street view images often face challenges such as distracting target occlusion and privacy concerns, necessitating meticulous preprocessing. Addressing these challenges, we propose an image inpainting algorithm based on multi-scale semantic priori guided for generating more realistic and natural static street view images. Firstly, a semantic prior network is designed to learn the multi-scale semantic priors of the missing regions of the input image to enhance the contextual information. The semantic enhancement generator adaptively fuses the multi-scale semantic priors and image features and at the same time introduces a multilevel attention shifting mechanism to refine the texture information of the image. Finally, a Markov discriminator is adopted to distinguish the generated image from the real image by adversarial training, which makes the reconstructed street scene image more realistic. Experiments on the Apolloscape dataset demonstrate that the images generated by our algorithm have achieved significant improvements in semantic structural coherence and detailed texture, solving the privacy problem in street view while providing a more reliable data base for realistic city applications.
Spaceborne synthetic aperture radar (SAR) imagery with higher spatial resolution requires greater knowledge of the satellite’s orbit. However, sometimes global positioning system (GPS) is not able to provide accurate position information that the image resolution requires. In our experience, the smoothness of spaceborne SAR orbit can be helpful for autofocus. In this paper, a novel approach for autofocusing in spaceborne SAR is proposed. First, the modeling of the orbit in three-dimensions as polynomial functions is involved. Therefore, autofocusing can be achieved by estimating the polynomial coefficients. Then, several patches distributed over the SAR image are selected, and the optimal range history of the center point in each patch is obtained based on the maximum-contrast optimization. The estimated orbit of the whole scene can be derived through the range history information. Finally, the image can be refined with better focusing performance. The estimated orbit is capable of satisfying the optimal quality for every patch. Furthermore, with proper patch selection, including the number and the relative location of patches, better quality within the whole scene can be reconstructed by the estimated orbit. This method is tested and validated with simulation experiments and real data.
The convergence of broadband LEO satellite communication systems with 5G is the development trend of satellite communication. To meet the demand for high-speed data transmission, multibeam technology based on large-scale digital phased arrays is indispensable. The peak-to-average ratio problem of multibeam signals induces the non-linear distortion of onboard power amplifiers, while the non-linear distortion of power amplifiers leads to serious interference between multibeams. A digital pre-distortion structure for multibeam is proposed for low-orbit satellite communication systems using large-scale phased arrays. A pre-distortion model is developed to solve the problems of non-linearity, intermodulation distortion and multibeam interference. Through simulation experiments, it is found that the proposed structure is more advantageous than the traditional pre-distortion structure in terms of performance and complexity balance, providing a feasible solution for the implementation of multibeam and overcoming non-linearity on board.
Taking Bozhou City, Anhui Province as the research area, the differences in the light intensity of several buildings with different uses in two phases were shown in a visualized way. The light area and the impervious area were analyzed by linear regression and IoU (intersection over union) to obtain the differences and changes in light distribution in urban and rural areas. The results show that: 1) Different building types have different light intensities at different times. 2) Due to the high spatial resolution, SDGSAT-1 low-light images can partially distinguish between the main road lights and building lights in urban areas. 3) The linear regression of the light area and the area of the impermeable layer can produce a good fitting effect in the urban area, but the light in the township area can not be well fitted with the area of the impermeable layer because it is not easy to be received by the sensor. 4) The IoU of light area and impervious area in the towns of Bozhou is related to GDP.
Sleep apnea syndrome is a common and potentially harmful sleep disorder, and the classification and detection of sleep apnea can provide an important basis for the diagnosis of the disease. Due to their non-contact nature, video-based sleep monitoring systems are universally applicable for disease screening, among which thermal imaging cameras, with strong privacy protection, have attracted wide attention in recent years. In this paper, we propose a novel sleep apnea detection and classification method using thermal imaging. By obtaining the temporal information of thoracic and abdominal movement, a two-dimensional complex feature space mapping central and obstructive sleep apnea under different physiological mechanisms is constructed. Based on their statistical properties, the respiratory effort intensity feature and the respiratory effort asynchrony feature are proposed to achieve the classification and detection of two types of sleep apnea. Experimental results show that the accuracy of detecting both types of sleep apnea exceeds 97.0%. This work effectively overcomes the problem of difficulty in extracting valid information caused by observation noise and redundant information in videos, and is expected to assist in the actual screening and diagnosis of sleep disorders.
Vascular puncture interventional surgery is a modern minimally invasive treatment method, which punctures the target vein and inserts guide wire to accurately reach the affected area for treatment. It has the characteristics of little trauma, fast recovery and high safety. However, due to the different depth of the vessel, it is difficult to locate and accurately puncture the target vessel in puncture interventional surgery. Current puncture devices are not suitable for emergency vasculature access in non-clinical settings due to their size and weight. We designed a portable ultrasound-guided robot for venous puncture. The robot observed the venous tube through ultrasonic equipment, aligned the ultrasonic image, puncture needle and target venous tube through mechanical structure, and realized venous puncture at different depths by adjusting the moving distance up and down. The robot is small and lightweight, making it portable for use in non-clinical conditions. Experiments show that compared with conventional puncture methods, the robot can reduce patient trauma, reduce the difficulty of operators, and improve the success rate of vascular puncture.
Bounding-box annotation form has been the most frequently used method for visual object localization tasks. However, bounding-box annotation relies on a large amount of precisely annotating bounding boxes, and it is expensive and laborious. It is impossible to be employed in practical scenarios and even redundant for some applications (such as tiny person localization) that the size would not matter. Therefore, we propose a novel point-based framework for the person localization task by annotating each person as a coarse point (CoarsePoint) instead of an accurate bounding box that can be any point within the object extent. Then, the network predicts the person’s location as a 2D coordinate in the image. Although this greatly simplifies the data annotation pipeline, the CoarsePoint annotation inevitably decreases label reliability (label uncertainty) and causes network confusion during training. As a result, we propose a point self-refinement approach that iteratively updates point annotations in a self-paced way. The proposed refinement system alleviates the label uncertainty and progressively improves localization performance. Experimental results show that our approach has achieved comparable object localization performance while saving up to 80% of annotation cost.
This paper proposes a multi-spectral remote sensing image sharpening method based on a deep convolutional neural network and residual network. The method addresses the problems of spectral distortion in traditional remote sensing image sharpening methods and insufficient information utilization between network layers in current deep learning-based methods. The proposed method uses the depth convolution and residual network to design the depth residual module to extract the spatial and spectral features of the deep image. Additionally, residual connections between sub-blocks are established to transmit gradient information to deeper networks and avoid gradient explosion problems, making the network more efficient. Experiments are conducted on simulated and real-world multi-spectral images from WorldView-2, and the results are compared with traditional and existing deep learning-based methods. The proposed method improves the spectral distortion phenomenon and learns deeper image features to better preserve the spatial and spectral information of the image. The proposed method outperforms the deep convolutional sharpening network method in terms of various evaluation metrics, including ERGAS, SAM, SCC, UIQI, and the global fusion quality evaluation index. The proposed method improves these metrics by 24.4%, 26.7%,6.2%,4.7%, and 6.3%, respectively. Subjective and objective evaluations and spectral curve also indicate that the proposed method significantly improves the spatial and spectral resolution of remote sensing images, especially under complex environmental conditions.