The Impact of Aging Agricultural Labor Population on Farmland Output: From the Perspective of Farmer Preferences

Chinese agriculture is facing an aging workforce which could negatively impact the industry. In this context, research is needed on how work preferences and age of farmers affect agricultural output. This paper attempts to investigate these factors to more fully understand the impact of an aging agricultural labor population on agricultural production. The results show that, in this context of aging, changes in the working-age households have a significant impact on agricultural output. Despite the fact that the impacts of intention to abandon land management were not significant, we can ignore this preference in the workforce. The combination of changes in the composition of the working-age households indicates that 58.53 percent of the agricultural producers will likely quit. This is a potential threat for the future of agricultural development. We also found that elderly farmers who do not intend to abandon farming had higher agricultural output compared to other farmers. This indicates that the adverse effects of changes in the agricultural population age result more from the agricultural output of older farmers who intend to give up farming. This intention adversely affected other elements and reduced investment. Therefore, various forms of training should increase efforts to cultivate modern professional farmers and policies should be simultaneously developed to increase agricultural production levels

Lanthanide-MOFs as multi-responsive photoluminescence sensor for sensitively detecting Fe

Fast and selective detection of contaminants plays a key role in meeting human health and environmental concerns. Herein, two groups of isostructural lanthanide MOFs, [Ln(Hpta)(oxalic acid)]·

Study on Key Cost Drivers of Prefabricated Buildings Based on System Dynamics

The prefabricated building as a major initiative has been put forward by China in recent years to promote the transformation and upgrading of the construction industry, but its rapid development also faces high cost constraints. Therefore, it is necessary and urgent to study the key cost drivers and cost control paths of prefabricated buildings. Most of the current research focuses on the construction cost of prefabricated building as a static object. This article, on the other hand, regards the construction cost of prefabricated building as a dynamic formation process and conducts systematic research from product systems, technical systems, construction processes, and management modes. The influence factors of prefabricated building cost are defined and screened with the help of HSM and previous research results. A cause-and-effect model and cost control model of prefabricated building cost driver are established. Based on the model test of the actual project, the cost generation of prefabricated buildings is simulated. Through sensitivity analysis, key cost drivers of prefabricated building are identified and ranked as degree of design standardization, unit price, prefabrication rate, information technology level, transportation mode, labor level, machinery level, transportation distance, etc. Accordingly, corresponding strategies are proposed for the cost control of prefabricated buildings

Mapping Large-Scale Mangroves along the Maritime Silk Road from 1990 to 2015 Using a Novel Deep Learning Model and Landsat Data

Mangroves are important ecosystems and their distribution and dynamics can provide an understanding of the processes of ecological change. Meanwhile, mangroves protection is also an important element of the Maritime Silk Road (MSR) Cooperation Project. Large amounts of accessible satellite remote sensing data can provide timely and accurate information on the dynamics of mangroves, offering significant advantages in space, time, and characterization. In view of the capability of deep learning in processing massive data in recent years, we developed a new deep learning model—Capsules-Unet, which introduces the capsule concept into U-net to extract mangroves with high accuracy by learning the spatial relationship between objects in images. This model can significantly reduce the number of network parameters to improve the efficiency of data processing. This study uses Landsat data combined with Capsules-Unet to map the dynamics of mangrove changes over the 25 years (1990–2015) along the MSR. The results show that there was a loss in the mangrove area of 1,356,686 ha (about 21.5%) between 1990 and 2015, with anthropic activities such as agriculture, aquaculture, tourism, urban development, and over-development appearing to be the likely drivers of this decline. This information contributes to the understanding of ecological conditions, variability characteristics, and influencing factors along the MSR

Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake

Mapping water bodies with a high accuracy is necessary for water resource assessment, and mapping them rapidly is necessary for flood monitoring. Poyang Lake is the largest freshwater lake in China, and its wetland is one of the most important in the world. Poyang Lake is affected by floods from the Yangtze River basin every year, and the fluctuation of the water area and water level directly or indirectly affects the ecological environment of Poyang Lake. Synthetic Aperture Radar (SAR) is particularly suitable for large-scale water body mapping, as SAR allows data acquisition regardless of illumination and weather conditions. The two-satellite Sentinel-1 constellation, providing C-Band SAR data, passes over the Poyang Lake about five times a month. With its high temporal-spatial resolution, the Sentinel-1 SAR data can be used to accurately monitor the water body. After acquiring all the Sentinel-1 (1A and 1B) SAR data, to ensure the consistency of data processing, we propose the use of a Python and SeNtinel Application Platform (SNAP)-based engine (SARProcMod) to process the data and construct a Poyang Lake Sentinel-1 SAR dataset with a 10 m resolution. To extract water body information from Sentinel-1 SAR data, we propose an automatic classification engine based on a modified U-Net convolutional neural network (WaterUNet), which classifies all data using artificial sample datasets with a high validation accuracy. The results show that the maximum and minimum water areas in our study area were 2714.08 km2 on 20 July 2020, and 634.44 km2 on 4 January 2020. Compared to the water level data from the Poyang gauging station, the water area was highly correlated with the water level, with the correlation coefficient being up to 0.92 and the R2 from quadratic polynomial fitting up to 0.88; thus, the resulting relationship results can be used to estimate the water area or water level of Poyang Lake. According to the results, we can conclude that Sentinel-1 SAR and WaterUNet are very suitable for water body monitoring as well as emergency flood mapping

Rotary upgrading method and its experimental study of an inertially stabilized platform

Rotation modulation can significantly improve the navigation accuracies of an inertial navigation system (INS) and a strap-down configuration dominating in this type of INS. However, this style of construction is not a good scheme since it has no servo loop to counteract a vehicle manoeuvre. This paper proposes a rotary upgrading method for a rotational INS based on an inertially stabilized platform. The servo control loop is reconstructed on a four-gimbal platform, and it has the functions of providing both a level stability relative to the navigation frame and an azimuth rotation at a speed of 1.2°/s. With the platform’s rotation, the observability and the convergence speed of the estimation for the initial alignment can be improved, as well as the biases of the gyroscopes and accelerometers be modulated into zero-mean periodic values. An open-loop initial alignment method is designed, and its detailed algorithms are delivered. The experiment result shows that the newly designed rotational INS has reached an accuracy of 0.38 n mile/h (CEP, circular error probable). The feasibility and engineering applicability of the designed scheme have been validated