Agricultural Population Supported in Rural Areas under Traditional Planting Mode Based on Opportunity Cost Analysis

The return of rural migrant workers through increasing agricultural income by expanding farming scale is significant for rural sustainable development without rural population loss. This paper selected six representative counties in Henan Province, China’s major grain-producing province, to conduct a questionnaire survey, investigated the incomes of farmers from farming and migrant workers, calculated moderate farming scale under different opportunity costs, and also estimated the agricultural population that can be supported by arable land resources. Results are as follows: (1) Under the traditional planting mode, annual per capita income of farmers in farming was USD 342.18, which was substantially lower than USD 5255.63 in migrant workers. This huge income gap has led to continuous rural population loss. (2) Under the opportunity cost of farming income equal to migrant workers income, moderate farming scales of the six selected counties were 1.39, 1.17, 1.22, 1.08, 1.34, and 1.01 ha, respectively. Under the 0.8x and 0.6x opportunity cost, corresponding moderate farming scales were 1.11, 0.94, 1.11, 0.86, 1.07, and 1.34 ha; and 0.84, 0.70, 0.73, 0.65, 0.80 and 1.01 ha, respectively. (3) On the basis of the three moderate farming scales and status quo of arable land resources, agricultural populations that can be supported by rural Henan Province were 8.0386 million, 10.0479 million and 13.3942 million, respectively. Findings can guide the formulation of rural revitalization strategic measures and the preparation of village territorial spatial planning

Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing

Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods in simulated and real datasets. The results indicate that: (1) sparse representing can successfully decompose the hyperspectral water-surface reflectance signal from random noises; (2) the proposed method exhibited better performance compared with the other three methods in different input signal-to-noise ratio (SNR) levels; (3) the proposed method effectively erased abnormal spectral vibrations of field-measured and remote-sensing hyperspectral data; (4) whilst the method is built in 1-D, it can still control the salt-and-pepper noise of PRISMA hyperspectral image. In conclusion, the proposed denoising method can improve the hyperspectral data of an optically complex water body and offer a better data source for the remote monitoring of water color

Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing

Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods in simulated and real datasets. The results indicate that: (1) sparse representing can successfully decompose the hyperspectral water-surface reflectance signal from random noises; (2) the proposed method exhibited better performance compared with the other three methods in different input signal-to-noise ratio (SNR) levels; (3) the proposed method effectively erased abnormal spectral vibrations of field-measured and remote-sensing hyperspectral data; (4) whilst the method is built in 1-D, it can still control the salt-and-pepper noise of PRISMA hyperspectral image. In conclusion, the proposed denoising method can improve the hyperspectral data of an optically complex water body and offer a better data source for the remote monitoring of water color

Amino Acid-Based Zwitterionic Polymer Surfaces Highly Resist Long-Term Bacterial Adhesion

The surfaces or coatings that can effectively suppress bacterial adhesion in the long term are of critical importance for biomedical applications. Herein, a group of amino acid-based zwitterionic polymers (pAAZ) were investigated for their long-term resistance to bacterial adhesion. The polymers were derived from natural amino acids including serine, ornithine, lysine, aspartic acid, and glutamic acid. The pAAZ brushes were grafted on gold via the surface-initiated photoiniferter-mediated polymerization (SI-PIMP). Results show that the pAAZ coatings highly suppressed adsorption from the undiluted human serum and plasma. Long-term bacterial adhesion on these surfaces was investigated, using two kinds of representative bacteria [Gram-positive Staphylococcus epidermidis and Gram-negative Pseudomonas aeruginosa] as the model species. Results demonstrate that the pAAZ surfaces were highly resistant to bacterial adhesion after culturing for 1, 5, 9, or even 14 days, representing at least 95% reduction at all time points compared to the control unmodified surfaces. The bacterial accumulation on the pAAZ surfaces after 9 or 14 days was even lower than on the surfaces grafted with poly­[poly­(ethyl glycol) methyl ether methacrylate] (pPEGMA), one of the most common antifouling materials known to date. The pAAZ brushes also exhibited excellent structural stability in phosphate-buffered saline after incubation for 4 weeks. The bacterial resistance and stability of pAAZ polymers suggest they have good potential to be used for those applications where long-term suppression to bacterial attachment is desired