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Plant income insurance has become an important part of agricultural insurance in China. It has been recommended to pilot since 2016 by Chinese government in several counties, and is now (2022) required to be implemented in all major grain producing counties in the 13 major grain producing provinces. The measurement of yield for plant income insurance in such huge volume urgently needs the support of remote sensing technology. Therefore, the development history and application status of remote sensing technology in the whole agricultural insurance industry was reviewed to help understanding the whole context circumstances of plant income insurance firstly. Then, the application scenarios of remote sensing technology were analyzed, and the key remote sensing technologies involved were introduced. The technologies involved include crop field plot extraction, crop classification, crop disaster estimation, and crop yield estimation. Research progress of these technologies were reviewed and summarized ,and the satellite data sources that most commonly used in plant income insurance were summarized as well. It was found that to obtain a better support for a development of plant income insurance as well as all crop insurance from remote sensing communities, issues existed not only in the involved remote sensing technologies, but also in the remote sensing industry as well as the insurance industry. The most two important technical problems in the current application scenario of planting income insurance are that: the plot extraction and crop classification are not automated enough; the yield estimation mechanism is not strong, and the accuracy is not high. At the industry level, the first issue is the limitation of the remote sensing technology itself in that the remote sensing is not almighty, suffering from limited data source, either from satellite or from other platform, laborious data preprocessing, and pricey data fees for most of the data, and the second is the compatibility between the current business of the insurance industry and the combination of remote sensing. In this regard, this paper proposed in total five specific suggestions, which are: 1st, to establish a data distribution platform to solve the problems of difficult data acquisition and processing and standardization of initial data; 2nd, to improve the sample database to promote the automation of plot extraction and crop classification; 3rd, to achieve faster, more accurate and more scientific yields through multidisciplinary research; 4th, to standardize remote sensing technology application in agricultural insurance, and 5th, to write remote sensing applications in crop insurance contract. With these improvements, the application mode of plant income insurance and probably the whole agriculture insurance would run in a way with easily available data, more automated and intelligent technology, standards to follow, and contract endorsements.

Lettuce is an annual plant of the family Asteraceae. It is most often grown as a leaf vegetable, but sometimes for its stem and seeds, and its growth status and quality are evaluated based on its morphological phenotypic traits. However, traditional measurement methods are often labor-intensive and time-consuming due to manual measurements and may result in less accuracy. In this study, we proposed a new method utilizing RGB images and Mask R-Convolutional Neural Network (CNN) for estimating lettuce critical phenotypic traits. Leveraging publicly available datasets, we employed an improved Mask R-CNN model to perform a phenotypic analysis of lettuce images. This allowed us to estimate five phenotypic traits simultaneously, which include fresh weight, dry weight, plant height, canopy diameter, and leaf area. The enhanced Mask R-CNN model involved two key aspects: (1) replacing the backbone network from ResNet to RepVGG to enhance computational efficiency and performance; (2) adding phenotypic branches and constructing a multi-task regression model to achieve end-to-end estimation of lettuce phenotypic traits. Experimental results demonstrated that the present method achieved high accuracy and stable results in lettuce image segmentation, detection, and phenotypic estimation tasks, with APs for detection and segmentation being 0.8684 and 0.8803, respectively. Additionally, the R2 values for the five phenotypic traits are 0.96, 0.9596, 0.9329, 0.9136, and 0.9592, with corresponding mean absolute percentage errors (MAPEs) of 0.1072, 0.1522, 0.0757, 0.0548, and 0.0899, respectively. This study presents a novel technical advancement based on digital knowledge for phenotypic analysis and evaluation of lettuce quality, which could lay the foundation for artificial intelligence expiation in fresh vegetable production. [ABSTRACT FROM AUTHOR]

Vertically aligned nanowire arrays, with high surface-to-volume ratio and efficient light-trapping absorption, have attracted much attention for photoelectric devices. In this paper, vertical β-Ga2O3 nanowire arrays with an average diameter/height of 110/450 nm have been fabricated by the inductively coupled plasma etching technique. Then a metal-semiconductor-metal structured solar-blind photodetector (PD) has been fabricated by depositing interdigital Ti/Au electrodes on the nanowire arrays. The fabricated β-Ga2O3 nanowire PD exhibits ∼10 times higher photocurrent and responsivity than the corresponding film PD. Moreover, it also possesses a high photocurrent to dark current ratio (Ilight/Idark) of ∼104 and a ultraviolet/visible rejection ratio (R260 nm/R400 nm) of 3.5 × 103 along with millisecond-level photoresponse times.

In recent years, revenue protection crop insurance is an innovative insurance that has been prioritized in China. But it still lacks the support of the third-party yield data around crop harvest time. Aiming to provide objective yield data for revenue protection crop insurance, satellite remote sensing production estimation technology was employed to discuss its application mode and applicability. Taking the soybean revenue protection insurance in Jiaxiang county, Shandong province as an example, we first extracted soybean planting plots, calculated vegetation index and crop physiological parameters based on Sentinel-2 satellite images in 2018 . Combining to TRMM precipitation data from TRMM precipitation-monitoring radar satellite and MODIS land surface temperature data from Terra/Aqua satellite and site yield data, we established a multi-parameter linear regression model, and estimated soybean yield per unit area. The crop extraction results showed that the soybean planting area in the study area was 1.24 km2, which was in good agreement with the 1.27 km2 reported by the local agricultural bureau; and with using the actual measurement plots, the remote sensing identification accuracy of the planting distribution plots reached 90%. The yield estimation results showed that the NDVI of the soybean pod stage on August 23 and the leaf area index of the soybean seedling stage on September 7 explained the soybean yield per hectare the best, and the average estimated yield of the whole area was 244,500 kg/m2, which reflects the severely affected agricultural conditions, comparing to 299,800 kg/km2 in previous years.The regression coefficient between the estimated yield data and the measured data reached 0.92, which meet the application needs.With this results, the estimated yield of different towns can be summarized, and the regional yield was present, and was used as the real yield in 2018, multiplying with the average soybean price around October 11 to December 10 from the local price bureau, the real revenue was obtained. Compared the real revenue to the expected revenue in the contract of insurance, the claims work was decided. The results indicated that the Sentinel-2 satellite data could be used to identify the soybean planting distribution in the study area accurately, and to complete the yield estimation as soon as one week after the soybean harvest, which could guide the insurance company's claims work. The whole methodology is capable of aiding the claims work in revenue protection crop insurance.

The cubic boron nitride (cBN) single crystals present a higher expectation for deep ultraviolet (DUV) detectors. Simulations have been carried out using Synopsys TCAD to improve the performance of detectors. The light and dark current were observed by changing the parameters such as electrode spacing, doping concentration. The detector has the maximum response with the doping concentration of 1013 cm−3. The light-to-dark current is up to about 60 with an electrode width and spacing of 15 µm. The photocurrent was measured based on the prototype device. [ABSTRACT FROM AUTHOR]

The meta‐analysis aims to evaluate and compare fracture‐associated wound infections (FAWIs) following surgical treatment of fractures in human immunodeficiency virus (HIV)‐positive and HIV‐negative patients. Examinations comparing HIV‐negative to HIV‐negative for fracture was among the meta‐analysis from various languages that met the inclusion criteria. Using dichotomous random or fixed models, the results of these investigations were examined, and the Odds ratio (OR) with 95% confidence intervals was computed (CIs). 14 examinations from 1991 to 2023 were recruited for the current analysis including 3528 personals with fractures. HIV‐positive had significantly higher FAWI before antiretroviral (OR, 3.59; 95% CI, 2.01–6.41, p < 0.001) compared to HIV‐negative personals with fractures. However, no significant difference was found between HIV‐positive and HIV‐negative in FAWI after antiretroviral (OR, 0.58; 95% CI, 0.30–1.12, p = 0.10) in personals with fractures. The examined data revealed that HIV‐positive had significantly higher FAWI before antiretroviral, however, no significant difference was found in FAWI after antiretroviral compared to HIV‐negative personals with fractures. Nevertheless, caution should be exercised while interacting with its values since some of the chosen examinations were found with a low sample size and a low number of examinations were found for the comparisons studied for the meta‐analysis. [ABSTRACT FROM AUTHOR]

β -Ga2O3 films have been prepared on (0001) sapphire substrates with different off-axis angles toward $?> < 11 2 ˉ 0 > by sol â€" gel method and the solar-blind ultraviolet photodetectors (PDs) with metal-semiconductor-metal structure have been fabricated on these films. The effects of off-angled sapphire substrates on the surface morphology and crystal quality of β -Ga2O3 films have been systematically investigated. All the results indicate that to some extent the larger off-angled sapphire substrate could lead to the smoother surface morphology and higher crystal quality of β -Ga2O3 films. Reduction in the numbers of in-plane rotational domains for β -Ga2O3 films on off-angled substrates may result in the above-mentioned improvement, which have been confirmed by x-ray diffraction Ď• -scan. β -Ga2O3 film grown on ∼7° off-angled sapphire substrate has ( 400) preferred orientation with the single-domain growth mode. Besides, the performance of β -Ga2O3 PDs fabricated on different off-angled substrates show the same trend with changing the off-axis angles. The β -Ga2O3 PD fabricated on ∼7° off-angled sapphire substrate has excellent optical characteristics with large photocurrent to dark current ratio of 430.53 and high UVâ€"visible rejection ratio of 1.42 Ă— 103. These results would be helpful for improving the crystal-quality of hetero-epitaxy β -Ga2O3 films by using off-angled sapphire substrates and fabricating the cost-effective mass-producible solar-blind ultraviolet PDs by solâ€"gel method. [ABSTRACT FROM AUTHOR]