Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

Accurate and timely estimation of cotton yield over large areas is essential for precision agriculture, facilitating the operation of commodity markets and guiding agronomic management practices. Remote sensing (RS) and crop models are effective means to predict cotton yield in the field. The satellite vegetation indices (VIs) can describe crop yield variations over large areas but can’t take the exact environmental impact into consideration. Climate variables (CVs), the result of the influence of spatial heterogeneity in large regions, can provide environmental information for better estimation of cotton yield. In this study, the most important VIs and CVs for estimating county-level cotton yield across Xinjiang Province were screened out. We found that the VIs of canopy structure and chlorophyll contents, and the CVs of moisture, were the most significant factors for cotton growth. For yield estimation, we utilized four approaches: least absolute shrinkage and selection operator regression (LASSO), support vector regression (SVR), random forest regression (RFR) and long short-term memory (LSTM). Due to its ability to capture temporal features over the long term, LSTM performed best, with an R2 of 0.76, root mean square error (RMSE) of 150 kg/ha and relative RMSE (rRMSE) of 8.67%; moreover, an additional 10% of the variance could be explained by adding CVs to the VIs. For the within-season yield estimation using LSTM, predictions made 2 months before harvest were the most accurate (R2 = 0.65, RMSE = 220 kg/ha, rRMSE = 15.97%). Our study demonstrated the feasibility of yield estimation and early prediction at the county level over large cotton cultivation areas by integrating satellite and environmental data

Performance management algorithm of financial shared service center based on Internet of Things public cloud privacy protection

The Internet of Things (IoT) refers to the use of various communication technologies to achieve the interconnection of everything in cyberspace, and to achieve smart home and intelligent transportation, thus generating unprecedented amounts of data. In the financial sharing center, all businesses can extract effective data from these massive databases for analysis, and use data analysis tools to collect business, financial, human, process, knowledge and social data. At present, various types of IT (Internet Technology) systems have been widely used in financial sharing centers. However, a large number of sensitive data have also been generated. In order to protect these sensitive data, there is a high requirement for the personal information of IT system operation and financial sharing center personnel. In order to protect user data privacy, the optimal and most effective use of IT systems is an important issue that must be considered in privacy management. At present, there are many algorithms to protect data and privacy, but the effect is not ideal. Considering the balance between privacy issues, this paper proposed a K-means clustering algorithm based on IoT public cloud privacy protection technology to analyze the performance management of financial sharing center. The research results showed that before the improvement, the average number of employees who were dissatisfied with the post training ability and information platform construction ability of the financial sharing center was 57.9 and 57.8% respectively, more than half of them. After the improvement of IoT based public cloud privacy protection, the average number of employees dissatisfied with the post training ability and information platform construction ability of the financial sharing center was 5 and 3.9%, far less than the data prior to the improvement. It showed that IoT public cloud privacy protection was conducive to the performance management of the financial sharing center, and the relationship between the two was positive

Mesenchymal stem cells alleviate bacteria‐induced liver injury in mice by inducing regulatory dendritic cells

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102718/1/hep26670.pd

Empagliflozin alleviates atherosclerotic calcification by inhibiting osteogenic differentiation of vascular smooth muscle cells

SGLT-2 inhibitors, such as empagliflozin, have been shown to reduce the occurrence of cardiovascular events and delay the progression of atherosclerosis. However, its role in atherosclerotic calcification remains unclear. In this research, ApoE−/− mice were fed with western diet and empagliflozin was added to the drinking water for 24 weeks. Empagliflozin treatment significantly alleviated arterial calcification assessed by alizarin red and von kossa staining in aortic roots and reduced the lipid levels, while had little effect on body weight and blood glucose levels in ApoE−/− mice. In vitro studies, empagliflozin significantly inhibits calcification of primary vascular smooth muscle cells (VSMCs) and aortic rings induced by osteogenic media (OM) or inorganic phosphorus (Pi). RNA sequencing of VSMCs cultured in OM with or without empagliflozin showed that empagliflozin negatively regulated the osteogenic differentiation of VSMCs. And further studies confirmed that empagliflozin significantly inhibited osteogenic differentiation of VSMCs via qRT-PCR. Our study demonstrates that empagliflozin alleviates atherosclerotic calcification by inhibiting osteogenic differentiation of VSMCs, which addressed a critical need for the discovery of a drug-based therapeutic approach in the treatment of atherosclerotic calcification

Satellite Observed Positive Impacts of Fog on Vegetation

Fog is an important water source for many ecosystems, especially in drylands. Most fog‐vegetation studies focus on individual plant scale; the relationship between fog and vegetation function at larger spatial scales remains unclear. This hinders an accurate prediction of climate change impacts on dryland ecosystems. To this end, we examined the effect of fog on vegetation utilizing both optical and microwave remote sensing‐derived vegetation proxies and fog observations from two locations at Gobabeb and Marble Koppie within the fog‐dominated zone of the Namib Desert. Significantly positive relationships were found between fog and vegetation attributes from optical data at both locations. The positive relationship was also observed for microwave data at Gobabeb. Fog can explain about 10%–30% of variability in vegetation proxies. These findings suggested that fog impacts on vegetation can be quantitatively evaluated from space using remote sensing data, opening a new window for research on fog‐vegetation interactions

Laboratory Calibration of a Field Imaging Spectrometer System

A new Field Imaging Spectrometer System (FISS) based on a cooling area CCD was developed. This paper describes the imaging principle, structural design, and main parameters of the FISS sensor. The FISS was spectrally calibrated with a double grating monochromator to determine the center wavelength and FWHM of each band. Calibration results showed that the spectral range of the FISS system is 437–902 nm, the number of channels is 344 and the spectral resolution of each channel is better than 5 nm. An integrating sphere was used to achieve absolute radiometric calibration of the FISS with less than 5% calibration error for each band. There are 215 channels with signal to noise ratios (SNRs) greater than 500 (62.5% of the bands). The results demonstrated that the FISS has achieved high performance that assures the feasibility of its practical use in various fields

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe