Analysis and Compensation of Lorentz Force Magnetic Bearing Magnetic Flux Density Uniformity Error.

Aiming at the influence of the magnetic flux density uniformity error (MFDUE) of the Lorentz force magnetic bearing (LFMB) on the sensitivity accuracy of magnetically suspended control and sensing gyroscopes (MSCSGs) on the angular rate of a spacecraft, a high precision measurement method of the angular rate of a spacecraft based on the MFDUE compensation of LFMB is proposed. Firstly, the structure of MSCSG and the sensitivity principle of MSCSG to the spacecraft angular rate are introduced. The mechanism influencing the accuracy of MSCSG to spacecraft angular rate sensitivity is deduced based on the definition of magnetic flux density uniformity. Secondly, the 3D magnetic flux distribution of LFMB is analyzed using ANSYS. The relationship between the rotor tilt angle, tilt angular rate, and magnetic flux density is established. The induced current calculation model due to MFDUE is proposed, and the LFMB magnetic flux density error compensation is realized. Finally, the simulation results show that the estimation accuracy of the induced current by the proposed method can reach 96%, and the simulation and the experiment show that the error compensation method can improve the accuracy of MSCSG in measuring the spacecraft angular rate by 12.5%.

Posterior capsular radial sign: a novel method to confirm anterior vitreous cortex resection in phacovitrectomy.

BACKGROUND: The main purpose of this paper is to introduce a method that can accurately locate the posterior capsule of the lens to facilitate a relatively complete resection of the anterior vitreous body. METHODS: A total of 51 patients in the experimental group and control group were enrolled in this study. Phacoemulsification combined with vitrectomy was performed in all cases. After the cataract procedure was completed in the control group, the surgeon performed a conventional anterior vitrectomy with the operative eye. In the experimental group, anterior vitrectomy was performed according to the threadiness corrugation of the posterior capsule of the lens. During the operation, with the help of triamcinolone, two surgeons confirmed the resection of the anterior vitreous cortex; the best corrected visual acuity and intraocular pressure of all patients were recorded at 1 week, 1 month and 3 months after surgery. RESULTS: Fifty patients underwent phacoemulsification combined with vitrectomy, except one patient in the experimental group who was lost to follow-up. After surgery, no significant complications were observed in all patients except two patients in the control group with temporary increases in intraocular pressure. There was no significant difference in preoperative visual acuity between the two groups (t = 0.83, P = 0.25). Both groups had varying degrees of improvement in best corrected visual acuity at 1 week, 1 month and 3 months after surgery. Moreover, there was no significant difference in BCVA between the two groups at the three follow-up time points (t=-1.15, -1.65, -1.09, P = 0.53, 0.21, 0.23). After surgery, no significant complications were observed in all patients except two patients in the control group with temporary increases in intraocular pressure. Incomplete resection of the anterior vitreous cortex was observed in 2 patients in each group, but there was no significant difference (χ2 = 7.81, P > 0.05). CONCLUSION: In the process of cataract surgery combined with vitrectomy, thready corrugation appears in the posterior capsule of the lens and is an important sign of its localization. Anterior vitrectomy can be accomplished safely and effectively with the help of thread-like corrugation, and the surgical effect is almost the same as that of traditional surgery. Especially suitable for beginners in vitreous surgery.

Effect of Negative Online Reviews and Physician Responses on Health Consumers' Choice: Experimental Study.

BACKGROUND: The COVID-19 pandemic has highlighted the importance of online medical services. Although some researchers have investigated how numerical ratings affect consumer choice, limited studies have focused on the effect of negative reviews that most concern physicians. OBJECTIVE: This study aimed to investigate how negative review features, including proportion (low/high), claim type (evaluative/factual), and physician response (absence/presence), influence consumers' physician evaluation process under conditions in which a physician's overall rating is high. METHODS: Using a 2×2×2 between-subject decision-controlled experiment, this study examined participants' judgment on physicians with different textual reviews. Collected data were analyzed using the t test and partial least squares-structural equation modeling. RESULTS: Negative reviews decreased consumers' physician selection intention. The negative review proportion (ß=-0.371, P<.001) and claim type (ß=-0.343, P<.001) had a greater effect on consumers' physician selection intention compared to the physician response (ß=0.194, P<.001). A high negative review proportion, factual negative reviews, and the absence of a physician response significantly reduced consumers' physician selection intention compared to their counterparts. Consumers' locus attributions on the negative reviews affected their evaluation process. Physician attribution mediated the effects of review proportion (ß=-0.150, P<.001), review claim type (ß=-0.068, P=.01), and physician response (ß=0.167, P<.001) on consumer choice. Reviewer attribution also mediated the effects of review proportion (ß=-0.071, P<.001), review claim type (ß=-0.025, P=.01), and physician response (ß=0.096, P<.001) on consumer choice. The moderating effects of the physician response on the relationship between review proportion and physician attribution (ß=-0.185, P<.001), review proportion and reviewer attribution (ß=-0.110, P<.001), claim type and physician attribution (ß=-0.123, P=.003), and claim type and reviewer attribution (ß=-0.074, P=.04) were all significant. CONCLUSIONS: Negative review features and the physician response significantly influence consumer choice through the causal attribution to physicians and reviewers. Physician attribution has a greater effect on consumers' physician selection intention than reviewer attribution does. The presence of a physician response decreases the influence of negative reviews through direct and moderating effects. We propose some practical implications for physicians, health care providers, and online medical service platforms.

BMI and plasma lipid levels with risk of proliferative diabetic retinopathy: a univariable and multivariable Mendelian randomization study.

Background: The study aimed to determine whether a causal effect exists between body mass index (BMI) or plasma lipid levels and proliferative diabetic retinopathy (PDR) risk in humans. Methods: We utilized univariable (UVMR) and multivariable two-sample Mendelian randomization (MVMR) analyses to confirm the effects of BMI and plasma lipid levels on the risk of PDR. Genetic variants associated with BMI and three plasma lipids were obtained from GWAS summary datasets generated by many different consortia and were deposited in the MR-Base database. The GWAS summary data for PDR from the FinnGen biobank included 2,12,889 participants of European ancestry (8,681 cases and 2,04,208 controls). Inverse variance weighted (IVW) was applied as the main MR analysis. Sensitivity analysis was used to evaluate the robustness of our findings. Results: In the UVMR analysis, the causal associations of genetically predicted BMI with PDR presented a positive association (OR = 1.120, 95% CI = 1.076-1.167, P < 0.001), and the lower HDL-C level was associated with a higher risk of PDR (OR = 0.898, 95% CI = 0.811-0.995, P = 0.040). No evidence of an association between LDL-C or TG levels (P > 0.05) and PDR risk was found. In the MVMR analysis controlling for the HDL-C level, there was strong evidence for a direct causal effect of BMI on the risk of PDR (OR = 1.106, 95%CI = 1.049, 1.166, P < 0.001, IVW). After adjusting for BMI, there was no evidence for a direct causal effect of the HDL-C level on the risk of PDR (OR = 0.911, 95% CI = 0.823, 1.008, P = 0.072). Sensitivity analyses confirmed that the results were reliable and stable. Conclusion: Robust evidence was demonstrated for an independent, causal effect of BMI in increasing the risk of PDR. Further studies are required to understand the potential biological mechanisms underlying this causal relationship.

Establishing a non-hydrostatic global atmospheric modeling system at 3-km horizontal resolution with aerosol feedbacks on the Sunway supercomputer of China.

During the era of global warming and highly urbanized development, extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and property. Despite the vast development of atmospheric models, there still exist substantial numerical forecast biases objectively. To accurately predict extreme weather, severe air pollution, and abrupt climate change, numerical atmospheric model requires not only to simulate meteorology and atmospheric compositions simultaneously involving many sophisticated physical and chemical processes but also at high spatiotemporal resolution. Global integrated atmospheric simulation at spatial resolutions of a few kilometers remains challenging due to its intensive computational and input/output (I/O) requirement. Through multi-dimension-parallelism structuring, aggressive and finer-grained optimizing, manual vectorizing, and parallelized I/O fragmenting, an integrated Atmospheric Model Across Scales (iAMAS) was established on the new Sunway supercomputer platform to significantly increase the computational efficiency and reduce the I/O cost. The global 3-km atmospheric simulation for meteorology with online integrated aerosol feedbacks with iAMAS was scaled to 39,000,000 processor cores and achieved the speed of 0.82 simulation day per hour (SDPH) with routine I/O, which enabled us to perform 5-day global weather forecast at 3-km horizontal resolution with online natural aerosol impacts. The results demonstrate the promising future that the increasing of spatial resolution to a few kilometers with online integrated aerosol feedbacks may significantly improve the global weather forecast.

ITUnet: Integration Of Transformers And Unet For Organs-At-Risk Segmentation.

Recently, convolutional neural network(CNN) has achieved great success in medical image segmentation. However, due to the limitation of convolutional receptive field, the pure convolutional neural network is difficult to further improve its performance. Given the outstanding ability of transformers in extracting the long-range dependency, some works have successfully applied it to computer vision and achieved better results than CNN in some tasks. Based on transformers could remedy the shortage of CNN, in this paper, we propose ITUnet, a segmentation network using CNN and transformers as features extractor. The combination of CNN and transformers enables the network to learn both short- and long-range dependency of features, which is beneficial to segmentation tasks. We evaluate our method on a head-and-neck CT dataset which has 18 kinds of organs to be segmented. The experimental results demonstrate that our proposed method shows better accuracy and robustness, the proposed methods achieve the Dice score of 77.72 and the 95% Hausdorff Distance of 2.31, outperforming the existing methods.

Whole Slide Image Multi-Classification of Cervical Epithelial Lesions Based on Unsupervised Pre-training.

Cervical cancer has become one of the important factors threatening women's health. Histopathological diagnosis is the most important criterion for cervical cancer diagnosis and treatment. Accurate classification of lesion degree of cervical epithelium by analyzing whole slide images (WSIs) can effectively improve the therapeutic effect and prognosis. However, classification of cervical lesion degree shows poor reproductivity due to lack of standardisation and is subjective among clinicians. In addition, due to the lack of large-scale finely annotated datasets, current deep learning methods do not perform well on this task. In this paper, we propose a two-stage method based on unsupervised pre-training to solve this multi-classification task. Our method first applied a patch-level network to predict the patch-level score and generate a heatmap that can highlight the lesion area. This network is pre-trained using an unsupervised method and verified on a public dataset. Then without extracting manual features, heatmaps are fed into a convolutional neural network (CNN) model directly for the WSI-level prediction. Our approach achieved an accuracy of 81.19% and a custom metric score of 0.9495 on the public cervical cancer WSI dataset, which is the highest in the public so far.

[Structural Design and Finite Element Analysis of New Type Posterior Integral Glaucoma Valve].

The treatment of refractory Glaucoma is a difficult problem in clinical ophthalmology. For refractory glaucoma patients with hyphema, shallow anterior chamber, anterior conglutination of peripheral chamber angle, corneal endothelium dystrophy or decompensated, at present, there is no effective treatment. In order to solve this problem, a new type posterior integral glaucoma valve with IOP control device was designed using medical titanium alloy, and the valve model was established by Abaqus software, and the stiffness and preload of the valve were analyzed by finite element method. The results showed that the opening and closing of the valve were controlled automatically by the pressure difference between the front and back of the valve, and the opening and flow rate of the valve increase dynamically with the increase of intraocular pressure, and finally reached the set ideal IOP value of steady state.

The Development History and Research Tendency of Medical Informatics: Topic Evolution Analysis.

BACKGROUND: Medical informatics has attracted the attention of researchers worldwide. It is necessary to understand the development of its research hot spots as well as directions for future research. OBJECTIVE: The aim of this study is to explore the evolution of medical informatics research topics by analyzing research articles published between 1964 and 2020. METHODS: A total of 56,466 publications were collected from 27 representative medical informatics journals indexed by the Web of Science Core Collection. We identified the research stages based on the literature growth curve, extracted research topics using the latent Dirichlet allocation model, and analyzed topic evolution patterns by calculating the cosine similarity between topics from the adjacent stages. RESULTS: The following three research stages were identified: early birth, early development, and rapid development. Medical informatics has entered the fast development stage, with literature growing exponentially. Research topics in medical informatics can be classified into the following two categories: data-centered studies and people-centered studies. Medical data analysis has been a research hot spot across all 3 stages, and the integration of emerging technologies into data analysis might be a future hot spot. Researchers have focused more on user needs in the last 2 stages. Another potential hot spot might be how to meet user needs and improve the usability of health tools. CONCLUSIONS: Our study provides a comprehensive understanding of research hot spots in medical informatics, as well as evolution patterns among them, which was helpful for researchers to grasp research trends and design their studies.

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images.

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models' classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models' overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

Abiotic and biotic factors contribute to CO2 exchange variation at the hourly scale in a semiarid maize cropland.

Understanding the variables influencing the carbon budget in agricultural ecosystems is crucial for the prediction of future carbon dynamics. The purpose of this study was to identify the biotic and abiotic determinants of the net ecosystem CO2 exchange (NEE) and net assimilation rate (NPP) in a semiarid maize cropland. The CO2 exchange (NEE and NPP) was measured at different growth stages of maize plants using an improved chamber methodology. Heat map clustering of the correlation coefficients between CO2 exchange and its driving factors demonstrated that soil temperature and air humidity were positively correlated with CO2 emissions regardless of daytime or nighttime, while other factors affecting CO2 exchange were negatively correlated with emissions during daytime yet positively correlated during nighttime. The machine learning algorithm random forest (RF) and structural equation modeling (SEM) were used to analyze the effects of different factors on CO2 exchange. The RF analysis results indicated that for CO2 exchange in the daytime, photosynthetically active radiation (PAR) was the most important variable and presented an importance score of 0.574 for NEE and 0.558 for NPP. The SEM results indicated that in the daytime PAR exerted significant direct and indirect effects on both NEE and NPP, and the standardized direct and indirect effects were -0.668 and 0.022, respectively, for NEE, and the effects were 0.655 and -0.011, respectively for NPP. Like PAR, soil water content also exerted significant direct and indirect effects on both NEE and NPP, but the remaining factors affecting CO2 exchange only have one of the direct or indirect effects, sometimes neither. For CO2 exchange at night, the leaf area was the most important variable and presented an importance score of 0.72 for NEE and 0.45 for NPP. At night, both the direct and indirect effects of most abiotic factors on NEE and NPP were significant.

Developing an Automated Gas Sampling Chamber for Measuring Variations in CO2 Exchange in a Maize Ecosystem at Night.

The measurement of net ecosystem exchange (NEE) of field maize at a plot-sized scale is of great significance for assessing carbon emissions. Chamber methods remain the sole approach for measuring NEE at a plot-sized scale. However, traditional chamber methods are disadvantaged by their high labor intensity, significant resultant changes in microclimate, and significant impact on the physiology of crops. Therefore, an automated portable chamber with an air humidity control system to determinate the nighttime variation of NEE in field maize was developed. The chamber system can automatically open and close the chamber, and regularly collect gas in the chamber for laboratory analysis. Furthermore, a humidity control system was created to control the air humidity of the chamber. Chamber performance test results show that the maximum difference between the temperature and humidity outside and inside the chamber was 0.457 °C and 5.6%, respectively, during the NEE measuring period. Inside the chamber, the leaf temperature fluctuation range and the maximum relative change of the maize leaf respiration rate were 0.3 to 0.3 °C and 23.2015%, respectively. We verified a series of measurements of NEE using the dynamic and static closed chamber methods. The results show a good common point between the two measurement methods (N = 10, R2 = 0.986; and mean difference: △CO2 = 0.079  ). This automated chamber was found to be useful for reducing the labor requirement and improving the time resolution of NEE monitoring. In the future, the relationship between the humidity control system and chamber volume can be studied to control the microclimate change more accurately.

Development of Response Surface Model of Endurance Time and Structural Parameter Optimization for a Tailsitter UAV.

This study designed a vertical take-off and landing tailsitter unmanned aerial vehicle (UAV) with a long endurance time. Nine parameters of the tailsitter UAV were investigated. Using a 2k full factorial test, 512 experiments on the nine parameters were conducted at their maximum and minimum values. The time coefficient and air resistance were calculated using the computational fluid dynamics (CFD) method under different parameter combinations. The analysis of variance determined that the specific factors influencing the time coefficient and air resistance were the root chord, wingtip chord, wingspan, and sweep angle. By carrying out a central composite design (CCD) test, 25 sample points of the four particular factors were constructed. The time coefficient and air resistance were simulated under different structural parameter combinations using the CFD method. CFD simulation was verified by carrying out a wind tunnel test, and the results revealed that the aerodynamic coefficient error was less than 5%, while the air resistance error was less than 6%. The response surface methodology (RSM) for the time coefficient and air resistance was established using a genetic aggregation method. A multi-objective genetic algorithm (MOGA) was used to optimize the parameters with regard to the maximum time coefficient and minimum air resistance. The optimal structural parameters were wing root chord length at 315 mm, wingtip chord length at 182 mm, wingspan length at 1198 mm, and sweep angle at 16°. Compared with the original layout and size, the time coefficient of the new design of the tailsitter UAV improved by 19.5%, while the air resistance reduced by 34.78%. The results obtained by this study are significant for the design of tailsitter UAVs.

An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO2 and Atmospheric Particulate Matter in Laboratory.

We developed and tested an unmanned aerial vehicle-based gas sampling system (UGSS) for collecting gases and atmospheric particulate matter (PM). The system applies an alternative way of collecting both vertical and horizontal transects of trace gases in order to analyze them in the laboratory. To identify the best position of the UGSS intake port, aerodynamic flow simulations and experimental verifications of propeller airflow were conducted with an unmanned aerial vehicle (UAV) in hover mode. The UGSS will automatically replace the original gas in the system with gas from a target location to avoid the original gas being stored in the air bags. Experimental results show that the UGSS needs 5 s to replace the system's own original gas using its pump. CO2 and PM2.5/10 above the corn field are used as the test species to validate the accuracy of the CO2 gas and PM concentrations collected by UGSS. Deming regression analyses showed good agreement between the measurements from the UGSS and the ground sampling station (y = 1.027x - 11.239, Pearson's correlation coefficient of 0.98 for CO2; y = 0.992x + 0.704, Pearson's correlation coefficient of 0.99 for PM).The UGSS provides a measuring method that actively collects gases and PM for manual analyses in the laboratory.

Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments.

An unmanned aerial vehicle (UAV) particulate-matter (PM) monitoring system was developed that can perform three-dimensional stereoscopic observation of PM2.5 and PM10 in the atmosphere. The UAV monitoring system was mainly integrated by modules of data acquisition and processing, wireless data transmission, and global positioning system (GPS). Particularly, in this study, a ground measurement-control subsystem was added that can display and store collected data in real time and set up measurement scenarios, data-storage modes, and system sampling frequency as needed. The UAV PM monitoring system was calibrated via comparison with a national air-quality monitoring station; the data of both systems were highly correlated. Since rotation of the UAV propeller affects measured PM concentration, this study specifically tested this effect by setting up another identical monitoring system fixed at a tower as reference. The UAV systems worked simultaneously to collect data for comparison. A correction method for the propeller disturbance was proposed. Averaged relative errors for the PM2.5 and PM10 concentrations measured by the two systems were 6.2% and 6.6%, respectively, implying that the UAV system could be used for monitoring PM in an atmosphere environment.

Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices.

The rapid, accurate, and real-time estimation of crop coefficients at the farm scale is one of the key prerequisites in precision agricultural water management. This study aimed to map the maize crop coefficient (Kc) with improved accuracy under different levels of deficit irrigation. The proposed method for estimating the Kc is based on multispectral images of high spatial resolution taken using an unmanned aerial vehicle (UAV). The analysis was performed on five experimental plots using Kc values measured from the daily soil water balance in Ordos, Inner Mongolia, China. To accurately estimate the Kc, the fraction of vegetation cover (fc) derived from the normalized difference vegetation index (NDVI) was used to compare with field measurements, and the stress coefficients (Ks) calculated from two vegetation index (VI) regression models were compared. The results showed that the NDVI values under different levels of deficit irrigation had no significant difference in the reproductive stage but changed significantly in the maturation stage, with a decrease of 0.09 with 72% water applied difference. The fc calculated from the NDVI had a high correlation with field measurement data, with a coefficient of determination (R2) of 0.93. The ratios of transformed chlorophyll absorption in reflectance index (TCARI) to renormalized difference vegetation index (RDVI) and TCARI to soil-adjusted vegetation index (SAVI) were used, respectively, to establish two types of Ks regression models to retrieve Kc. Compared to the TCARI/SAVI model, the TCARI/RDVI model under different levels of deficit irrigation had better correlation with Kc, with R2 and root-mean-square error (RMSE) values ranging from 0.68 to 0.80 and from 0.140 to 0.232, respectively. Compared to Kc calculated from on-site measurements, the Kc values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops. Overall, use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale.

Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model.

OBJECTIVE: Dyslipidemia has been recognized as a major risk factor of several diseases, and early prevention and management of dyslipidemia is effective in the primary prevention of cardiovascular events. The present study aims to develop risk models for predicting dyslipidemia using Random Survival Forest (RSF), which take the complex relationship between the variables into account. METHODS: We used data from 6328 participants aged between 19 and 90 years free of dyslipidemia at baseline with a maximum follow-up of 5 years. RSF was applied to develop gender-specific risk model for predicting dyslipidemia using variables from anthropometric and laboratory test in the cohort. Cox regression was also adopted in comparison with the RSF model, and Harrell's concordance statistic with 10-fold cross-validation was used to validate the models. RESULTS: The incidence density of dyslipidemia was 101/1000 in total and subgroup incidence densities were 121/1000 for men and 69/1000 for women. Twenty-four predictors were identified in the prediction model of males and 23 in females. The C-statistics of the prediction models for males and females were 0.731 and 0.801, respectively. The RSF model shows better discriminative performance than CPH model (0.719 for males and 0.787 for females). Moreover, some predictors were observed to have a nonlinear effect on dyslipidemia. CONCLUSION: The RSF model is a promising method in identifying high-risk individuals for the prevention of dyslipidemia and related diseases.

Maize Canopy Temperature Extracted From UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring.

To identify drought-tolerant crop cultivars or achieve a balance between water use and yield, accurate measurements of crop water stress are needed. In this study, the canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red-green-blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV). To reduce the number of parameters for crop water stress monitoring, four simple methods that require only Tc were identified: Tc, degrees above non-stress, standard deviation of Tc, and variation coefficient of Tc. The ground-truth temperatures obtained using a handheld infrared thermometer were used to calibrate the temperature obtained from the UAV thermal images and to evaluate the Tc extraction results. Measured leaf stomatal conductance values were used to evaluate the performance of the four Tc-based crop water stress indicators. The results showed a strong correlation between ground-truth Tc and Tc extracted by the red-green ratio index (RGRI)-Otsu method proposed in this study, with a coefficient of determination of 0.94 (n = 15) and root mean square error value of 0.7°C. The RGRI-Otsu method was most accurate for estimating temperatures around 32.9°C, but the magnitude of residuals increased above and below this value. This phenomenon may be attributable to changes in canopy cover (leaf curling) under water stress, resulting in changes in the proportion of exposed sunlit soil in UAV thermal orthophotographs. Therefore, to improve the accuracy of maize canopy detection and extraction, optimal methods and better strategies for eliminating mixed pixels are needed. This study demonstrates the potential of using high-resolution UAV RGB images to supplement UAV thermal images for the accurate extraction of maize Tc.

The potential agents from food for preventing leukopenia induced by benzene: garlic preparations.

Leukopenia is the early clinical manifestation of benzene poisoning. The aim of our research was to evaluate the preventive effects of three kinds of garlic preparations on benzene induced leukopenia. The mouse model of Leukopenia was established with benzene orally. At the same time, mice were administrated with garlic homogenate (GH), garlic oil (GO) or diallyl trisulfide (DATS) as preventional measures. The counts of white blood cells (WBC), the organ indexes, pathological examinations, blood biochemical parameters, weight gains, and food intakes were evaluated to observe the protective effect and potential adverse events. The results demonstrated that the counts of WBC increased by 144.04%, 140.07%, and 148.34%, respectively, after intervention by GH (400 mg/kg), GO (60 mg/kg) and DATS (30 mg/kg), compared with that in the model group. The spleen and thymus indexes in the benzene model group were 44.99% and 54.04% lower than those in the blank control group, the number of spleen nodules reduced and the thymus atrophy, which were restored by three garlic preparations at different degree. The results suggested that the three preparations all could prevent the leukopenia and protect the organ injuries induced by benzene. However, the spleen index and weight gains revealed that GH and GO brought more adverse events than DATS.

Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing.

Rational utilization of water resources is one of the major methods of water conservation. There are significant differences in the irrigation needs of different agricultural fields because of their spatial variability. Therefore, a decision support system for variable rate irrigation (DSS-VRI) by center pivot was developed. This system can process multi-spectral images taken by unmanned aerial vehicles (UAVs) and obtain the vegetation index (VI). The crop evapotranspiration model (ETc) and crop water stress index (CWSI) were obtained from their established relationships with the VIs. The inputs to the fuzzy inference system were constituted with ETc, CWSI and precipitation. To provide guidance for users, the duty-cycle control map was outputted using ambiguity resolution. The control command contained in the map adjusted the duty cycle of the solenoid valve, and then changed the irrigation amount. A water stress experiment was designed to verify the rationality of the DSS-VRI. The results showed that the more severe water stress is, the more irrigation is obtained, consistent with the expected results. Meanwhile, a user-friendly software interface was developed to implement the DSS-VRI function.