Research on Target Detection Algorithm of Radar and Visible Image Fusion Based on Wavelet Transform

The target detection rate of unmanned surface vehicle is low because of waves, fog, background clutter and other environmental factors on the interference. Therefore, the paper studies the target detection algorithm of radar and visible image fusion based on wavelet transform. The visible image is preprocessed to ensure the detection effect. The multi-scale fractal model is used to extract the target features, and the difference between the fractal features of the target and the background is used to detect the target. The radar image is denoised by a combination of median filtering and wavelet transform. The processed visible light and radar image are fused with wavelet transform strategy. The coefficients of the low frequency sub-band are processed by the average fusion strategy. The coefficients of the high frequency sub-band are processed using a strategy with a higher absolute value. The standard deviation, the spatial frequency and the contrast resolution of the image fusion result are compared. The simulation results show that the processed image is better than the unprocessed image after the fusion

Effect of sulfur on enhancing nitrogen-doping and magnetic properties of carbon nanotubes

Sulfur (S) is introduced as an additive in the growth atmosphere of carbon nanotubes (CNTs) in the range of 940-1020°C. CNT products with distorted sidewalls can be obtained by S-assisted growth. Moreover, many fascinating CNT structures can also be found in samples grown with S addition, such as bamboo-like CNTs, twisted CNTs, arborization-like CNTs, and bead-like CNTs. Compared with CNTs grown without S, more nitrogen-doping content is achieved in CNTs with S addition, which is beneficial for the properties and applications of nitrogen-doped CNTs. In addition, S can also enhance the encapsulation of ferromagnetic materials and thus improve the soft magnetic properties of CNTs, which is favorable to the applications of CNTs in the electromagnetic wave-absorbing and magnetic data storage areas

Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2 = 0.48; RMSE= 151.92 kg ha−1) compared with the unassimilated results (R2 = 0.23;RMSE= 373.6 kg ha−1) and the TM LAI results (R2 = 0.27; RMSE= 191.6 kg ha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates

Joint retrieval of growing season corn canopy LAI and leaf chlorophyll content by fusing Sentinel-2 and MODIS images

Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R2) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1-0.2 and 0.0-0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R2 value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions

Robotic versus laparoscopic right hemicolectomy with complete mesocolic excision: a retrospective multicenter study with propensity score matching

ObjectiveDuring the past decade, the concept of complete mesocolic excision (CME) has been developed in an attempt to minimize recurrence for right-sided colon cancer. This study aims to compare outcomes of robotic versus laparoscopic right hemicolectomy with CME for right-sided colon cancer.MethodsWe performed a retrospective multicenter propensity score matching study. From July 2016 to July 2021, 382 consecutive patients from different Chinese surgical departments were available for inclusion out of an initial cohort of 412, who underwent robotic or laparoscopic right hemicolectomy with CME. Data of all patients were retrospectively collected and reviewed. Of these, 149 cases were performed by a robotic approach, while the other 233 cases were done by laparoscopy. Propensity score matching was applied at a ratio of 1:1 to compare perioperative, pathologic, and oncologic outcomes between the robotic and the laparoscopic groups (n = 142).ResultsBefore propensity score matching, there were no statistical differences regarding the sex, history of abdominal surgery, body mass index (BMI), American Joint Committee on Cancer (AJCC) staging system, tumor location, and center between groups (p > 0.05), while a significant difference was observed regarding age (p = 0.029). After matching, two comparable groups of 142 cases were obtained with equivalent patient characteristics (p > 0.05). Blood loss, time to oral intake, return of bowel function, length of stay, and complications were not different between groups (p > 0.05). The robotic group showed a significantly lower conversion rate (0% vs. 4.2%, p = 0.03), but a longer operative time (200.9 min vs. 182.3 min, p < 0.001) and a higher total hospital cost (85,016 RMB vs. 58,266 RMB, p < 0.001) compared with the laparoscopic group. The number of harvested lymph nodes was comparable (20.4 vs. 20.5, p = 0.861). Incidence of complications, mortality, and pathologic outcomes were similar between groups (p > 0.05). The 2-year disease-free survival rates were 84.9% and 87.1% (p = 0.679), and the overall survival rates between groups were 83.8% and 80.7% (p = 0.943).ConclusionDespite the limitations of a retrospective analysis, the outcomes of robotic right hemicolectomy with CME were comparable to the laparoscopic procedures with fewer conversions to open surgery. More clinical advantages of the robotic surgery system need to be further confirmed by well-conducted randomized clinical trials with large cohorts of patients

Interaction between human serum albumin and cholesterol-grafted polyglutamate as the potential carriers of protein drugs

Currently there is no successful platform technology for the sustained release of protein drugs. It seems inevitable to specifically develop new materials for such purpose, and hence the understanding of protein–material interactions is highly desirable. In this study, we synthesized cholesterol-grafted polyglutamate (PGA-g-Chol) as a hydrophobically-modified polypeptide, and thoroughly characterized its interaction with a model protein (human serum albumin) in the aqueous solution by using circular dichroism, fluorescence methods, and light scattering. With the protein concentration fixed at 5 μmol/L, adding PGA-g-Chol polymers into the solution resulted in continuous blue shift of the protein fluorescence (from 339 to 332 nm), until the polymer molar concentration reached the same value as the protein. In contrast, the un-modified polyglutamate polymers apparently neither affected the protein microenvironment nor formed aggregates. Based on the experimental data, we proposed a physical picture for such protein–polymer systems, where the polymer first bind with the protein in a 1:1 molar ratio via a fraction of their hydrophobic pendant cholesterol resides along the polymer chain. In this protein/polymer complex, there are excess unbound cholesterol residues. As the polymer concentration increases, the polymers form multi-polymer aggregates around 200 nm in diameter via the same hydrophobic cholesterol residues. The protein/polymer complex also participate in the aggregation via their excess cholesterol residues, and consequently the proteins are encapsulated into the nanoparticles. The encapsulation was also found to increase the thermal stability of the model protein. KEY WORDS: Protein drug delivery, Polypeptide, Self-assemble, Human serum albumin, Interaction, Carrie

Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2 = 0.48; RMSE= 151.92 kg ha−1) compared with the unassimilated results (R2 = 0.23;RMSE= 373.6 kg ha−1) and the TM LAI results (R2 = 0.27; RMSE= 191.6 kg ha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs) derived from TERRA and AQUA MODIS data. Linear regression models were calibrated using LSTs from 2003 to 2011 and validated using LST data from 2012 to 2013, combined with meteorological station data. The results show that these models can provide a robust estimation of measured daily mean air temperature and that models that only accounted for meteorological data from rural regions performed best. Daily mean air temperature maps were generated from each of four MODIS LST products and merged using different strategies that combined the four MODIS products in different orders when data from one product was unavailable for a pixel. The annual average spatial coverage increased from 20.28% to 55.46% in 2012 and 28.31% to 44.92% in 2013.The root-mean-square and mean absolute errors (RMSE and MAE) for the optimal image merging strategy were 2.41 and 1.84, respectively. Compared with the least-effective strategy, the RMSE and MAE decreased by 17.2% and 17.8%, respectively. The interpolation algorithm uses the available pixels from images with consecutive dates in a sliding-window mode. The most appropriate window size was selected based on the absolute spatial bias in the study area. With an optimal window size of 33 × 33 pixels, this approach increased data coverage by up to 76.99% in 2012 and 89.67% in 2013

Provenance of Jurassic Sediments from Yuqia Sandstone-Type Uranium Deposits in the Northern Margin of Qaidam Basin, China and Its Implications for Uranium Mineralization

The Yuqia uranium deposit is a newly discovered sandstone-type uranium deposit in the northern margin of the Qaidam Basin. Concerning the sources of sediment in the basin, most scholars have focused on the study of Cenozoic sediment sources in the northern part of the basin, rather than on the study of Jurassic provenance and its implications for uranium mineralization. In this paper, the Jurassic sandstones in the area were selected for petrography, petrogeochemistry and electron microprobe analysis (EPMA), and the significance of sedimentary provenance and uranium metallogeny were further discussed, based on the previous data of detrital zircon chronology. It is reported here for the first time that coffinite and pitchblende are the main contributors. Independent uranium minerals in the region mainly occur in strawberry pyrite, xenotime, and margins or of quartz. The rocks in the source area, Jurassic sandstones, were mainly formed under the tectonic setting of the active continental margin; however, the sediments are mainly derived from the Indosinian and Paleozoic granitoids exposed in the northern margin of the Qaidam Basin. The uranium-rich granites in the source area led to the preconcentration of uranium in the sandstone of the target layer, and the uranium was brought into the ore-bearing target layer through uranium-bearing oxygenated water. The reduction reaction occurred under the action of reducing matter, and finally, the U6+ was reduced to U4+ in the formation of coffinite and pitchblende

Combined impact of the cold vortex and atmospheric blocking on cold outbreaks over East Asia and the potential for short-range prediction of such occurrences

This study explores the consequences of independent and combined effects of blockings on the northeast Asian cold vortex (NACV), and corresponding cold outbreaks over East Asia (EA) during boreal winters of 1979–2019. The results show that the development of NACV is closely associated with blocking over the Ural Mountains (UB) upstream and eastern Siberia to mid-North Pacific (SPB) downstream. Here we focus on the initial periods before the peak day of NACV events. It is found that the strong NACV events are usually induced by the initial-UB situation, leading to the greatest temperature drop in EA. While the weak NACV events may be associated with the initial-SPB condition, which can bring less dramatic outbreaks but longer duration, owing to the Ural ridge that formed by the westward shift of SPB. Furthermore, an SPB-UB relay effect is discovered against the background of a negative Arctic Oscillation pattern. In such cases, UB is formed by the westward shift of downstream SPB after the occurrence of NACV, forming a relay effect that motivates the second NACV process, hence prolonging the duration of cold anomalies in EA. These findings highlight the importance of the combined effect of blockings and NACV in the intraseasonal time scale. Compared to the ‘Initial-UB’ and ‘Initial-SPB’ situation, this ‘SPB-UB relay’ scenario can produce longer-lasting cold extreme in EA, which may be indicative of the short-term weather forecasting of such extreme cold weather