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Objective To observe the clinical efficacy and safety of Shuangshi Tonglin capsules combined with doxycycline hydrochloride in treating male non-gonococcal urethritis (Damp-Heat Pouring Downward type). Methods From March 2022 to March 2023, 104 patients with non-gonococcal urethritis (Damp-Heat Pouring Downward type) were randomly divided into two groups based on block randomization, including the treatment group (n =52) and the control group ( n = 52) . The patients in the control group were orally administrated with doxycycline hydrochloride for 7 days, and the treatment group with Shuangshi Tonglin capsules in combination with doxycycline hydrochloride for 7 days. Both groups of patients were re-examined at the 7th day after stopping the medication. The clinical efficacy of the two groups was observed and compared, as well as the traditional Chinese medicine symptom scores, the time of clinical symptom regression, laboratory test results for the pathogen, and the incidence of adverse reactions. Results The total efficacy rate for the treatment group and the control group was 96. 15% and 84. 62%, respectively, with a significant difference (P <0. 05) . Symptom regression times of the treatment group for frequent urination, urgency, pain, redness and swelling of the urethral opening, itching, and urethral discharge[ (3.12 ±0.38), (3.34 +0.44), (4.12 ±0.68), (4.03 ±0.59), (3.36 ±0.32), and (3.56 ±0.52) days] were all better than those of the control group[ (4.26 ± 0.76), (4.76 ±0.83), (4.89 ±0.89), (5.12 ±0.92), (4.56 ± 0.82), and (4.64 ±0.78) days], with statistical significance (P <0. 05) . Symptom scores of both groups after treatment showed an obvious decrease (P <0.05), and the scores for the treatment group for each symptom [(0.86 ±0.18), (0.94 ± 0.23), (0.79 ±0. 6), (1.01 ±0.27), (0.83 ± 0.20), and (0.97 ±0.25)] were all notably lower than those of the control group[ (1.38 ±0.46), (1.54 ± 0.52), (1.46 ±0.49), (1.50 ± 0.51), (1.44 ± 0.43), and (1.32 ± 0.36)], with a significant difference (P <0. 05) . The pathogen clearance rate of the treatment group was 96. 15%, which was higher than that of in the control group (82. 69%, P <0.05) . The cases with gastrointestinal reactions, headaches, maculopapular rash, and dry mouth in the treatment group were 1, 1, 0, and 1 and the cases in the control group were 1, 2, 2, and 0 cases. The incidence of adverse reactions in the treatment group was 5.77%, which was lower than that in the control group. However, the difference was not statistically significant (P >0.05). Conclusions The combined treatment of Shuangshi Tonglin capsules and doxycycline hydrochloride for male non-gonocoecal urethritis of the Damp-Heat Pouring Downward type can significantly enhance the clinical pathogen clearance rate, effectively improve clinical symptoms and the life quality of patients. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived SIF for drought monitoring has not been widely investigated due to a lack of data. The lack of high spatiotemporal resolution satellite SIF hinders the resolution enhancement of SIF derived by downscaling or reconstruction algorithms. The TROPOspheric Monitoring Instrument (TROPOMI) SIF provides an alternative with finer spatiotemporal resolution. We present an operational downscaling method to generate 500 m 16-day SIF (TSIF) using Neural Networks over a local spatiotemporal window. The results showed that our method is very robust against overfitting, and TSIF has a strong spatiotemporal consistency with TROPOMI SIF (TROPOSIF) with R 2 = 0.956 and RMSE = 0.054   mWm − 2 sr − 1 nm − 1 . Comparison with another SIF product (CASIF) showed a spatiotemporal consistency with TSIF. Comparison with tower gross primary productivity (GPP) from AmeriFlux in California showed a strong correlation with R 2 for multiple ecosystems ranging from 0.58 to 0.88. We explored the capacity of TSIF for monitoring a drought event in Henan, China, showing that TSIF is more sensitive to drought and precipitation compared to the Enhanced Vegetation Index. Our TSIF is a very promising indicator for regional drought monitoring. [ABSTRACT FROM AUTHOR]

NDVI (Normalized difference vegetation index) is a critical variable for monitoring climate change, studying ecological balance, and exploring the pattern of regional phenology. Traditional neural network models only consider image features in time series prediction, while historical data and its changes play an important role in time series forecasting. For this study, we proposed convolutional neural networks (CNN) combined feature engineering forecasting model (SF-CNN), which integrated both the advantages of image characteristics learned from CNN and statistic characteristics calculated by historical data related to the forecast period to improve the accuracy of NDVI predictions in the next 3 months with 30-day interval at multiple complex areas. To intuitively show the performance of SF-CNN, it was compared with CNN using the same parameters. Results mainly showed that (1) in terms of visual analysis, the texture, pattern, and structure of predicted NDVI using SF-CNN are similar to the observed NDVI, and SF-CNN exhibits strong generalization ability; (2) in terms of quantitative assessment, SF-CNN generally outperforms CNN, and it can improve the reliability and robustness for predicting NDVI through simple statistical characteristics while reducing the uncertainties; (3) SF-CNN can learn seasonal and sudden changes in four different and complex study areas with considerable accuracy and without extra data. [ABSTRACT FROM AUTHOR]

The land surface temperature (LST) and vegetation growth status are two direct indicators of drought. In this study, we selected the LST index and vegetation index to construct drought eigenvectors, then proposed a new remote sensing drought index to assess the drought severity by calculating the similarity between the drought eigenvector of the target pixel and the drought eigenvector under an extremely wet state. Considering the different responses of various objects to drought, the drought eigenvectors of different land cover types were established. The results showed that the Temperature-Vegetation Water Stress Index (T-VWSI) were highly correlated with the measured relative soil moisture (RSM). The correlation coefficients (r) between the T-VWSI and 20-cm RSM reached 0.81, 0.77, and 0.78 in May, June, and July, respectively. Therefore, the T-VWSI is a promising drought index that will play an important role in drought monitoring. [ABSTRACT FROM AUTHOR]

Cognitive radio networks (CRNs) have been widely studied to improve spectrum utilization through a dynamic spectrum allocation scheme. However, many challenges remain and one big issue is how to provide an efficient spectrum management in a mobile environment. In this paper, we propose a new prediction-based spectrum management strategy to tackle this challenge. Both spectrum prediction and users’ mobility prediction are fully considered in the proposed strategy. Combining the prediction information and cooperative sensing, a CR base station can obtain the sensing information for future location of each secondary user, so as to prearrange high-quality channels for secondary users ahead of time. In addition, we propose a new channel selection scheme when multiple channels are available simultaneously. Besides traditional decision factors, the new channel selection algorithm takes the channel's future availability obtained from spectrum prediction into account. By properly integrating the spectrum prediction, user mobility prediction, and channel selection, the new spectrum management strategy is capable of allocating the spectrum resource more efficiently. Extensive simulations are conducted and results verify that the proposed spectrum management strategy significantly improves the system performance in terms of reducing handoff times and improving user satisfactory, connection reliability, and channel utilization. [ABSTRACT FROM AUTHOR]

Due to the inconsistent spatiotemporal spectral scales, a remote sensing dataset over a large-scale area and over long-term time series will have large variations and large statistical distribution features, which will lead to a performance drop of the deep learning model that is only trained on the source domain. For building an extraction task, deep learning methods perform weak generalization from the source domain to the other domain. To solve the problem, we propose a Capsule–Encoder–Decoder model. We use a vector named capsule to store the characteristics of the building and its parts. In our work, the encoder extracts capsules from remote sensing images. Capsules contain the information of the buildings' parts. Additionally, the decoder calculates the relationship between the target building and its parts. The decoder corrects the buildings' distribution and up-samples them to extract target buildings. Using remote sensing images in the lower Yellow River as the source dataset, building extraction experiments were trained on both our method and the mainstream methods. Compared with the mainstream methods on the source dataset, our method achieves convergence faster, and our method shows higher accuracy. Significantly, without fine tuning, our method can reduce the error rates of building extraction results on an almost unfamiliar dataset. The building parts' distribution in capsules has high-level semantic information, and capsules can describe the characteristics of buildings more comprehensively, which are more explanatory. The results prove that our method can not only effectively extract buildings but also perform great generalization from the source remote sensing dataset to another. [ABSTRACT FROM AUTHOR]