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Timely and accurate mapping of paddy rice cultivation based on remote sensing technology is crucial and valuable for ensuring food security and sustainable environmental management. In most relevant studies, rice mapping was conducted using time-series images, but conventional rice mapping methods are not specifically designed for time-series data, making it difficult to extract the deep information contained in these data. To address these problems, in this paper, an automatic rice mapping method based on an integrated time-series gradient boosting tree (Auto-ITSGBT) is proposed using GF-6 WFV and Sentinel-2 MSI data. This method accounts for the local and overall shape features of time-series curves, and fully exploits the information related to phenological characteristics between time-series data. The proposed rice mapping method is tested and validated in three typical rice-producing areas, which are located in different provinces of China characterized by diverse climate conditions, planting times or topographies. The results show that the overall accuracy and Kappa coefficient of the method exceeded 95% and 0.93, respectively, at all study sites, respectively. Our method performs better than the existing competing methods, with an overall accuracy improvement of 2% to 4%. To identify the rice planting areas as early as possible, rice mapping was conducted by reducing the number of images one by one. The rice distribution map was obtained in mid-July with an overall accuracy of at least 90%, thus obtaining a spatial distribution map of rice with high accuracy before harvesting.

Abstract Building structures are subjected to strong earthquakes, which result in lateral collisions between them. Such collisions often cause severe structural damage and exacerbate the seismic hazard risk of building structures during earthquake events. This paper discusses the application of vibration control devices based on negative stiffness inerter damper in single-story adjacent building structures. The dynamic equations of the vibration control system containing different types of negative stiffness inerter damper under seismic excitation are established as a unified model. The H2 norm theory and Monte Carlo pattern search method are used to optimize the design parameters to improve the vibration control performance of the system, and the dynamic characteristics of the system are investigated. The results demonstrate that attaching negative stiffness inerter damper to adjacent building structures can effectively improve the overall seismic capacity reserve of the building and reduce the risk of collision of adjacent building structures; improve the robustness and stability of the system, and better reduce the displacement response of the building structure under seismic excitation. In addition, the potential of NSID-based vibration control devices to convert seismic energy into usable electricity has been investigated.

Light field images, rich in spatial and angular domain information, have garnered increasing interest in fields like three-dimonsions (3D) imaging and virtual reality. Despite advancements in natural image encryption, encryption techniques for light field images remain nascent. This paper proposes a hierarchical compression encryption scheme based on light field multi-view array, using Zu Chongzhi algorithm (ZUC), chaotic systems, and pixel permutation and diffusion. Initially, the view array undergoes processing via our proposed prediction structure to yield residual and central view. Subsequently, the residual views are segmented into few layers and individually encoded using high efficiency video coding (HEVC) encoder to produce bitstreams, each of which is sequentially chain-encrypted using the ZUC algorithm. Finally, an enhanced three-dimensional chaos system is employed to generate a key, which is applied to the pixel-shift, bit-shift, and pixel substitution operation of the central color image. Experimental results demonstrate that our compression encryption scheme not only achieves significant compression effects but also surpasses the existing schemes in terms of anti-statistical attack ability, anti-differential attack ability, and keys sensitivity.

Light-field images (LFIs) are gaining increased attention within the field of 3D imaging, virtual reality, and digital refocusing, owing to their wealth of spatial and angular information. The escalating volume of LFI data poses challenges in terms of storage and transmission. To address this problem, this paper introduces an MSHPE (most-similar hierarchical prediction encoding) structure based on light-field multi-view images. By systematically exploring the similarities among sub-views, our structure obtains residual views through the subtraction of the encoded view from its corresponding reference view. Regarding the encoding process, this paper implements a new encoding scheme to process all residual views, achieving lossless compression. High-efficiency video coding (HEVC) is applied to encode select residual views, thereby achieving lossy compression. Furthermore, the introduced structure is conceptualized as a layered coding scheme, enabling progressive transmission and showing good random access performance. Experimental results demonstrate the superior compression performance attained by encoding residual views according to the proposed structure, outperforming alternative structures. Notably, when HEVC is employed for encoding residual views, significant bit savings are observed compared to the direct encoding of original views. The final restored view presents better detail quality, reinforcing the effectiveness of this approach.

Differentiated nitrogen (N) application styles and dosages can directly affect crop yield and planting costs. To maximize the efficiency of N fertilizer utilization and solve the problem of increasing production without increasing income, the Decision Support Systems for Agrotechnology Transfer (DSSAT) model was used in this study to carry out experiments simulating different N application styles and dosages for potatoes at 15 meteorological observation stations in the northeast region of China during 2013–2017. The present study aims to analyze the relationship among yield, cost, and net gains under different N fertilizer management scenarios by combining the cost-benefit method. The main conclusions are as follows: (1) the staged and incremental application of N fertilizer during the potato growth period can promote a yield increase. The lowest yield was achieved by a one-time basal application of N fertilizer at sowing, and the highest yield was observed when N fertilizer was applied in a 1:1 ratio at sowing and the end of tuber formation, respectively. (2) The range for N fertilizer application to achieve the highest potato yield at each station is 275–330 kg·ha−1. (3) The highest potato yield under sufficient N application treatments did not correspond to the highest net gains. Potato net gains at all stations showed an increasing-then-decreasing trend with the increase in N application. The highest gains were obtained when 220 kg·ha−1, 220 kg·ha−1, and 275 kg·ha−1 of N fertilizer were applied in a 1:1 ratio as a basal fertilizer and supplementary fertilizer at sowing, and the end of tuber formation in Heilongjiang, Jilin, and Liaoning provinces, respectively. This study provides a reference program for potato planting with the highest benefits for regional governments, farmers, and enterprises and aims to improve the status quo of potato planting, increasing production without increasing income.

Objective:China has established a goal of reducing adult smoking prevalence from 27.7% to 20% by 2030. Understanding the possible ongoing impairment in lung function in smokers, is critically important to encourage the populations to change their smoking behavior.MethodsA total of 14,273 males joined the health examination at Huadong Sanatorium from Jan 2012 to Dec 2019 were included. In cross-sectional analysis, we used multiple linear regression to evaluate the association between baseline lung function and smoking status. Then, 3,558 males who received ≥2 spirometry exams were analyzed in longitudinal study. Annual lung function decline was compared using mixed linear models adjusted for confounders.ResultsIn cross-sectional analysis, compared with never-smokers, decreases of −133.56 mL (95% CI: −167.27, −99.85) and −51.44 mL (−69.62, −33.26) in FEV1, −1.48% (−1.94, −1.02) and −1.29% (−1.53, −1.04) in FEV1/FVC were observed in former and current smokers. In longitudinal analysis, significant declines were observed in FEV1 [5.04 (2.30, 7.78) mL] and FEV1/FVC [0.09 (0.05, 0.13) %] in current smokers but not observed in former smokers after adjustment. Participants with long duration of smoking cessation had decelerate lung function than short duration. The annual decline rate of current smokers with high smoking intensity (≥30 cigarettes per day) was 13.80 and 14.17 times greater than that of never-smokers in FEV1 and FVC. Thus, early smoking cessation can slow down lung function decline trend for current smokers.ConclusionsThe harms of current smoking on lung function emphasize the necessity of smoking cessation, especially for those with comorbidities.

Abstract Background The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. Methods In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared. Results The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil. Conclusion The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.

Rice is one of the staple food crops worldwide, and timely and accurate paddy rice mapping (PRM) is essential to coordinate agricultural production and assure grain security. In cloudy and foggy regions, there are low exploitation rates of optical images, and accurate PRM is a commonly occurring difficulty. To achieve a precise and timely PRM in these regions, an automatic PRM method based on constrained feature matching (Auto-CFM) for arbitrary length time series was proposed using Sentinel-1 Synthetic Aperture Radar (SAR) data, which takes into account the local shape differences of the time-series σ0 VH curves of different land cover types and the overall deviation of the curves due to the discrepancy in rice planting time. Moreover, it solved the problem of high precision extraction of rice when the Sentinel-1 data might suffer from partial missing images. In this study, the PRM was conducted in Hunan Province and validated in Hubei, Guangdong, and Heilongjiang Provinces, which featured different planting times, climates, and topographies. The results showed that the Auto-CFM improved the accuracy by 3%–9% compared to current competing methods and the PRM accuracy exceeded 92% in different validation areas, proving the effectiveness and robustness of the method. To obtain cultivation areas as early as possible, the PRM was performed by reducing the number of images one by one to eventually acquired the rice maps in mid to late August with an overall accuracy of no less than 90%, achieving the goal of access to the spatial distribution of rice with high accuracy before harvest.

Under the action of an earthquake, the adjacent building structure will have a large displacement and even cause damage to the structure. Structure displacement can be suppressed by adding a tuned viscous mass damper (TVMD) and negative stiffness amplifier damper (NSAD) between adjacent structures. This paper deduces the motion equation of adjacent building structures under earthquake action. The transfer function of the vibration control system is obtained using Laplace transform. The parameter optimization analysis of the structural vibration control system is carried out through H2 norm theory. The vibration control performance of the TVMD vibration control system and NSAD vibration control system is studied by taking two adjacent building structures composed of single freedom building structures as an example. The results show that the two vibration damping devices proposed in this paper can play the role of vibration control. The influence of the same parameters on the two structures is not completely consistent. Under seismic excitation, the NSAD control system has better displacement control ability than the TVMD control system.

Abstract In this paper, an error recovery (ER) detection based on the lattice reduction (LR) algorithm with adaptive iteration is presented for uplink massive multiple‐input multiple‐output (MIMO) systems to obtain an improved bit error rate (BER) performance, especially when the number of active users increases. In the proposed algorithm, as the result of LR pre‐processing, a larger diagonally dominant index (DDI) is used to measure the diagonal dominance of gram matrix for the initialisation of the ER detection. In addition, the larger DDI could be further used to realise adaptive iteration, which helps to strike a trade‐off between the BER performance and the computational complexity. Moreover, two properties of the DDI are derived, and the simulation results show that the larger DDI leads to better detection performance, and it could be widely applied to linear elimination and MIMO detection algorithms.

As a promising method, unmanned aerial vehicle (UAV) multispectral remote sensing (RS) has been extensively studied in precision agriculture. However, there are numerous problems to be solved in the data acquisition and processing, which limit its application. In this study, the Micro-MCA12 camera was used to obtain images at different altitudes. The piecewise empirical line (PEL) method suitable for predicting the reflectance of different ground objects was proposed to accurately acquire the reflectance of multi-altitude images by comparing the performance of the conventional methods. Several commonly utilized vegetation indices (VIs) were computed to estimate the rice growth parameters and yield. Then the rice growth monitoring and yield prediction were implemented to verify and evaluate the effects of radiometric calibration methods (RCMs) and UAV flying altitudes (UAV-FAs). The results show that the variation trends of reflectance and VIs are significantly different due to the change in component proportion observed at different altitudes. Except for the milking stage, the reflectance and VIs in other periods fluctuated greatly in the first 100 m and remained stable thereafter. This phenomenon was determined by the field of view of the sensor and the characteristic of the ground object. The selection of an appropriate calibration method was essential as a result of the marked differences in the rice phenotypes estimation accuracy based on different RCMs. There were pronounced differences in the accuracy of rice growth monitoring and yield estimation based on the 50 and 100 m-based variables, and the altitudes above 100 m had no notable effect on the results. This study can provide a reference for the application of UAV RS technology in precision agriculture and the accurate acquisition of crop phenotypes.

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening.

The occurrence of pests at high frequencies has been identified as a major cause of reduced citrus yields, and early detection and prevention are of great significance to pest control. At present, studies related to citrus pest identification using deep learning suffer from unbalanced sample sizes between data set classes, which may cause slow convergence of network models and low identification accuracy. To address the above problems, this study built a dataset including 5182 pest images in 14 categories. Firstly, we expanded the dataset to 21,000 images by using the Attentive Recurrent Generative Adversarial Network (AR-GAN) data augmentation technique, then we built Visual Geometry Group Network (VGG), Residual Neural Network (ResNet) and MobileNet citrus pest recognition models by using transfer learning, and finally, we introduced an appropriate attention mechanism according to the model characteristics to enhance the ability of the three models to operate effectively in complex, real environments with greater emphasis placed on incorporating the deep features of the pests themselves. The results showed that the average recognition accuracy of the three models reached 93.65%, the average precision reached 93.82%, the average recall reached 93.65%, and the average F1-score reached 93.62%. The integrated application of data augmentation, transfer learning and attention mechanisms in the research can significantly enhance the model’s ability to classify citrus pests while saving training cost and time, which can be a reference for researchers in the industry or other fields.

Abstract Background LncRNA WT1-AS is a recently identified potential tumor suppressor in gastric cancer. This study mainly explored the role of WT1-AS in non-small cell lung cancer (NSCLC). Methods WT1-AS and TGF-β1 mRNA in two types of tissues of 74 NSCLC patients were detected by performing RT-qPCR experiments. WT1-AS and TGF-β1 expression vectors were established using the pcDNA3.1 vector. Protein concentration was measured by BCA assay. Mean values in this study were calculated using the data of three biological replicates of each experiment. Results We found that WT1-AS was down-regulated, while TGF-β1 was upregulated in NSCLC tissues. Survival analysis showed that low levels of WT1-AS expression predicted poor survival of NSCLC patients. WT1-AS and TGF-β1 were inversely correlated in NSCLC tissues. Over-expression experiments revealed down-regulated TGF-β1 after WT1-AS over-expression, while TGF-β1 over-expression failed to affect WT1-AS. WT1-AS over-expression resulted in inhibited cancer cell stemness. TGF-β1 over-expression played an opposite role and attenuated the effects of TGF-β1 over-expression. Conclusion Therefore, WT1-AS over-expression may inhibit non-small cell lung cancer cell stemness by down-regulating TGF-β1. Trial registration The First Affiliated Hospital of Anhui Medical University Ethics committee approved this study (AHMU20101009).

Fast, accurate, and non-destructive large-scale detection of sweet cherry ripeness is the key to determining the optimal harvesting period and accurate grading by ripeness. Due to the complexity and variability of the orchard environment and the multi-scale, obscured, and even overlapping fruit, there are still problems of low detection accuracy even using the mainstream algorithm YOLOX in the absence of a large amount of tagging data. In this paper, we proposed an improved YOLOX target detection algorithm to quickly and accurately detect sweet cherry ripeness categories in complex environments. Firstly, we took a total of 2400 high-resolution images of immature, semi-ripe, and ripe sweet cherries in an orchard in Hanyuan County, Sichuan Province, including complex environments such as sunny days, cloudy days, branch and leaf shading, fruit overlapping, distant views, and similar colors of green fruits and leaves, and formed a dataset dedicated to sweet cherry ripeness detection by manually labeling 36068 samples, named SweetCherry. On this basis, an improved YOLOX target detection algorithm YOLOX-EIoU-CBAM was proposed, which embedded the Convolutional Block Attention Module (CBAM) between the backbone and neck of the YOLOX model to improve the model’s attention to different channels, spaces capability, and replaced the original bounding box loss function of the YOLOX model with Efficient IoU (EIoU) loss to make the regression of the prediction box more accurate. Finally, we validated the feasibility and reliability of the YOLOX-EIoU-CBAM network on the SweetCherry dataset. The experimental results showed that the method in this paper significantly outperforms the traditional Faster R-CNN and SSD300 algorithms in terms of mean Average Precision (mAP), recall, model size, and single-image inference time. Compared with the YOLOX model, the mAP of this method is improved by 4.12%, recall is improved by 4.6%, F-score is improved by 2.34%, while model size and single-image inference time remain basically comparable. The method in this paper can cope well with complex backgrounds such as fruit overlap, branch and leaf occlusion, and can provide a data base and technical reference for other similar target detection problems.

Thrombocytopenia is closely linked with hemorrhagic diseases, for which induction of thrombopoiesis shows promise as an effective treatment. Polyphenols widely exist in plants and manifest antioxidation and antitumour activities. In this study, we investigated the thrombopoietic effect and mechanism of 3,3′,4′-trimethylellagic acid (TMEA, a polyphenol in Sanguisorba officinalis L.) using in silico prediction and experimental validation. A KEGG analysis indicated that PI3K/Akt signalling functioned as a crucial pathway. Furthermore, the virtual molecular docking results showed high-affinity binding (a docking score of 6.65) between TMEA and mTOR, suggesting that TMEA might target the mTOR protein to modulate signalling activity. After isolation of TMEA, in vitro and in vivo validation revealed that this compound could promote megakaryocyte differentiation/maturation and platelet formation. In addition, it enhanced the phosphorylation of PI3K, Akt, mTOR, and P70S6K and increased the expression of GATA-1 and NF-E2, which confirmed the mechanism prediction. In conclusion, our findings are the first to demonstrate that TMEA may provide a novel therapeutic strategy that relies on the PI3K/Akt/mTOR pathway to facilitate megakaryocyte differentiation and platelet production.

Abstract Background Involuntary subordination is a mechanism that switches off fighting behaviors when a losing organism is unable to continue in a struggle. The study aim was to investigate the association between involuntary subordination and the common mental disorders of anxiety and depression among men who have sex with men (MSM) in Shanghai, China. Methods A cross-sectional study was conducted of 547 MSM in four Shanghai districts. Sociodemographic and psychosocial participant data were collected. Logistic regression was used to assess the association between anxiety, depression, and involuntary subordination. Results 12.2 and 30.9% Of the MSM demonstrated high levels of anxiety and depression respectively. Univariate analysis showed that involuntary subordination and the involuntary subordination constructs of defeat, social comparison, submissive behavior, and entrapment were associated with anxiety and depression. Multivariate analysis indicated that defeat (ORm = 1.091, 95% CI = 1.004–1.185) and entrapment (ORm = 1.174, 95% CI = 1.079–1.278) were significantly associated with anxiety. Defeat (ORm = 1.265, 95% CI = 1.166–1.372), social comparison (ORm = 1.119, 95% CI = 1.061–1.181), entrapment (ORm = 1.132, 95% CI = 1.047–1.224), and submissive behavior (ORm = 0.897, 95% CI = 0.825–0.975) were significantly associated with depression. Conclusions The findings confirmed an association between anxiety, depression, and involuntary subordination among MSM. These findings could form the basis of a new, integrated, and holistic approach to the identification of high-risk groups and the development of interventions for anxiety and depression among MSM.

Abstract Background In China, high prevalence of risky sexual behaviours and inequity in health services lead to situations in which migrant men who have sex with men face higher risk of contracting the human immunodeficiency virus. Consistent condom use is a primary means of preventing HIV infection during anal sex among MSM. This study aimed to apply the information–motivation–behavioural skills model to examine the predictors of consistent condom use among migrant MSM in Shanghai, and tested the associations between model constructs. Methods A cross-sectional study was conducted among 412 migrant MSM in Shanghai. Data on HIV-related information, motivation, behavioural skills, and behaviours were collected via structured questionnaires. A structural equation model was used to assess the IMB model. Results Of the 412 participants, 4.4% reported HIV-positive status, and prevalence of consistent condom use in the previous 6 months was 44.9%. A restricted IMB model provided an acceptable fit to the data. Behavioural skills were found to directly predict consistent condom use (β = 0.629, P 0.05), but motivation predicted it indirectly and was mediated by behavioural skills. Conclusion The prevalence of consistent condom use was found to be relatively low among migrant MSM in Shanghai. The restricted IMB model was found to be a good predictor of consistent condom use among them. The results of this study indicate that intervention strategies for safer sexual behaviour should not only include information dissemination, but also emphasize motivation and behavioural skills among this population.

Abstract Background In China, unmarried female migrants are vulnerable to sexual and reproductive health risks. One effective protection strategy is promoting consistent condom use (CCU). Methods We conducted a cross-sectional study to apply the information-motivation-behavioral skills (IMB) model and modified it by addition of psychological and personal factors to examine the related factors of CCU and provide suggestions for intervention among unmarried female migrants. Results Of all 903 eligible participants, only 13.8% of participants reported CCU in the past six months. Both the IMB model and the modified IMB model provided acceptable fit to the data. In both models, information had no direct or indirect influence on CCU (p > 0.05). However, behavioral skills had a positive effect on CCU (β = 0.344, p 0.05). In the modified model, psychological and personality factors influenced CCU directly (β = − 0.100, p = 0.005). Conclusions Our results highlight the importance of conducting CCU promotion among unmarried female migrants. Future intervention strategies should focus on both the traditional IMB model constructs and the added psychological and personality factors.

A precise forecast of rice yields at the plot scale is essential for both food security and precision agriculture. In this work, we developed a novel technique to integrate UAV-based vegetation indices (VIs) with brightness, greenness, and moisture information obtained via tasseled cap transformation (TCT) to improve the precision of rice-yield estimates and eliminate saturation. Eight nitrogen gradients of rice were cultivated to acquire measurements on the ground, as well as six-band UAV images during the booting and heading periods. Several plot-level VIs were then computed based on the canopy reflectance derived from the UAV images. Meanwhile, the TCT-based retrieval of the plot brightness (B), greenness (G), and a third component (T) indicating the state of the rice growing and environmental information, was performed. The findings indicate that ground measurements are solely applicable to estimating rice yields at the booting stage. Furthermore, the VIs in conjunction with the TCT parameters exhibited a greater ability to predict the rice yields than the VIs alone. The final simulation models showed the highest accuracy at the booting stage, but with varying degrees of saturation. The yield-prediction models at the heading stage satisfied the requirement of high precision, without any obvious saturation phenomenon. The product of the VIs and the difference between the T and G (T − G) and the quotient of the T and B (T/B) was the optimum parameter for predicting the rice yield at the heading stage, with an estimation error below 7%. This study offers a guide and reference for rice-yield estimation and precision agriculture.

Rice pests are one of the main factors affecting rice yield. The accurate identification of pests facilitates timely preventive measures to avoid economic losses. Some existing open source datasets related to rice pest identification mostly include only a small number of samples, or suffer from inter-class and intra-class variance and data imbalance challenges, which limit the application of deep learning techniques in the field of rice pest identification. In this paper, based on the IP102 dataset, we first reorganized a large-scale dataset for rice pest identification by Web crawler technique and manual screening. This dataset was given the name IP_RicePests. Specifically, the dataset includes 8248 images belonging to 14 categories. The IP_RicePests dataset was then expanded to include 14,000 images via ARGAN data augmentation technique to address the difficulties in obtaining large samples of rice pests. Finally, the parameters trained on the public image ImageNet dataset using VGGNet, ResNet and MobileNet networks were used as the initial values of the target data training network to achieve image classification in the field of rice pests. The experimental results show that all three classification networks combined with transfer learning have good recognition accuracy, among which the highest classification accuracy can be obtained on the IP_RicePests dataset via fine-tuning the parameters of the VGG16 network. In addition, following ARGAN data augmentation the dataset demonstrates high accuracy improvements in all three models, and fine-tuning the VGG16 network parameters obtains the highest accuracy in the augmented IP_RicePests dataset. It is demonstrated that CNN combined with transfer learning can employ the ARGAN data augmentation technique to overcome difficulties in obtaining large sample sizes and improve the efficiency of rice pest identification. This study provides foundational data and technical support for rice pest identification.

Soil is one of the most significant natural resources in the world, and its health is closely related to food security, ecological security, and water security. It is the basic task of soil environmental quality assessment to monitor the temporal and spatial variation of soil properties scientifically and reasonably. Soil moisture content (SMC) is an important soil property, which plays an important role in agricultural practice, hydrological process, and ecological balance. In this paper, a hyperspectral SMC estimation method for mixed soil types was proposed combining some spectral processing technologies and principal component analysis (PCA). The original spectra were processed by wavelet packet transform (WPT), first-order differential (FOD), and harmonic decomposition (HD) successively, and then PCA dimensionality reduction was used to obtain two groups of characteristic variables: WPT-FOD-PCA (WFP) and WPT-FOD-HD-PCA (WFHP). On this basis, three regression models of principal component regression (PCR), partial least squares regression (PLSR), and back propagation (BP) neural network were applied to compare the SMC predictive ability of different parameters. Meanwhile, we also compared the results with the estimates of conventional spectral indices. The results indicate that the estimation results based on spectral indices have significant errors. Moreover, the BP models (WFP-BP and WFHP-BP) show more accurate results when the same variables are selected. For the same regression model, the choice of variables is more important. The three models based on WFHP (WFHP-PCR, WFHP-PLSR, and WFHP-BP) all show high accuracy and maintain good consistency in the prediction of high and low SMC values. The optimal model was determined to be WFHP-BP with an R2 of 0.932 and a prediction error below 2%. This study can provide information on farm entropy before planting crops on arable land as well as a technical reference for estimating SMC from hyperspectral images (satellite and UAV, etc.).

Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recognized as the most effective among the methods combining convolutional neural networks and 3D fluorescence analysis. Based on this, we took some samples from the actual environment for measuring 3D fluorescence data and obtained a batch of public datasets from the internet species. Firstly, we preprocessed the data (including two steps of PARAFAC analysis and CNN dataset generation), and then we proposed a 3D fluorescence classification method and a components fitting method based on VGG16 and VGG11 convolutional neural networks. The VGG16 network is used for the classification of 3D fluorescence data with a training accuracy of 99.6% (as same as the PCA + SVM method (99.6%)). Among the component maps fitting networks, we comprehensively compared the improved LeNet network, the improved AlexNet network, and the improved VGG11 network, and finally selected the improved VGG11 network as the component maps fitting network. In the improved VGG11 network training, we used the MSE loss function and cosine similarity to judge the merit of the model, and the MSE loss of the network training reached 4.6 × 10−4 (characterizing the variability of the training results and the actual results), and we used the cosine similarity as the accuracy criterion, and the cosine similarity of the training results reached 0.99 (comparison of the training results and the actual results). The network performance is excellent. The experiments demonstrate that the convolutional neural network has a great application in 3D fluorescence analysis.

The pathogenesis of Huntington’s disease (HD), an inherited progressive neurodegenerative disease, is highly associated with the cytotoxicity-inducing mutant huntingtin (mHtt) protein. Emerging evidence indicates that autophagy plays a pivotal role in degrading aggregated proteins such as mHtt to enhance neuronal viability. In this study, by employing preparative high-performance liquid chromatography (pre-HPLC), ultra-high performance liquid chromatography diode-array-detector quadrupole time-of-flight mass spectrometry (UHPLC-DAD-Q-TOF-MS) and nuclear magnetic resonance (NMR), three escins, escin IA (EA), escin IB (EB) and isoescin IA (IEA), were isolated and identified from the seed of Aesculus chinensis Bge. (ACB). After EGFP-HTT74-overexpressing HT22 cells were treated with EA, EB and IEA at safe concentrations, the clearance of mHtt and mHtt-induced apoptosis were investigated by Western blot, immunofluorescence microscopy and flow cytometry methods. In addition, the autophagy induced by these escins in HT22 cells was monitored by detecting GFP-LC3 puncta, P62 and LC3 protein expression. The results showed that EA, EB and IEA could significantly decrease mHtt levels and inhibit its induced apoptosis in HT22 cells. In addition, these three saponins induced autophagic flux by increasing the ratio of RFP-LC3 to GFP-LC3, and by decreasing P62 expression. Among the tested escins, EB displayed the best autophagy induction, which was regulated via both the mTOR and ERK signaling pathways. Furthermore, the degradation of mHtt and the commensurate decrease in its cytotoxic effects by EA, EB and IEA were demonstrated to be closely associated with autophagy induction, which depended on ATG7. In conclusion, we are the first to report that the escins, including EA, EB and IEA are novel autophagy inducers that degrade mHtt and inhibit mHtt-induced apoptosis in vitro and in vivo. As a result of these findings, the triterpenoid saponins in ACB might be considered to be promising candidates for the treatment of HD in the future.

Abstract Background HIV prevalence among men who have sex with men (MSM) in China is rising rapidly, and unprotected anal intercourse (UAI) is associated with HIV transmission. Recent research has shown that associations between UAI and other factors can differ according to the type of sex partners, including regular partners and casual partners. This study aimed to explore the relationship between sexual compulsivity and UAI according to partner type among MSM in Shanghai, China. Methods A cross-sectional study was conducted among 547 MSM from four districts in Shanghai, China. All participants were recruited using snowball sampling. The Sexual Compulsivity Scale was used to evaluate participants’ sexual compulsivity. Multivariable logistic regression was used to identify factors associated with sexual compulsivity and UAI. The mediation effects of substance use before sex on the relationship between sexual compulsivity and UAI were tested through mediation analyses. Results After adjusting for sociodemographic variables, sexual compulsivity was associated with overall UAI (adjusted odds ratios [AOR] = 1.039, 95% confidence intervals [CI] = 1.004–1.075), UAI with non-regular sex partners (AOR = 1.089, 95% CI = 1.033–1.148) and UAI with commercial sex partners (AOR = 1.185, 95% CI = 1.042–1.349). No significant association was found between sexual compulsivity and UAI with regular sex partners (AOR = 1.029, 95% CI = 0.984–1.077). Mediation analyses indicated that the relationship between sexual compulsivity and UAI was not mediated by either alcohol use before sex or drug use before sex. Conclusions The association between sexual compulsivity and UAI varies depending on the type of UAI partner. Therefore, individuals may engage in different types of UAI for different reasons, and tailored HIV cognitive–behavioral intervention programs are needed.

Abstract Background Despite reports of mental health issues, suicidality has not been closely examined among the migrant population. The association between induced abortion and suicidal ideation is unknown among unmarried female migrant workers of reproductive age in China. This study aims to examine induced abortion and suicidality among the Chinese migrant population. Methods We recruited 5115 unmarried female migrant workers during 2015 to 2016 from Shanghai, Beijing and Guangzhou, and collected demographic, psychosocial, reproductive and mental health information using structured questionnaires. We used logistic regression models to examine the association between lifetime induced abortion and suicidal ideation during the past year among the subjects. Results Overall, 8.2% of the subjects had suicidal ideation during the past year, and 15.5% of the subjects experienced induced abortion. Induced abortion was associated with nearly twice the odds of having past-year suicidal ideation (Odds ratio, OR = 1.89; 95% confidence interval, CI: 1.46, 2.44) after adjusting for age, education, years in the working place, tobacco use, alcohol consumption, daily internet use, attitude towards premarital pregnancy, multiple induced abortion, self-esteem, loneliness, depression, and anxiety disorders. The association was stronger in those aged > 25 (OR = 3.37, 95% CI = 2.16, 5.28), with > 5 years of stay in the working place (OR = 2.98, 95% CI = 2.02, 4.39), the non-anxiety group (OR = 2.28, 95% CI = 1.74, 3.00), and the non-depression group (OR = 2.94, 95% CI = 2.08, 4.15). Conclusions Induced abortion was associated with increased odds for suicidal ideation among the unmarried female migrant workers in urban cities in China. More attention should be paid to the mental health of the population.

Index modulated OFDM spread spectrum (OFDM-IM-SS) is a recently proposed scheme, which transmits the additional bits (index bits) through the code domain. In this paper, we propose a novel variant of OFDM-IM-SS, called index modulated OFDM with in-phase/quadrature spread spectrum (OFDM-IM-SS-IQ), to increase the spectrum efficiency of OFDM-IM-SS. In OFDM-IM-SS-IQ, the transmitted symbols are divided into in-phase and quadrature parts, which are combined by the corresponding spreading codes selected by their respective index bits. A low complexity detection algorithm for OFDM-IMSS-IQ is investigated to relieve the computational complexity of the optimal maximum-likelihood detection. A closed-form of the upper bound on the bit error rate is then derived to evaluate the error performance of OFDM-IM-SS-IQ. In addition, a diversity-enhancing scheme of OFDM-IM-SS, named index modulated OFDM spread spectrum enhancement (OFDM-IM-SS-E), is also designed to achieve a full diversity order. Computer simulation results validate the theoretical analysis and show that OFDM-IM-SS-IQ outperforms the conventional OFDM-IM-SS scheme. They also prove that the proposed OFDM-IM-SS-E obtains full diversity and achieves a better bit error rate performance than OFDM-IM-SS.

The separation of the forwarding and control planes of software-defined networking brings a lot of flexibility to network management. However, with the increase in network capacity, network structure becomes more and more complicated. The controller placement problem in a large-scale network is still a hard nut to crack because of high complexity and difficulty in the tradeoff between performance. In this paper, a novel approach named community detection controller deployment is proposed. With the aid of theory of complex network analysis, the network topology of the controller to be deployed is regarded as a network composed of multiple communities, and then a suitable position is selected in each community to place the controller, which is capable of avoiding the complexity of global deployment. In order to balance the number of switches managed by the controller in each community, the scale constraint factor is introduced into Louvain heuristic algorithm to limit the number of nodes in each community and balance the differences in the number of nodes among different communities. Distinct from the existing clustering-based approaches, this method can independently identify partitions with community attributes according to the network structure without manual intervention. On the other hand, it can adjust the number of nodes within communities on demand to achieve topological equilibrium partition. Experiments are performed on real network topologies, and corresponding results show that the proposed method is more suitable for networks with plenty of nodes and can effectively balance the controllers’ load while keeping latency at a lower level.

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) improves the spectral efficiency of the conventional OFDM-IM scheme by considering multiple distinguishable constellations for signal modulation. In this paper, we propose a novel scheme, called space-time MM-OFDM-IM (ST-MM-OFDM-IM), to increase the transmit diversity of MM-OFDM-IM. In ST-MM-OFDM-IM, the signal matrix, which consists of multiple signal vectors of MM-OFDM-IM, is transmitted over multiple time slots by following a specific rule. A low-complexity detection is proposed to mitigate the high burden of the optimal maximum-likelihood detection at the receiver side. A closed-form upper bound on the bit error rate is derived to evaluate the performance of ST-MM-OFDM-IM. Moreover, a diversity improving scheme of ST-MM-OFDM-IM is also studied to obtain full transmit diversity. Simulation results verify the theoretical analysis and show that ST-MM-OFDM-IM outperforms the conventional MM-OFDM-IM scheme.

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.

In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE) maximization problem in a 5G massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) downlink system with imperfect channel state information (CSI) at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA) algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM). A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP) that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach’s algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

Saliva contains important personal physiological information that is related to some diseases, and it is a valuable source of biochemical information that can be collected rapidly, frequently, and without stress. In this article, we reported a new and simple localized surface plasmon resonance (LSPR) substrate composed of polyaniline (PANI)-gold hybrid nanostructures as an optical sensor for monitoring the pH of saliva samples. The overall appearance and topography of the substrates, the composition, and the wettability of the LSPR surfaces were characterized by optical and scanning electron microscope (SEM) images, infrared spectra, and contact angles measurement, respectively. The PANI-gold hybrid substrate readily responded to the pH. The response time was very short, which was 3.5 s when the pH switched from 2 to 7, and 4.5 s from 7 to 2. The changes of visible-near-infrared (NIR) spectra of this sensor upon varying pH in solution showed that—for the absorption at given wavelengths of 665 nm and 785 nm—the sensitivities were 0.0299 a.u./pH (a.u. = arbitrary unit) with a linear range of pH = 5–8 and 0.0234 a.u./pH with linear range of pH = 2–8, respectively. By using this new sensor, the pH of a real saliva sample was monitored and was consistent with the parallel measurements with a standard laboratory method. The results suggest that this novel LSPR sensor shows great potential in the field of mobile healthcare and home medical devices, and could also be modified by different sensitive materials to detect various molecules or ions in the future.

We introduce a double-layer code based on the combination of a low-density parity-check (LDPC) code with the multiple-input multiple-output (MIMO) system, where the decoding can be done in both inner-iteration and outer-iteration manners. The present code, called low-density MIMO code (LDMC), has a double-layer structure, that is, one layer defines subcodes that are embedded in each transmission vector and another glues these subcodes together. It supports inner iterations inside the LDPC decoder and outeriterations between detectors and decoders, simultaneously. It can also achieve the desired design rates due to the full rank of the deployed parity-check matrix. Simulations show that the LDMC performs favorably over the MIMO systems.