A Precise Framework for Rice Leaf Disease Image-Text Retrieval Using FHTW-Net.

Cross-modal retrieval for rice leaf diseases is crucial for prevention, providing agricultural experts with data-driven decision support to address disease threats and safeguard rice production. To overcome the limitations of current crop leaf disease retrieval frameworks, we focused on four common rice leaf diseases and established the first cross-modal rice leaf disease retrieval dataset (CRLDRD). We introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image-text retrieval. To address the challenge of matching diverse image categories with complex text descriptions during the retrieval process, we initially employed ViT and BERT to extract fine-grained image and text feature sequences enriched with contextual information. Subsequently, two-way mixed self-attention (TMS) was introduced to enhance both image and text feature sequences, with the aim of uncovering important semantic information in both modalities. Then, we developed false-negative elimination-hard negative mining (FNE-HNM) strategy to facilitate in-depth exploration of semantic connections between different modalities. This strategy aids in selecting challenging negative samples for elimination to constrain the model within the triplet loss function. Finally, we introduced warm-up bat algorithm (WBA) for learning rate optimization, which improves the model's convergence speed and accuracy. Experimental results demonstrated that FHTW-Net outperforms state-of-the-art models. In image-to-text retrieval, it achieved R@1, R@5, and R@10 accuracies of 83.5%, 92%, and 94%, respectively, while in text-to-image retrieval, it achieved accuracies of 82.5%, 98%, and 98.5%, respectively. FHTW-Net offers advanced technical support and algorithmic guidance for cross-modal retrieval of rice leaf diseases.

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet.

Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.

A Precise Image-Based Tomato Leaf Disease Detection Approach Using PLPNet.

Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease. Tomato plants are commonly planted in soil. When a disease occurs near the leaf's edge, the soil backdrop in the image tends to interfere with the infected region. These problems can make tomato detection challenging. In this paper, we propose a precise image-based tomato leaf disease detection approach using PLPNet. First, a perceptual adaptive convolution module is proposed. It can effectively extract the disease's defining characteristics. Second, a location reinforcement attention mechanism is proposed at the neck of the network. It suppresses the interference of the soil backdrop and prevents extraneous information from accessing the network's feature fusion phase. Then, a proximity feature aggregation network with switchable atrous convolution and deconvolution is proposed by combining the mechanisms of secondary observation and feature consistency. The network solves the problem of disease interclass similarities. Finally, the experimental results show that PLPNet achieved 94.5% mean average precision with 50% thresholds (mAP50), 54.4% average recall (AR), and 25.45 frames per second (FPS) on a self-built dataset. The model is more accurate and specific for the detection of tomato leaf diseases than other popular detectors. Our proposed method may effectively improve conventional tomato leaf disease detection and provide modern tomato cultivation management with reference experience.

Decoupling trend and emission reduction potential of CO2 emissions from China's petrochemical industry.

This paper aims to study the decoupling status and emission reduction potential of China's petrochemical industry from 1996 to 2019. Firstly, the IPCC method is used to calculate the CO2 emissions of the petrochemical industry in China, then the logarithmic mean Divisia index (LMDI) method is used to identify the influencing factors of CO2 emissions, then the decoupling index is constructed to analyze the dependence of economic development on CO2 emissions, and finally the emission reduction potential model is established by using the influencing factors to reflect the CO2 emission reduction potential of the petrochemical industry. The results reveal that (1) the CO2 emissions can be divided into two stages of slow decline (1996-2000), (2015-2019), and one stage of rapid growth (2000-2015). (2) The energy intensity effect is the most effective factor to restrain CO2 emission, the economic growth effect is the key factor to promote CO2 emission. (3) From 1996 to 2019, there was a weak decoupling relationship between CO2 emission of petrochemical industry and economic development. Strong decoupling only occurred in 1996-2000 and 2015-2019. The CO2 emissions show only three decoupling score: I, II, and III. (4) CO2 mitigation occurred in four sub periods: 1996-2000, 2005-2010, 2010-2015, and 2015-2019. Therefore, the government should establish an energy-saving and environment-friendly industrial production system, intensify the use of clean energy, and optimize the labor force structure. It not only effectively strengthens the decoupling between the petrochemical industry and economic development, but also provides an empirical example for the carbon emission reduction and economic sustainable development of the petrochemical industry in other countries in the world.

Factor decomposition for ecological pressure of the whole industrial energy carbon footprint: a case study of China.

The purpose of this paper is to study the influencing factors of the ecological pressure of the energy carbon footprint (EPECF) of China's whole industry from 2000 to 2018. First, the EPECF of 48 sub industries is calculated, then divides 48 sub-industries into high-, medium-, and low-pressure industries, and uses the logarithmic mean Divisia index (LMDI) method to analyze and summarize the main driving forces of China's industrial EPECF changes. Finally, policy suggestions for the future industrial decompression are put forward. The main results are as follows: (1) Economic development is the most important factor to promote the growth of EPECF of the three major industries. (2) At present, the population pressure factors of forest and grassland have little effect, and the effect of returning farmland to forest and grassland has not been truly played. (3) The adjustment of industrial structure has gradually become a key factor in reducing EPECF of the three industries. (4) The gradual stability of energy intensity has a certain inhibitory effect on the increase of EPECF in high-pressure industry. (5) The adjustment of energy structure in low-pressure industry has gradually worked. Therefore, the government should establish an economic sustainable development system, vigorously develop clean energy, and realize the green transformation of various industries. This provides an empirical example for other countries in the world to reduce the EPECF.

MPC-STANet: Alzheimer's Disease Recognition Method Based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism.

Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer's disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50.

CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases.

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases.

Corrigendum: Risk Perception and PTSD Symptoms of Medical Staff Combating Against COVID-19: A PLS Structural Equation Model.

[This corrects the article DOI: 10.3389/fpsyt.2021.607612.].

Maize leaf disease identification based on WG-MARNet.

In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of noise, background interference, and low detection accuracy of maize leaf disease images. To begin, a processing layer called Wavelet threshold guided bilateral filtering (WT-GBF) based on the WG-MARNet model is employed to reduce image noise and perform high and low-frequency decomposition of the input image using WT-GBF. This increases the input image's resistance to environmental interference and feature extraction capability. Secondly, for the multiscale feature fusion technique, an average down-sampling and tiling method is employed to increase feature representation and limit the risk of overfitting. Then, on high and low-frequency multi-channel, an attenuation factor is introduced to optimize the performance instability during training of the deep network. Finally, when the convergence and accuracy are compared, PRelu and Adabound are used instead of the Relu activation function and the Adam optimizer. The experimental results revealed that our method's average recognition accuracy was 97.96%, and the detection time for a single image was 0.278 seconds. The average detection accuracy has been increased. The method lays the groundwork for the precise control of maize diseases in the field.

Application of PVAR model in the study of influencing factors of carbon emissions.

Based on the panel data of China from 2003 to 2017, this paper applies the panel vector autoregressive (PVAR) model to the study of the influencing factors of carbon emissions. After the cross-section dependence test, unit root test and cointegration test of panel data, the dynamic relationship between energy consumption, economic growth, urbanization, financial development and CO2 emissions is investigated by using PVAR model. Then, we used the impulse response function tool to better understand the reaction of the main variables of interest, CO2 emissions, aftershocks on four factors. Finally, through the variance decomposition of all factors, the influence degree of a single variable on other endogenous variables is obtained. Overall, the results show that the four factors have a significant and positive impact on carbon emissions. In addition, variance decomposition also showed that energy consumption and economic growth strongly explained CO2 emissions. These results indicate that the financial, economic and energy sectors of China's provinces still make relatively weak contributions to reducing carbon emissions and improving environmental quality. Therefore, several policies are proposed and discussed.

Risk Perception and PTSD Symptoms of Medical Staff Combating Against COVID-19: A PLS Structural Equation Model.

Medical staff were battling against coronavirus disease 2019 (COVID-19) at the expense of their physical and mental health, particularly at risk for posttraumatic stress disorder (PTSD). In this case, intervening PTSD of medical staff and preparing them for future outbreaks are important. Previous studies showed that perceived stress was related to the development of PTSD. Hence, in this study, the association between risk perception of medical staff and PTSD symptoms in COVID-19 and the potential links were explored. Three hundred four medical staff's exposure to COVID-19 patients, risk perception for working during COVID-19, PTSD symptoms, anxiety, and sleep quality were measured. Mediation analysis tested the indirect effects of anxiety and sleep quality on the relationship between risk perceptions and PTSD symptoms; 27.6% of participants were deemed as having probable PTSD diagnosis. Mediation analysis showed a significant chain-mediating effect of anxiety and sleep quality on the relationships between risk perceptions and PTSD symptoms; higher risk perceptions were related to increased anxiety, worsened sleep quality, and severe PTSD symptoms. Conclusively, medical staff have a high prevalence of PTSD symptoms after 3 months of COVID-19. Their PTSD symptoms were associated with the perceived risk level through the potential links with anxiety and sleep quality. Therefore, risk perception could be critical for our medical staff's responses to public health emergencies. It could be plausible to intervene in the perceived stress to alleviate aroused anxiety and improve sleep quality and thereby deter the development of PTSD.

The influence of social anxiety on interpersonal information processing in a military-life environment: A cross-sectional study.

Although social anxiety as a ubiquitous emotion impacting people's social behaviors has aroused much researchers' interest in exploring its cognitive behavioral model, no previous study has focused on soldiers with different social anxiety within the context of the specific military environment.To explore the associations between social anxiety and interpersonal information processing concerted on interpretation and judgment, the study may provide an intervention point for soldiers to ameliorate social anxiety and accommodate to the military-life environment.A self-reported questionnaire and 2 behavioral tasks were conducted in the cross-section study to explore the associations.Seventy-four soldiers were randomly recruited from a naval base. The Interpersonal Anxiety Scale was used to assess social anxiety of soldiers. Two behavioral tasks were designed to test the characteristics of interpersonal information processing, one for interpretation bias and the other for judgment bias.This cross-sectional study showed social anxiety had a significant negative correlation with interpretation bias and abidance (as judgment bias), signaling that soldiers with higher levels of social anxiety had a stronger tendency to negative interpretation bias and showed lower abidance. The mediating effect analysis showed the interpretation bias could indirectly affect the soldier's abidance through social anxiety. Notably, none of the interaction effects of social anxiety and social information types were statistically significant; therefore, the level of social anxiety predetermined the abidance of soldiers.Soldiers' social anxiety has an influence on processing military-life interpersonal information, and it plays a certain intermediary role in the associations between low abidance and negative interpretation bias. The stronger negative interpretation bias than positive bias of soldiers, the higher social anxiety they could show with the less possibility to abide, which might result in behaviors against the military collective requirements. Social anxiety has the primary effect on the abidance of soldiers; hence, in the future, the interpretation bias modification could be a plausible cognitive-behavior therapy to help soldiers ameliorate social anxiety, thus contributing to enhancing their sense of belonging to the troops and accommodation to military life.

Social support and mental health among health care workers during Coronavirus Disease 2019 outbreak: A moderated mediation model.

PURPOSES: During the outbreak of Coronavirus Disease 2019 (COVID-19) all over the world, the mental health conditions of health care workers are of great importance to ensure the efficiency of rescue operations. The current study examined the effect of social support on mental health of health care workers and its underlying mechanisms regarding the mediating role of resilience and moderating role of age during the epidemic. METHODS: Social Support Rating Scale (SSRS), Connor-Davidson Resilience scale (CD-RISC) and Symptom Checklist 90 (SCL-90) were administrated among 1472 health care workers from Jiangsu Province, China during the peak period of COVID-19 outbreak. Structural equation modeling (SEM) was used to examine the mediation effect of resilience on the relation between social support and mental health, whereas moderated mediation analysis was performed by Hayes PROCESS macro. RESULTS: The findings showed that resilience could partially mediate the effect of social support on mental health among health care workers. Age group moderated the indirect relationship between social support and mental health via resilience. Specifically, compared with younger health care workers, the association between resilience and mental health would be attenuated in the middle-aged workers. CONCLUSIONS: The results add knowledge to previous literature by uncovering the underlying mechanisms between social support and mental health. The present study has profound implications for mental health services for health care workers during the peak period of COVID-19.

Interpretation bias of high trait anxiety Chinese military servicemen in ambiguous military scenarios.

Converging evidence reveals the negative interpretation bias in anxiety. Given that anxiety is a severe psychological problem among Chinese military personnel, the present study examined whether high trait anxiety military personnel showed negative interpretation bias in real-world situations and whether their interpretations were influenced by self-relevance.The sample included 24 high trait anxiety (H-TA) and 22 low trait anxiety (L-TA) Chinese military servicemen. Participants completed 20 open-ended ambiguous scenarios by deciding how much they believed in the positive and negative ending of each sentence. The 20 scenarios were designed according to real life in military and half of them were self-relevant and the others were non-self-relevant.A 2(group) ×2(self-relevance) ANOVA of positive and negative endings revealed that compared to L-TA, H-TA believed more in negative continuations and less in positive continuations. Moderate correlations were found between samples' believes in positive and negative endings and their trait anxiety scores. Military personnel showed more positive interpretation biases in non-self-relevant scenarios than in self-relevant scenarios.These findings are the first to show interpretation bias in military situations, and interventional strategies to modify servicemen's interpretation bias could be designed according to military situations.

Sanqi oral solution ameliorates renal damage and restores podocyte injury in experimental membranous nephropathy via suppression of NFκB.

Membranous nephropathy (MN) is one of the leading causes of nephrotic syndrome in adults. However, the current treatment of MN has been a matter of fierce debate for decades, and the needs for more advanced pharmaceuticals are critical for improving the treatment strategies. Sanqi oral solution (SQ), mainly consisting of Radix Astragali and Radix Notoginseng, is a formulated product to treat chronic kidney disease for over 20 years with good efficiency in clinic, while the role of SQ on MN remains unclear. In this study, by establishing an experimental rat model of membranous nephropathy induced by cationic Bovine Serum Albumin (C-BSA), we tried to investigate the effects of SQ. We found that administration of SQ ameliorated MN by reducing proteinuria, elevating serum albumin, and ameliorating pathological renal damages. SQ also significantly reduced the C3 and IgG depositions, and restored podocin and synaptopodin expressions. Furthermore, SQ inhibited the activation of nuclear factor-kappa B (NF-κB) signaling pathway. Our results provide evidence that SQ exerts a novel therapeutic effect on MN via reducing proteinuria, ameliorating renal damage and restoring podocyte injuries, which are associated with the suppression of NFκB.