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Abstract Fungal diseases are the main factors affecting the quality and production of vegetables. Rapid and accurate detection of pathogenic spores is of great practical significance for early prediction and prevention of diseases. However, there are some problems with microscopic images collected in the natural environment, such as complex backgrounds, more disturbing materials, small size of spores, and various forms. Therefore, this study proposed an improved detection method of GCS-YOLOv8 (Global context and CARFAE and Small detector-optimized YOLOv8), effectively improving the detection accuracy of small-target pathogen spores in natural scenes. Firstly, by adding a small target detection layer in the network, the network’s sensitivity to small targets is enhanced, and the problem of low detection accuracy of the small target is effectively improved. Secondly, Global Context attention is introduced in Backbone to optimize the CSPDarknet53 to 2-Stage FPN (C2F) module and model global context information. At the same time, the feature up-sampling module Content-Aware Reassembly of Features (CARAFE) was introduced into Neck to enhance the ability of the network to extract spore features in natural scenes further. Finally, we used an Explainable Artificial Intelligence (XAI) approach to interpret the model’s predictions. The experimental results showed that the improved GCS-YOLOv8 model could detect the spores of the three fungi with an accuracy of 0.926 and a model size of 22.8 MB, which was significantly superior to the existing model and showed good robustness under different brightness conditions. The test on the microscopic images of the infection structure of cucumber down mildew also proved that the model had good generalization. Therefore, this study realized the accurate detection of pathogen spores in natural scenes and provided feasible technical support for early predicting and preventing fungal diseases.

Traditional studies typically employed random and deliberate attack methods to explore port failure, overlooking real-world factors. In this research, we focus on exploring the reliability of the Maritime Silk Road (MSR) container shipping networks after the failure of Chinese coastal ports due to the impact of typhoons. This article analyzes AIS trajectory data and typhoon occurrence data through entropy weight method and grey correlation analysis, to construct an evaluation model for the failure of Chinese critical ports. Then, we will look at the effects of deliberately removing Chinese critical ports from the MSR container shipping system. The main conclusions drawn are as follows: (1) Except for some of the largest ports (i.e., Ningbo-Zhoushan, Shanghai, and Kaohsiung), the importance of Chinese coastal ports varies slightly in the MSR container maritime network, while it varies widely in their exposure and resistance to typhoons. (2) Ports of Ningbo Zhoushan, Shanghai, Hong Kong, Shekou, Kaohsiung, and Yantian rank high in the comprehensive evaluation results. They have a higher probability of failure when affected by typhoon risks. (3) After the failure of Chinese crucial ports, the reliability of the MSR container maritime network successively declined, recovered, and stabilized. This study can offer a valuable reference for relevant actors involved in safeguarding crucial ports, finding alternative ports, optimizing shipping routes, and improving the reliability of maritime networks under typhoons and other major natural disasters.

Abstract Background In the era of Agri 4.0 and the popularity of Plantwise systems, the availability of Plant Electronic Medical Records has provided opportunities to extract valuable disease information and treatment knowledge. However, developing an effective prescription recommendation method based on these records presents unique challenges, such as inadequate labeling data, lack of structural and linguistic specifications, incorporation of new prescriptions, and consideration of multiple factors in practical situations. Results This study proposes a plant disease prescription recommendation method called PRSER, which is based on sentence embedding retrieval. The semantic matching model is created using a pre-trained language model and a sentence embedding method with contrast learning ideas, and the constructed prescription reference database is retrieved for optimal prescription recommendations. A multi-vegetable disease dataset and a multi-fruit disease dataset are constructed to compare three pre-trained language models, four pooling types, and two loss functions. The PRSER model achieves the best semantic matching performance by combining MacBERT, CoSENT, and CLS pooling, resulting in a Pearson coefficient of 86.34% and a Spearman coefficient of 77.67%. The prescription recommendation capability of the model is also verified. PRSER performs well in closed-set testing with Top-1/Top-3/Top-5 accuracy of 88.20%/96.07%/97.70%; and slightly worse in open-set testing with Top-1/Top-3/Top-5 accuracy of 82.04%/91.50%/94.90%. Finally, a plant disease prescription recommendation system for mobile terminals is constructed and its generalization ability with incomplete inputs is verified. When only symptom information is available without environment and plant information, our model shows slightly lower accuracy with Top-1/Top-3/Top-5 accuracy of 75.24%/88.35%/91.99% in closed-set testing and Top-1/Top-3/Top-5 accuracy of 75.08%/87.54%/89.84% in open-set testing. Conclusions The experiments validate the effectiveness and generalization ability of the proposed approach for recommending plant disease prescriptions. This research has significant potential to facilitate the implementation of artificial intelligence in plant disease treatment, addressing the needs of farmers and advancing scientific plant disease management.

Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features, for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning, a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically, a trajectory encoding module is designed to mine the semantic information of trajectory data, and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular, a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai, and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.

Abstract Background Automatic and accurate estimation of disease severity is critical for disease management and yield loss prediction. Conventional disease severity estimation is performed using images with simple backgrounds, which is limited in practical applications. Thus, there is an urgent need to develop a method for estimating the disease severity of plants based on leaf images captured in field conditions, which is very challenging since the intensity of sunlight is constantly changing, and the image background is complicated. Results This study developed a simple and accurate image-based disease severity estimation method using an optimized neural network. A hybrid attention and transfer learning optimized semantic segmentation model was proposed to obtain the disease segmentation map. The severity was calculated by the ratio of lesion pixels to leaf pixels. The proposed method was validated using cucumber downy mildew, and powdery mildew leaves collected under natural conditions. The results showed that hybrid attention with the interaction of spatial attention and channel attention can extract fine lesion and leaf features, and transfer learning can further improve the segmentation accuracy of the model. The proposed method can accurately segment healthy leaves and lesions (MIoU = 81.23%, FWIoU = 91.89%). In addition, the severity of cucumber leaf disease was accurately estimated (R2 = 0.9578, RMSE = 1.1385). Moreover, the proposed model was compared with six different backbones and four semantic segmentation models. The results show that the proposed model outperforms the compared models under complex conditions, and can refine lesion segmentation and accurately estimate the disease severity. Conclusions The proposed method was an efficient tool for disease severity estimation in field conditions. This study can facilitate the implementation of artificial intelligence for rapid disease severity estimation and control in agriculture.

Abstract Background Cyclin-dependent kinase 16 (CDK16) is an atypical PCTAIRE kinase, and its activity is dependent on the Cyclin Y (CCNY) family. Ccnys have been reported to regulate mammary stem cell activity and mammary gland development, and CCNY has been recognized as an oncoprotein in various cancers, including breast cancer. However, it remains unclear whether CDK16 has a role in breast cancer and whether it can be used as a therapeutic target for breast cancer. Methods Publicly available breast cancer datasets analyses and Kaplan-Meier survival analyses were performed to reveal the expression and clinical relevance of atypical CDKs in breast cancer. CDK16 protein expression was further examined by immunohistochemical and immunoblot analyses of clinical samples. Cell proliferation was measured by colony formation and MTT analyses. Cell cycle and apoptosis were examined by fluorescence-activated cell sorting (FACS) analysis. Wound-healing and trans-well invasion assays were conducted to test cell migration ability. The functions of CDK16 on tumorigenesis and metastasis were evaluated by cell line-derived xenograft, patient-derived organoid/xenograft, lung metastasis and systemic metastasis mouse models. Transcriptomic analysis was performed to reveal the potential molecular mechanisms involved in the function of CDK16. Pharmacological inhibition of CDK16 was achieved by the small molecular inhibitor rebastinib to further assess the anti-tumor utility of targeting CDK16. Results CDK16 is highly expressed in breast cancer, particularly in triple-negative breast cancer (TNBC). The elevated CDK16 expression is correlated with poor outcomes in breast cancer patients. CDK16 can improve the proliferation and migration ability of TNBC cells in vitro, and promote tumor growth and metastasis of TNBC in vivo. Both genetic knockdown and pharmacological inhibition of CDK16 significantly suppress the tumor progression of TNBC. Mechanistically, CDK16 exerts its function by phosphorylating protein regulator of cytokinesis 1 (PRC1) to regulate spindle formation during mitosis. Conclusion CDK16 plays a critical role in TNBC and is a novel promising therapeutic target for TNBC.

Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture. Traditional detection methods are time-consuming, laborious, and subjective, and image processing methods mainly rely on manually designed features that are difficult to cope with pathogen spore detection in complex scenes. Therefore, an MG-YOLO detection algorithm (Multi-head self-attention and Ghost-optimized YOLO) is proposed to detect gray mold spores rapidly. Firstly, Multi-head self-attention is introduced in the backbone to capture the global information of the pathogen spores. Secondly, we combine weighted Bidirectional Feature Pyramid Network (BiFPN) to fuse multiscale features of different layers. Then, a lightweight network is used to construct GhostCSP to optimize the neck part. Cucumber gray mold spores are used as the study object. The experimental results show that the improved MG-YOLO model achieves an accuracy of 0.983 for detecting gray mold spores and takes 0.009 s per image, which is significantly better than the state-of-the-art model. The visualization of the detection results shows that MG-YOLO effectively solves the detection of spores in blurred, small targets, multimorphology, and high-density scenes. Meanwhile, compared with the YOLOv5 model, the detection accuracy of the improved model is improved by 6.8%. It can meet the demand for high-precision detection of spores and provides a novel method to enhance the objectivity of pathogen spore detection.

The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale.

Assortativity, one of the mixing patterns of complex networks, is characterized by measuring whether the nodes are preferentially connected to the nodes with a similar scale. While numerous studies have examined the assortative characteristics of various real-world networks, few studies have attempted to analyze the assortativity of networks in which the subject of trade is bulk. The novelty of this research is that, for the first time, the assortative coefficient method in physics is introduced into the bulk trade network, and the Automatic Identification System (AIS) data is used to explore the assortative mixing characteristics of the network. From the perspective of multi-scale (port and country) and multi-dimensional (node, link, and network) structure, this paper reveals the tendency of trade connection and explores the trade rules of bulk in networks. The results show that: (1) The trade network of bulk on the Maritime Silk Road is assortative. With the increase of spatial scale, the extent of assortativity is also gradually increasing; (2) In the bulk network of ports, trade cooperation shows the rule of distance attenuation; In the national bulk network, it shows the rule of preferential connection; (3) Ports with high out-degree will export bulk to the ports with high out-degree with broad market, while countries with high out-degree export to high in-degree countries with strong demand. The present study is expected to provide valuable references for port planning, national formulation of scientific bulk trade strategy, and promotion of coordinated development of bulk trade network along the Maritime Silk Road.

As one of the most strategically important natural resources, crude oil can be dangerous to transport by sea. The resilience of a maritime transport network denotes the ability of the system to withstand damage and remain operational after disturbances, representing the invulnerability of the network and its ability to recover from harm. In this study, we built a crude oil transportation network titled the Maritime Silk Road using Automatic Identification System (AIS) sensor data, designed a resilience assessment framework based on complex network theory, and assessed the resilience of the maritime crude oil transportation network from both qualitative and quantitative perspectives with the help of complex network metrics and a resilience model. The results show that the topology of the crude oil transportation network has a significant impact on its resilience, in that network density and centrality are negatively related to network resilience, whereas network connectivity and size are positively related to resilience. Subsequently, the resilience of crude oil transportation networks declines at a steady rate under random attacks but declines sharply under intentional attacks. Finally, a comprehensive analysis of invulnerability and recovery in the context of resilience concludes that strengthening small and medium-sized ports in the network is important to enhance network resilience. These results can provide reference and decision support for port planning, route design, optimization, and form a foundation for a more secure and reliable maritime transportation network system.

Holiday tourism flow is a significant indicator to evaluate the development of tourism. The exploration of the rule of tourism flow between cities can not only provide reasonable suggestions for stimulating demand, promoting consumption and economic development, but also make crucial significance for the management of tourism destinations and the optimization of spatial structure of tourism flow. Based on Weibo check-in data, this paper, by using social network analysis, studies the spatial distribution and network structure characteristics of tourism flow in 50 major cities in China during the National Day holiday in 2018. The results show that: 1) the tourism flow connection of the main cities in China represents a diamond shaped spatial structure with “Beijing-Shanghai-Guangzhou-Chengdu” as the core. There exists spatial heterogeneity in different levels of tourism flow intensity and proximity and selectivity in tourism links between cities; 2) the intensity of tourism connection between cities in China is clearly divided into different levels. On the basis of index of degree of centrality, Beijing and Shanghai are far higher than other cities;3) there are obvious differences between core nodes and edge nodes, with the core nodes often composed of cities with high economic development or rich tourism resources. Although the number is small, it plays a significant role in driving the edge cities; 4) the urban tourism flow network is relatively stable, but most cities have relatively weak tourism links and more small-scale tourism flows. In the division of cohesive subgroups, the fifth and sixth subgroups are not only the main tourist sources but also the main destinations. Whether it is the internal connection of subgroups or the connection with other subgroups, tourism flow has a very high density of connection.

Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images. In order to achieve robust segmentation, a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper. The segmentation method was based on a decision tree which was constructed by a two-step coarse-to-fine procedure. Firstly, a coarse decision tree was built by the CART (Classification and Regression Tree) algorithm with a feature subset. The feature subset consisted of color features that was selected by Pearson’s Rank correlations. Then, the coarse decision tree was optimized by pruning. Using the optimized decision tree, segmentation of disease symptom images was achieved by conducting pixel-wise classification. In order to evaluate the robustness and accuracy of the proposed method, an experiment was performed using greenhouse cucumber downy mildew images. Results showed that the proposed method achieved an overall accuracy of 90.67%, indicating that the method was able to obtain robust segmentation of disease symptom images. Keywords: Greenhouse vegetables, Symptom images, Decision tree, Image segmentation

China is the largest producer and consumer of vegetables, its vegetable industry is playing an important role in the domestic agricultural sector and global vegetable export market. It is important to promote the long-term sustainable development of Chinese vegetable industry for food security and quality of people’s lives. To find out the intrinsic way to promote the development of Chinese vegetable industry, this paper analyzed the influencing factors of Chinese vegetable production by utilizing the LMDI method and demonstrated the spatial-temporal characteristics of vegetable production through application of the ArcGIS spatial autocorrelation analysis method. The results showed that the influencing factors of vegetable production were the cultivated land area, multiple cropping index, vegetable planting proportion and vegetable yield per hectare in China. The major driving factor had changed from vegetable planting proportion to vegetable yield per hectare. The influencing degrees of factors on vegetable production are different in different regions, regionalization is therefore a major feature of Chinese vegetable production. The government should take production technology, regionalization-driven effect, and marketing integration into consideration to promote the development of Chinese vegetable industry. Keywords: Vegetable production, Influencing factors, Contribution ratio, Logarithmic mean Divisia index (LMDI)

The paper utilizes the data envelopment analysis (DEA) method based on the OECD policy classification to evaluate the performance of the agricultural support policy at the provincial and agricultural commodity levels from the respective viewpoints of agricultural input and output. The analysis at the provincial level mainly focuses on the similarities and differences in the performance of the agricultural support policies between the primary grain-producing areas and the secondary grain-producing areas; at the agricultural commodity level, 17 representative Chinese agricultural commodities are selected and their performance compared. The results are then used to proffer suggestions for the future agricultural support policy optimization in China. The paper found that 50% of the provinces were DEA-efficient, in general, during the study period due to the low overall province-level scale efficiency. The performance values of the secondary grain-producing areas were significantly lower than those of the primary grain-producing areas due to the overall low scale and technical efficiency values. These results show that the support policies for agricultural commodities in China altogether require a further improvement, mainly due to the low technical efficiency of agricultural commodities.

Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk Road traffic network. In this study, we used a complex network theory along with social network analysis and network flow analysis to analyze the spatial distribution characteristics of maritime traffic flow of the Maritime Silk Road; further, we empirically demonstrate the traffic inequality in the route. On this basis, we explore the role of the country in the maritime traffic system and the resulting traffic relations. There are three main results of this study. (1) The inequality in the maritime traffic of the Maritime Silk Road has led to obvious regional differences. Europe, west Asia, northeast Asia, and southeast Asia are the dominant regions of the Maritime Silk Road. (2) Different countries play different maritime traffic roles. Italy, Singapore, and China are the core countries in the maritime traffic network of the Maritime Silk Road; Greece, Turkey, Cyprus, Lebanon, and Israel have built a structure of maritime traffic flow in the eastern Mediterranean Sea, and Saudi Arabia serves as a bridge for maritime trade between Asia and Europe. (3) The maritime traffic relations show the characteristics of regionalization; countries in west Asia and the European Mediterranean region are clearly polarized, and competition–synergy relations have become the main form of maritime traffic relations among the countries in the dominant regions. Our results can provide a scientific reference for the coordinated development of regional shipping, improvement of maritime competition, cooperation strategies for countries, and adjustments in the organizational structure of ports along the Maritime Silk Road.

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.