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In the realm of smart agriculture technology's rapid advancement, the integration of various sensors and Internet of Things (IoT) devices has become prevalent in the agricultural sector. Within this context, the precise identification of pests and diseases using unmanned robotic systems assumes a crucial role in ensuring food security, advancing agricultural production, and maintaining food reserves. Nevertheless, existing recognition models encounter inherent limitations such as suboptimal accuracy and excessive computational efforts when dealing with similar pests and diseases in real agricultural scenarios. Consequently, this research introduces the lightweight cross-layer aggregation neural network (LCA-Net). To address the intricate challenge of fine-grained pest identification in agricultural environments, our approach initially enhances the high-performance large-scale network through lightweight adaptation, concurrently incorporating a channel space attention mechanism. This enhancement culminates in the development of a cross-layer feature aggregation (CFA) module, meticulously engineered for seamless mobile deployment while upholding performance integrity. Furthermore, we devised the Cut-Max module, which optimizes the accuracy of crop pest and disease recognition via maximum response region pruning. Thorough experimentation on comprehensive pests and disease datasets substantiated the exceptional fine-grained performance of LCA-Net, achieving an impressive accuracy rate of 83.8%. Additional ablation experiments validated the proposed approach, showcasing a harmonious balance between performance and model parameters, rendering it suitable for practical applications in smart agricultural supervision. [ABSTRACT FROM AUTHOR]

The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event arguments while disregarding the dynamic adjusting of the order according to the importance of argument roles. To address these issues, we propose a model based on graph neural networks which realizes the semantic interaction among documents, sentences, and entities. Additionally, our model adopts a dynamic argument detection strategy, extracting arguments depending on their number in correspondence with each role. The experimental results confirm the outperformance of our model, which surpasses previous methods by 7% and 1.9% in terms of an F1 score. [ABSTRACT FROM AUTHOR]

Multimodal Named Entity Recognition (MNER) and multimodal Relationship Extraction (MRE) play an important role in processing multimodal data and understanding entity relationships across textual and visual domains. However, irrelevant image information may introduce noise that misleads the recognition of information. Additionally, visual and semantic features originate from different modalities, and modal disparity hinders semantic alignment. Therefore, this paper proposes the Visual Description Augmentation Integration Network (VDAIN), which introduces an image description generation technique that allows semantic features generated from image descriptions to be presented in the same modality as the semantic features of textual information. This not only reduces the modal gap but also captures more accurately the high-level semantic information and underlying visual structure in the images. To filter out the modal noise, we use VDAIN to adaptively fuse visual features, semantic features of image descriptions, and textual information, thus eliminating irrelevant modal noise. The F1 score of the proposed model in this paper reaches 75.8% and 87.78% for the MNER task and 82.54% for the MRE task on the three public data sets, respectively, which are significantly better than the baseline model. The experimental results demonstrate the effectiveness of the proposed method in solving the modal noise and modal gap problems. [ABSTRACT FROM AUTHOR]

A novel all-pass slot microring resonator (SMRR), intended for label-free optical biosensing based on silicon-on-insulator platforms, is proposed. The sensor consists of a bent asymmetric directional coupler and an asymmetric-slot microring waveguide. The appropriate slot width of 140 nm is identified by the three-dimensional finite-difference time-domain (3D-FDTD) method for better light–matter interaction in applications. According to numerical calculations, the SMRR sensor with a footprint of 10 µm × 10 µm has a concentration sensitivity of 725.71 pm/% for sodium chloride (NaCl) solutions. The corresponding refractive index sensitivity is 403 nm/RIU (refractive index unit), which is approximately six times greater than that of traditional microring resonator sensors. A low detection limit of 0.129% is also achieved. This SMRR is an excellent candidate for label-free optical biosensors due to its compact structure and excellent sensing capability. [ABSTRACT FROM AUTHOR]

Based on the conjugated complex PVK: Ir(ppy)3, a green-emitting organic quasicrystal microcavity laser is demonstrated driven by optical pumping. The organic laser adopts a sandwich structure of DBR/organic gain membrane/output Ag-layer for the vertical oscillation and an octagonal quasi-crystal for in-plane light localization. The experimental results show that the single-mode lasing action is observed at 521 nm with an FWHM of 0.8 nm. The threshold of lasing is lowered to 0.181 μJ/cm2. [ABSTRACT FROM AUTHOR]

Wasting behavior has become a serious issue in modern society, especially when individuals face economic recessions and environmental problems. Despite the literature exploring cultural and sociological antecedents of wasting behavior, limited attention has been given to the role of individuals' associated psychological states. The present research fills this gap by examining how and why stress, a psychological state pervasive among people in the modern world, can influence individuals' wasting behavior through three studies. Pilot study and Study 1 provide evidence of the positive relationship between stress and wasting behavior. Then, Study 2 sheds light on the mechanism underlying the proposed effect by taking impaired self-control as a mediator. Lastly, the theoretical contributions and practical implications of this research are discussed. [ABSTRACT FROM AUTHOR]

The ripple effect of housing price movements between cities has been extensively investigated, but there are relatively few studies on this topic within a metropolitan context, especially at the submarket level. This paper describes the use of ripple effect theory to examine the diffusion process and convergence of intra-urban housing prices at the submarket level in Shanghai, an emerging global city in China. The analysis is based on directed acyclic graphs, local indicators of spatial association time-paths, and a recently developed convergence test. The empirical results of grouping analysis identify 25 submarkets in Shanghai, and the diffusion of housing prices between these submarkets is found to be caused by both geographical and economic proximities. There is also a complex recursive process of price spillovers from high- to low-priced submarkets, and vice versa, which contributes to the spiraling local housing prices. Housing prices diverge across all submarkets, and the whole market can be divided into three convergence clubs. Finally, these convergence clubs have a circular structure with a degree of continuity. This study broadens our knowledge of the price interrelationship among housing submarkets at the intra-urban level. These findings have profound implications for urban planners, policy makers, and local residents. [ABSTRACT FROM AUTHOR]

Aluminum (Al) toxicity is a major factor limiting crop productivity on acid soils. Soybean (Glycine max) is an important oil crop and there is great variation in Al tolerance in soybean germplasms. However, only a few Al-tolerance genes have been reported in soybean. Therefore, the purpose of this study was to identify candidate Al tolerance genes by comparative transcriptome analysis of two contrasting soybean varieties in response to Al stress. Two soybean varieties, M90-24 (M) and Pella (P), which showed significant difference in Al tolerance, were used for RNA-seq analysis. We identified a total of 354 Al-tolerance related genes, which showed up-regulated expression by Al in the Al-tolerant soybean variety M and higher transcript levels in M than P under Al stress. These genes were enriched in the Gene Ontology (GO) terms of cellular glucan metabolic process and regulation of transcription. Five out of 11 genes in the enriched GO term of cellular glucan metabolic process encode cellulose synthases, and one cellulose synthase gene (Glyma.02G205800) was identified as the key hub gene by co-expression network analysis. Furthermore, treatment of soybean roots with a cellulose biosynthesis inhibitor decreased the Al tolerance, indicating an important role of cellulose production in soybean tolerance to Al toxicity. This study provides a list of candidate genes for further investigation on Al tolerance mechanisms in soybean. [ABSTRACT FROM AUTHOR]

Electronic medical records (EMRs) are extremely important for patients' treatment, doctors' diagnoses, and medical technology development. In recent years, the distributed healthcare blockchain system has been researched for solving the information isolated island problem in centralized healthcare service systems. However, there still exists a series of important problems such as the patients' sensitive information security, cross-institutional data sharing, medical quality, and efficiency. In this paper, we establish a lightweight privacy-preserving mechanism for a healthcare blockchain system. First, we apply an interleaving encoder to encrypt the original EMRs. This can hide the sensitive information of EMRs to protect the patient's privacy security. Second, a ( t , n )-threshold lightweight message sharing scheme is presented. The EMRs are mapped to n different short shares, and it can be reconstructed by at least t shares. The EMR shares rather than the original EMRs are stored in the blockchain nodes. This can guarantee high security for EMR sharing and improve the data reconstruction efficiency. Third, the indexes of the stored EMR shares are employed to generate blocks that are chained together and finally form a blockchain. The authorized data users or institutions can recover an EMR by requesting at least t shares of the EMR from the blockchain nodes. In this way, the healthcare blockchain system can not only facilitate the cross-institution sharing process, but also provide proper protections for the EMRs. The security proof and analysis indicate that the proposed scheme can protect the privacy and security of patients' medical information. The simulation results show that our proposed scheme is more efficient than similar literature in terms of energy consumption and storage space, and the healthcare blockchain system is more stable with the proposed message sharing scheme. [ABSTRACT FROM AUTHOR]

As metabolomics is widely used in the study of disease mechanisms, an increasing number of studies have found that metabolites play an important role in the occurrence of diseases. The aim of this study is to investigate the effects and mechanisms of quercetin in high-fat-sucrose diet (HFD)-induced nonalcoholic fatty liver disease (NAFLD) development using nontargeted metabolomics. A rat model of NAFLD was established by feeding with an HFD for 30 and 50 days. The results indicated quercetin exhibited hepatoprotective activity in 30-day HFD-induced NAFLD rats by regulating fatty acid related metabolites (adrenic acid, etc.), inflammation-related metabolites (arachidonic acid, etc.), oxidative stress-related metabolites (2-hydroxybutyric acid) and other differential metabolites (citric acid, etc.). However, quercetin did not improve NAFLD in the 50-day HFD; perhaps quercetin was unable to reverse the inflammation induced by a long-term high-fat diet. These data indicate that dietary quercetin may be beneficial to NAFLD in early stages. Furthermore, combining metabolomics and experimental approaches opens avenues to study the effects and mechanisms of drugs for complex diseases. [ABSTRACT FROM AUTHOR]