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In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method’s effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method’s capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture.

This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. The system significantly enhances the accuracy and efficiency of disease detection through an innovative graph attention mechanism and optimized loss functions. Experimental results demonstrate that this system significantly outperforms traditional methods across key metrics such as precision, recall, and accuracy, with the graph attention mechanism excelling in all aspects, particularly achieving a precision of 0.94, a recall of 0.92, and an accuracy of 0.93. Furthermore, comparative experiments with various loss functions further validate the effectiveness of the graph attention loss mechanism in enhancing model performance. This research not only advances the application of deep learning in agricultural disease detection theoretically but also provides robust technological tools for disease management and decision support in actual agricultural production, showcasing broad application prospects and profound practical value.

Multiplex polymerase chain reaction (PCR) assays are mainly used to simultaneously detect or identify multiple pathogenic microorganisms. To achieve high specificity for detecting foodborne pathogenic bacteria, specific primers need to be designed for the target strains. In this study, we designed and achieved a multiplex PCR system for detecting eight foodborne pathogenic bacteria using specific genes: toxS for Vibrio parahaemolyticus, virR for Listeria monocytogenes, recN for Cronobacter sakazakii, ipaH for Shigella flexneri, CarA for Pseudomonas putida, rfbE for Escherichia coli, vvhA for Vibrio vulnificus, and gyrB for Vibrio alginolyticus. The sensitivity of the single system in this study was found to be 20, 1.5, 15, 15, 13, 14, 17, and 1.8 pg for V. parahaemolyticus, L. monocytogenes, E. coli O157:H7, C. sakazakii, S. flexneri, P. putida, V. vulnificus, and V. alginolyticus, respectively. The minimum detection limit of the multiplex system reaches pg/μL detection level; in addition, the multiplex system exhibited good specificity and stability. Finally, the assays maintained good specificity and sensitivity of 104 CFU/mL for most of the samples and we used 176 samples of eight aquatic foods, which were artificially contaminated to simulate the detection of real samples. In conclusion, the multiplex PCR method is stable, specific, sensitive, and time-efficient. Moreover, the method is well suited for contamination detection in these eight aquatic foods and can rapidly detect pathogenic microorganisms.

The vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance evaluation technique (DET) is proposed to solve this problem. Firstly, the vibration signal of the motor bearing is decomposed by ensemble empirical mode decomposition (EEMD). The proper intrinsic mode function (IMF) component that is the most sensitive to the degradation of the motor bearing is selected according to the sensitive IMF selection algorithm based on the similarity evaluation. Then the distance evaluation factor of each characteristic parameter is calculated by the DET method. The differential method is used to extract sensitive characteristic parameters which compose the characteristic matrix. And then the extracted degradation characteristic matrix is used as the input of support vector machine (SVM) to identify the degradation state. Finally, It is demonstrated that the proposed hybrid domain feature extraction method has higher recognition accuracy and shorter recognition time by comparative analysis. The positive performance of the method is verified.

The photoconversion of 2-chloronaphthalene (CN-2) in water in a simulated sunlight system was investigated. The photoconversion efficiency, photoproducts and mechanisms were inspected, and the effects of inorganic ions (NO 3 − , NO 2 − ) and fulvic acid (FA) were discussed. The results showed that CN-2 could be transformed in water under the irradiation. NO 3 − and NO 2 − promoted the photoconversion of CN-2 owing to ·OH generated by the photolysis of NO 3 − and NO 2 − ; FA at a lower concentration promoted the photoconversion, but it had an inhibition effect at a higher concentration. It was demonstrated that the acidic conditions promoted the photoconversion of CN-2 by the active groups such as superoxide radical anion, hydrogen peroxide and hydroxyl radical produced in the system. Eight photoproducts of CN-2 were characterized by the GC-MS method and the possible photoconversion mechanisms were proposed accordingly.

The traditional Chinese medicine Buyang Huanwu Decoction has been shown to improve the neu- rological function of patients with stroke. However, the precise mechanisms underlying its effect remain poorly understood. In this study, we established a rat model of cerebral ischemia by middle cerebral artery occlusion and intragastrically administered 5 g/kg Buyang Huanwu Decoction, once per day, for 1, 7, 14 and 28 days after cerebral ischemia. Immunohistochemical staining revealed a number of cells positive for the neural stem cell marker nestin in the cerebral cortex, the subven- tricular zone and the ipsilateral hippocampal dentate gyrus in rat models of cerebral ischemia. Buyang Huanwu Decoction significantly increased the number of cells positive for 5-bromodeoxyuridine （BrdU）, a cell proliferation-related marker, microtubule-associated protein-2, a marker of neuronal differentiation, and growth-associated protein 43, a marker of synaptic plasticity in the ischemic rat cerebral regions. The number of positive cells peaked at 14 and 28 days after intragastric administration of Buyang Huanwu Decoction. These findings suggest that Buyang Huanwu Decoction can promote the proliferation and differentiation of neural stem cells and en- hance synaptic plasticity in ischemic rat brain tissue.

Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things. A wearable ultrasound patch monitors subjects in motion using machine learning and wireless electronics.

Electronic patches, based on various mechanisms, allow continuous and noninvasive monitoring of biomolecules on the skin surface. However, to date, such devices are unable to sense biomolecules in deep tissues, which have a stronger and faster correlation with the human physiological status than those on the skin surface. Here, we demonstrate a photoacoustic patch for three-dimensional (3D) mapping of hemoglobin in deep tissues. This photoacoustic patch integrates an array of ultrasonic transducers and vertical-cavity surface-emitting laser (VCSEL) diodes on a common soft substrate. The high-power VCSEL diodes can generate laser pulses that penetrate >2 cm into biological tissues and activate hemoglobin molecules to generate acoustic waves, which can be collected by the transducers for 3D imaging of the hemoglobin with a high spatial resolution. Additionally, the photoacoustic signal amplitude and temperature have a linear relationship, which allows 3D mapping of core temperatures with high accuracy and fast response. With access to biomolecules in deep tissues, this technology adds unprecedented capabilities to wearable electronics and thus holds significant implications for various applications in both basic research and clinical practice. The authors present a wearable photoacoustic patch, which integrates laser diodes and piezoelectric transducers for three-dimensional imaging of hemoglobin and temperature in deep tissues.

Organic–inorganic hybrid perovskites have electronic and optoelectronic properties that make them appealing in many device applications 1 – 4 . Although many approaches focus on polycrystalline materials 5 – 7 , single-crystal hybrid perovskites show improved carrier transport and enhanced stability over their polycrystalline counterparts, due to their orientation-dependent transport behaviour 8 – 10 and lower defect concentrations 11 , 12 . However, the fabrication of single-crystal hybrid perovskites, and controlling their morphology and composition, are challenging 12 . Here we report a solution-based lithography-assisted epitaxial-growth-and-transfer method for fabricating single-crystal hybrid perovskites on arbitrary substrates, with precise control of their thickness (from about 600 nanometres to about 100 micrometres), area (continuous thin films up to about 5.5 centimetres by 5.5 centimetres), and composition gradient in the thickness direction (for example, from methylammonium lead iodide, MAPbI 3 , to MAPb 0.5 Sn 0.5 I 3 ). The transferred single-crystal hybrid perovskites are of comparable quality to those directly grown on epitaxial substrates, and are mechanically flexible depending on the thickness. Lead–tin gradient alloying allows the formation of a graded electronic bandgap, which increases the carrier mobility and impedes carrier recombination. Devices based on these single-crystal hybrid perovskites show not only high stability against various degradation factors but also good performance (for example, solar cells based on lead–tin-gradient structures with an average efficiency of 18.77 per cent). A solution-based lithography-assisted epitaxial-growth-and-transfer method is used to fabricate single-crystal hybrid perovskites on any surface, with precise control of the thickness, area and chemical composition gradient.

Immunomodulation has emerged as a promising strategy for promoting bone regeneration. However, designing osteoimmunomodulatory biomaterial that can respond to mechanical stress in the unique microenvironment of alveolar bone under continuous occlusal stress remains a significant challenge. Herein, a wireless piezoelectric stimulation system, namely, piezoelectric hydrogel incorporating BaTiO3 nanoparticles (BTO NPs), is successfully developed to generate piezoelectric potentials for modulating macrophage reprogramming. The piezoelectric stimulation reprograms macrophages towards the M2 phenotype, which subsequently induces osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). RNA sequencing analysis reveals that piezoelectricity‐modulated macrophage M2 polarization is closely associated with metabolic reprogramming, including increased amino acid biosynthesis and fatty acid oxidation. The composite hydrogel with excellent biocompatibility exhibits immunomodulatory and osteoinductive activities. In a rat model of alveolar bone defects, the piezoelectric hydrogel effectively promotes endogenous bone regeneration at the load‐bearing sites. The piezoelectric‐driven osteoimmunomodulation proposed in this study not only broadens understanding of the mechanism underlying piezoelectric biomaterials for tissue regeneration but also provides new insights into the design and development of next‐generation immunomodulatory biomaterials. BTO NPs‐mediated piezoelectric stimulation effectively induced macrophage M2 polarization and metabolic reprogramming, represented by enhanced TCA cycle and one‐carbon folate cycle. After that, anti‐inflammatory and pro‐differentiation M2 promoted osteogenic differentiation of bone marrow mesenchymal stem cells. The osteo‐friendly immune microenvironment formed by piezoelectric stimulation effectively promoted the regeneration of alveolar bone defects.