We demonstrate a fiber-optic 3D vector displacement sensor based on the monitoring of Bragg reflection from an eccentric grating inscribed in a depressed-cladding fiber using the femtosecond laser ...side-illumination and phase-mask technique. The compact sensing probe consists of a short section of depressed cladding fiber (DCF) containing eccentrically positioned fiber Bragg gratings. The eccentric grating breaks the cylindrical symmetry of the fiber cross-section and further has bending orientation-dependence. The generated fundamental resonance is strongly sensitive to bending of the fiber, and the direction of the bending plane can be determined from its responses. When integrated with axis strain monitoring, the sensor achieves a 3D vector displacement measurement via simple geometric analysis.
Breath, as an important health monitoring indicator, provides valuable diagnostic information for cardiovascular disease and pulmonary function. Humidity can act as a bridge between breath and ...sensing signals. Current monitoring methods depend on humidity-sensitive material characteristics. In this work, an all fiber-optic flexible humidity sensor for wearable breath monitoring is reported. An eccentric fiber Bragg grating (EFBG) is inscribed in a single mode fiber to excite a stable core mode and sensitive cladding modes. The core mode is shown to maintain stable spectral features under a high-humidity atmosphere and can be used to calibrate the wavelength and power of the system. Importantly, the interface evanescent field of the cladding mode is highly sensitive to the ambient refractive index (RI) and even humidity-induced RI variation. Without combining any sensitized material, EFBG can directly perceive humidity fluctuations during breath with fast response (92 ms) and recovery times (100 ms). Different breathing patterns can be recognized, and breathing frequency can be extracted by sensor responses. The EFBG humidity sensor demonstrates great reproducibility, fast response, high flexibility, excellent robustness, and self-compensation capability, showing promising potential for wearable breath monitoring.
•A novel all-fiber humidity sensor without any additional sensitive material is proposed for human breath monitoring.•The proposed sensor is successfully applied to monitor human breathing with different breathing pattern.•The proposed sensor offers self-compensation for source power fluctuation and temperature effect.•The long-term stability, reliability and reproducibility of proposed sensor are superior to most sensitized material based sensors.
A fringe visibility enhanced fiber-optic Fabry-Perot interferometer based ultrasonic sensor is proposed and experimentally demonstrated for seismic physical model imaging. The sensor consists of a ...graded index multimode fiber collimator and a PTFE (polytetrafluoroethylene) diaphragm to form a Fabry-Perot interferometer. Owing to the increase of the sensor's spectral sideband slope and the smaller Young's modulus of the PTFE diaphragm, a high response to both continuous and pulsed ultrasound with a high SNR of 42.92 dB in 300 kHz is achieved when the spectral sideband filter technique is used to interrogate the sensor. The ultrasonic reconstructed images can clearly differentiate the shape of models with a high resolution.
Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations ...in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models.
This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample.
Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images.
The test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.
Rapid and reliable diagnostic methods for Aspergillus fumigatus infection are urgently needed. Clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein 13a (Cas13a) has ...high sensitivity and specificity in the diagnosis of viral infection. However, its potential use in detecting A. fumigatus remains unexplored. A highly sensitive and specific method using the CRISPR/Cas13a system was developed for the reliable and rapid detection of A. fumigatus.
The conserved internal transcribed spacer (ITS) region of A. fumigatus was used to design CRISPR-derived RNA (crRNA) and the corresponding recombinase polymerase amplification (RPA) primer sequence with the T7 promoter for the CRISPR assay. Twenty-five clinical isolates and 43 bronchoalveolar lavage fluid (BALF) remaining from routine examinations of patients with confirmed pulmonary aspergillosis were collected to further validate the CRISPR assay.
No amplification signal was observed when genomic DNA from closely clinically related Aspergillus species, such as Aspergillus flavus, Aspergillus niger, and Aspergillus terreus, as well as Monascus purpureus Went and Escherichia coli, was tested by this assay, and the detection limit for A. fumigatus was 3 copies in a single reaction system. Validation experiments using the 25 clinical isolates demonstrated 91.7% specificity for the A. fumigatus section, and the sensitivity was 100% when first-generation sequencing was used as the standard. There was no significant difference between the PCR and CRISPR methods (P = 1.0), and the diagnosis results of the two methods were consistent (Kappa = 0.459, P = 0.003).
The study offers a new validated CRISPR/Cas13a technique for A. fumigatus detection, providing a simple, rapid and affordable test that is ready for application in the diagnosis of A. fumigatus infection.
Despite advances in deep learning for plant leaf disease recognition, accurately distinguishing morphological features under varying environmental conditions continues to pose significant challenges. ...Traditional deep learning models often fail to effectively merge local and global information, especially in small-scale datasets, impairing performance and elevating training costs. Focusing on citrus diseases, we propose an improved FasterViT Model, an advanced hybrid CNN-ViT framework that builds upon the FasterViT model. The proposed model seamlessly integrates CNN's rapid local learning capabilities with ViT's global information processing strength, thereby effectively extracting complex textures and morphological features from images. Cross-stage alternating Mixup and Cutout methods are strategically employed to enhance model robustness and generalization capabilities, particularly valuable for fast learning on small-scale datasets by simulating a more diverse training environment. Triplet Attention and AdaptiveAvgPool mechanisms are utilized to reduce training costs and optimize training performance. The proposed model is tested on both our specially constructed small-scale citrus disease dataset called in-field small dataset and the comprehensive PlantVillage dataset. The experimental results demonstrated that the model exhibits the capability of fast learning and adaptation to small sample training in plant disease detection tasks, and demonstrates the effectiveness of our improvement approach in improving model accuracy and reducing training costs. Additionally, its exemplary performance in transfer learning scenarios underscores its adaptability and broad applicability. This study not only highlights the efficacy of the improved FasterViT model in addressing the complexities of plant disease image recognition but also pioneers a new paradigm for developing efficient, scalable, and robust classification systems.
Reticulocalbin‐1 (RCN1) is expressed aberrantly and at a high level in various tumors, including acute myeloid leukemia (AML), yet its impact on AML remains unclear. In this study, we demonstrate ...that RCN1 knockdown significantly suppresses the viability of bone marrow mononuclear cells (BMMNCs) from AML patients but does not affect the viability of granulocyte colony‐stimulating factor (G‐CSF)‐mobilized peripheral blood stem cells (PBSCs) from healthy donors in vitro. Downregulation of RCN1 also reduces the viability of AML cell lines. Further studies showed that the RCN1 knockdown upregulates type I interferon (IFN‐1) expression and promotes AML cell pyroptosis through caspase‐1 and gasdermin D (GSDMD) signaling. Deletion of the mouse Rcn1 gene inhibits the viability of mouse AML cell lines but not the hematopoiesis of mouse bone marrow. In addition, RCN1 downregulation in human AML cells significantly inhibited tumor growth in the NSG mouse xenograft model. Taken together, our results suggest that RCN1 may be a potential target for AML therapy.
This study showed that RCN1 downregulation upregulates type I interferon (IFN‐1) by activating the STING pathway. Consequently, this results in an increased expression of cleaved‐caspase‐1 and IL‐1β, which triggers pyroptosis in AML cells via gasdermin D (GSDMD) signaling. These findings provide potential targets for AML therapy.
Microservice architecture (MSA) is a new software architecture, which divides a large single application and service into dozens of supporting microservices. With the increasingly popularity of MSA, ...the security issues of MSA get a lot of attention. In this paper, we propose an algorithm for mining causality and the root cause. Our algorithm consists of two parts: invocation chain anomaly analysis based on robust principal component analysis (RPCA) and a single indicator anomaly detection algorithm. The single indicator anomaly detection algorithm is composed of Isolation Forest (IF) algorithm, One-Class Support Vector Machine (SVM) algorithm, Local Outlier Factor (LOF) algorithm, and <inline-formula> <tex-math notation="LaTeX">3\sigma </tex-math></inline-formula> principle. For general and network time-consuming anomaly in the process of the MSA, we formulate different anomaly time-consuming detection strategies. We select a batch of sample data and three batches of test data of the 2020 International AIOps Challenge to debug our algorithm. According to the scoring criteria of the competition organizers, our algorithm has an average score of 0.8304 (The full score is 1) in the four batches of data. Our proposed algorithm has higher accuracy than some traditional machine learning algorithms in anomaly detection.
Based on the rockery photos of 10 classical gardens including world cultural heritage, this paper incorporates the combined analysis and comparison of constituent elements (rockeries, buildings and ...plants) of scenic surfaces, and probes into the fractal characteristics of landscaping through the theory and the fractal dimension (FD) value analysis software. Studies have shown that the quantitative evaluation data of visual complexity (FD, i.e., fractal dimension) can characterize the contour morphology of constituent elements of the scenic surface of rockeries. The relevant analysis results are as follows: (1) FD can directly quantify the morphological contour of each element. Through the statistical analysis, it can effectively avoid the misjudgment of empirical cognition and subjective feeling. Therefore, FD value can be used as one of the effective indexes to evaluate the complexity and diversity of rockery and landscape elements. (2) The change in the level of the FD value enables the intuitive analysis of the effects of the plant varieties and landscaping techniques on rockery morphological complexity. (3) Higher FD value is not always better. Necessary morphological maintenance is required to avoid excessive FD value of plants.
PDF
