A photothermal fiber sensor based on a microfiber knot resonator (MKR) and the Vernier effect is proposed and demonstrated. An MXene Ti3C2Tx nanosheet was deposited onto the ring of an MKR using an optical deposition method to prepare photothermal devices. An MXene MKR and a bare MKR were used as the sensing part and reference part, respectively, of a Vernier-cascade system. The optical and photothermal properties of the bare MKR and the MXene MKR were tested. Ti3C2Tx was applied to a photothermal fiber sensor for the first time. The experimental results showed that the modulation efficiency of the MXene MKR was 0.02 nm/mW, and based on the Vernier effect, the modulation efficiency of the cascade system was 0.15 nm/mW. The sensitivity was amplified 7.5 times. Our all-fiber photothermal sensor has many advantages such as low cost, small size, and good system compatibility. Our sensor has broad application prospects in many fields. The proposed stable MKR device based on two-dimensional-material modification provides a new solution for improving the sensitivity of optical fiber sensors.
We report on all-optical devices prepared from WSe2 combined with drawn tapered fibers as saturable absorbers to achieve ultrashort pulse output. The saturable absorber with a high damage threshold and high saturable absorption characteristics is prepared for application in erbium-doped fiber lasers by the liquid phase exfoliation method for WSe2, and the all-optical device exhibited strong saturable absorption characteristics with a modulation depth of 15% and a saturation intensity of 100.58 W. The net dispersion of the erbium-doped fiber laser cavity is ~−0.1 ps2, and a femtosecond pulse output with a bandwidth of 11.4 nm, a pulse width of 390 fs, and a single-pulse capability of 42 pJ is obtained. Results indicate that the proposed WSe2 saturable absorbers are efficient, photonic devices to realize stable fiber lasers. The results demonstrate that the WSe2 saturable absorber is an effective photonic device for realizing stable fiber lasers, which have a certain significance for the development of potential photonic devices.
The goal of this study was to model the total leaf chlorophyll content (LCC tot) of Gannan navel orange leaves using a field imaging spectroscopy system in the visible and near-infrared domain. The spectral range from 400 to 1000 nm with 176 wavebands (a wavelength interval of 3.41 nm) or 360 wavebands (a wavelength interval of 1.67 nm), labeled as ''Datasets_1.67'' and ''Datasets_3.41'', respectively, were used. Although different spectral data types were used, better prediction results for LCC tot were based on Datasets_1.67 for LCC tot prediction. Several prediction models of LCC tot were built based on partial least squares regression (PLSR), artificial neural networks (ANN), ordinary least squares regression (OLSR), and stepwise linear regression (SLR) using full spectral and effective wavelength (EW) data (raw spectral (RS), first derivative spectral (FDS) and second derivative spectral (SDS) data). The determination coefficient (R 2), the root mean square error (RMSE) and the residual predictive deviation (RPD) were used to evaluate the reliability and accuracy of the predicted LCC tot values. As a result, 14 (7 obtained from Datasets_1.67, 7 obtained from Datasets_3.41), 39 (21 obtained from Datasets_1.67, 18 obtained from Datasets_3.41) and 50 (27 obtained from Datasets_1.67, 23 obtained from Datasets_3.41) wavebands were selected from the RS data, FDS data and SDS data, respectively, as the EWs for LCC tot prediction of navel orange leaves. After that, PLSR and ANN predictive models were established using full spectra, and OLSR and SLR predictive models were built using the selected EWs. The experimental results demonstrated that these various regression methods were useful for estimating LCC tot in the order of PLSR models established using full spectra from RS data (F-RS-PLSR) > PLSR models established using full spectra from SDS data (F-SDS-PLSR) > PLSR models established using full spectra from FDS data (F-FDS-PLSR) > SLR models established using EWs by RS data (EWs-RS-SLR). However, models built with ANN and OLSR, where the RPD values were less than 3, cause the models to be inaccurate. Finally, in comparison, the F-RS-PLSR model exhibited the best performance of LCC tot estimation; with the number of principal components (Pcs) = 5, this model provided high values of the R 2 of calibration (C-R 2) = 0.92 and the R 2 of validation (V-R 2) = 0.96, small values of the RMSE of calibration (C-RMSE)=0.05 mg/g and the RMSE of validation (V-RMSE) = 0.19 mg/g, and sufficient the RPD of calibration (C-RPD)=17.00 and the RPD of validation (V-RPD)=3.63 values. Overall, the best modeling method was PLSR. Hence, the PLSR applicability for assessing chlorophyll content in navel orange leaves was demonstrated. INDEX TERMS Chlorophyll, hyperspectral data, navel oranges, partial least squares.
Background Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes. Results In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training. Conclusions Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research.
Blood transfusion is a critical medical treatment, which is performed to save patients’ lives. Chylous blood had high fats. The transfusion of chylous blood into a patient can cause the blockage of micro-vessels. Most blood collection stations are not equipped with the equipment for the detection of chylous blood, and the detection is usually performed with direct observation through the human naked eye, which is prone to certain human errors. Only a few large blood collection stations use the equipment for the detection of chylous blood. In this study, plasma hyperspectral data were collected to detect and identify chylous plasma. The data were preprocessed using the multiple scattering correction (MSC) method and then classified using four classification algorithms, including random forest (RF), K-nearest neighbor KNN), Perceptron, and stochastic gradient descent (SGD) algorithms. First, the healthy and chylous plasma samples were classified into simple dichotomies. The best algorithm was identified by comparing the results of classification algorithms. The results showed that the random forest algorithm-based classification model had the best effect.Then, the chylous plasma was subdivided into different degrees of chylous plasma, which were less separable.A random forest algorithm-based plasma chylous degree detection model was established. Finally, 10 important spectral bands, including 1192.45 nm, 1182.9 nm, 946.98 nm, 1202.01 nm, 1080.93 nm, 1278.41 nm, 1237.03 nm, 991.65 nm, 1020.35 nm, and 1697.8 nm, were selected by band selection. After adjusting the parameters to optimize the model, the prediction accuracy of the whole band was 0.89. This study suggested that hyperspectral technology could identify chylous plasma and could be used to improve its detection efficiency in biomedicine, blood donation centers, human function tests, and other aspects. Filling the gap between machine learning and hyperspectral technology.To provide a new method for the diagnosis of chylous plasma.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.