Aqueous solubility is one of the most important physicochemical properties in drug discovery. At present, the prediction of aqueous solubility of compounds is still a challenging problem. Machine learning has shown great potential in solubility prediction. Most machine learning models largely rely on the setting of hyperparameters, and their performance can be improved by setting the hyperparameters in a better way. In this paper, we used MACCS fingerprints to represent the structural features and optimized the hyperparameters of the light gradient boosting machine (LightGBM) with the cuckoo search algorithm (CS). Based on the above representation and optimization, the CS-LightGBM model was established to predict the aqueous solubility of 2446 organic compounds and the obtained prediction results were compared with those obtained with the other six different machine learning models (RF, GBDT, XGBoost, LightGBM, SVR, and BO-LightGBM). The comparison results showed that the CS-LightGBM model had a better prediction performance than the other six different models. RMSE, MAE, and R ² of the CS-LightGBM model were, respectively, 0.7785, 0.5117, and 0.8575. In addition, this model has good scalability and can be used to solve solubility prediction problems in other fields such as solvent selection and drug screening.

A photothermal fiber sensor based on a microfiber knot resonator (MKR) and the Vernier effect is proposed and demonstrated. An MXene Ti₃C₂T_x nanosheet was deposited onto the ring of an MKR using an optical deposition method to prepare photothermal devices. An MXeneMKR 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 MXeneMKR were tested. Ti₃C₂T_x was applied to a photothermal fiber sensor for the first time. The experimental results showed that the modulation efficiency of the MXeneMKR 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.

The goal of this study was to model the total leaf chlorophyll content (LCCtot) 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 LCCtot were based on Datasets_1.67 for LCCtot prediction. Several prediction models of LCCtot 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 (R2), the root mean square error (RMSE) and the residual predictive deviation (RPD) were used to evaluate the reliability and accuracy of the predicted LCCtot 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 LCCtot 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 LCCtot 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 LCCtot estimation; with the number of principal components (Pcs) = 5, this model provided high values of the R2 of calibration (C-R2) = 0.92 and the R2 of validation (V-R2) = 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.

Traditional hyperspectral image semantic segmentation algorithms can not fully utilize the spatial information or realize efficient segmentation with less sample data. In order to solve the above problems, a U-shaped hyperspectral semantic segmentation model (DCCaps-UNet) based on the depthwise separable and conditional convolution capsule network was proposed in this study. The whole network is an encoding–decoding structure. In the encoding part, image features are firstly fully extracted and fused. In the decoding part, images are then reconstructed by upsampling. In the encoding part, a dilated convolutional capsule block is proposed to fully acquire spatial information and deep features and reduce the calculation cost of dynamic routes using a conditional sliding window. A depthwise separable block is constructed to replace the common convolution layer in the traditional capsule network and efficiently reduce network parameters. After principal component analysis (PCA) dimension reduction and patch preprocessing, the proposed model was experimentally tested with Indian Pines and Pavia University public hyperspectral image datasets. The obtained segmentation results of various ground objects were analyzed and compared with those obtained with other semantic segmentation models. The proposed model performed better than other semantic segmentation methods and achieved higher segmentation accuracy with the same samples. Dice coefficients reached 0.9989 and 0.9999. The OA value can reach 99.92% and 100%, respectively, thus, verifying the effectiveness of the proposed model.

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.