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Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

The Zigui–Badong section of the Three Gorges Reservoir area is used as the research area in this study to research the impact of unbalanced sample sets on Landslide Susceptibility Mapping (LSM) and determine the sample ratio interval with the best performance for different models. We employ 12 LSM factors, five training sample sets with different sample ratios (1:1, 1:2, 1:4, 1:8, and 1:16), and C5.0, Support Vector Machine (SVM), Logistic Regression (LR), and one-dimensional Convolution Neural Network (CNN) models are used to obtain landslide susceptibility index and landslide susceptibility zoning in the study area, respectively. The prediction performance of the model is evaluated by the receiver operating characteristic curve area under the curve value, five statistical methods, and specific category precision. The results show that the CNN, SVM, and LR models in the sample ratio of 1:2 achieve better performance than on the balanced sample set, which indicates the importance of the unbalanced sample set in training the LSM modeling. The C5.0 model is always in a state of overfitting in this study and needs to be further studied. The conclusions put forward in this study help improve the scientificity and reliability of LSM.

Robotic total stations have been widely used in continuous automatic monitoring of dam deformations. In this regard, monitoring accuracy is an important factor affecting deformation analysis. First the displacements calculation methods for dam deformation monitoring with total stations are presented, and the corresponding mean square error formulas are derived. Then for errors caused by atmospheric refraction, two correction methods are described. Simulations were conducted to compare the displacement accuracy calculated through different methods. It indicated that the difference between polar coordinate method and forward intersection is less than 0.5mm within around 400m’ monitoring range, and in such cases, the polar coordinate method is preferred, as only one total station is required. Refraction correction tests with observations from two dams demonstrated that both correction methods could effectively enhance the monitoring accuracy. For observation correction, correction through the closest reference point achieves better correction results.

Robotic total stations are multi-sensor integrated instruments widely used in displacement monitoring. The principles of polar coordinate or forward intersection systems are usually utilized for calculating monitoring results. However, the polar coordinate method lacks redundant observations, leading to unreliable results sometimes. Forward intersection requires two instruments for automated monitoring, doubling the cost. In this regard, this paper proposes a novel automated displacement monitoring method using the robotic total station assisted by a fixed-length track. By setting up two station points at both ends of a fixed-length track, the robotic total station is driven to move back and forth on the track and obtain observations at both station points. Then, automated monitoring based on the principle of forward intersection with a single robotic total station is achieved. Simulation and feasibility tests show that the overall accuracy of forward intersection is better than that of polar coordinate system as the monitoring distance increases. At the same time, regardless of tracking a prism or not, the robotic total station is able to automatically find and aim at the targets when moving between station points on the track. Further practical tests show that the reliability of the monitoring results of the proposed method is superior to the polar coordinate method, which provides new ideas for ensuring the reliability of results while reducing cost in actual monitoring tasks.

This study introduces four rock–soil characteristics factors, that is, Lithology, Rock Structure, Rock Infiltration, and Rock Weathering, which based on the properties of rock formations, to predict Landslide Susceptibility Mapping (LSM) in Three Gorges Reservoir Area from Zigui to Badong. Logistic regression, artificial neural network, support vector machine is used in LSM modeling. The study consists of three main steps. In the first step, these four factors are combined with the 11 basic factors to form different factor combinations. The second step randomly selects training (70% of the total) and validation (30%) datasets out of grid cells corresponding to landslide and non-landslide locations in the study area. The final step constructs the LSM models to obtain different landslide susceptibility index maps and landslide susceptibility zoning maps. The specific category precision, receiver operating characteristic curve, and 5 other statistical evaluation methods are used for quantitative evaluations. The evaluation results show that, in most cases, the result based on Rock Structure are better than the result obtained by traditional method based on Lithology, have the best performance. To further study the influence of rock–soil characteristic factors on the LSM, these four factors are divided into “Intrinsic attribute factors” and “External participation factors” in accordance with the participation of external factors, to generate the LSMs. The evaluation results show that the result based on Intrinsic attribute factors are better than the result based on External participation factors, indicating the significance of Intrinsic attribute factors in LSM. The method proposed in this study can effectively improve the scientificity, accuracy, and validity of LSM.

The relationship between gestational age throughout the entire term period (37–41 weeks) and the occurrence of respiratory illness remains not fully understood. This population-based cohort study used birth data submitted by 50 states and the District of Columbia to the National Vital Statistics System database in USA to assess the association between gestational age and the incidence of neonatal respiratory failure (NRF) in term infants. Term singleton infants born from January 2021 to December 2022 were included in the analyses. The exposure variable of interest was the gestational age at birth. Primary outcome was NRF, defined by the need for assisted ventilation for over 6 h within the first days of life. Adjusted Odds ratios (aORs) compared NRF risk across gestational ages, with 39 weeks as the reference, adjusted for maternal and perinatal factors. In 4,978,703 term infants, NRF incidence at 37, 38, 39, 40, and 41 weeks was 1.7%, 0.9%, 0.6%, 0.7%, 0.8%, respectively. Compared to 39 weeks, the risk of NRF was higher at 37 weeks (aOR 2.08; 95% CI, 2.02–2.14), 38 weeks (aOR 1.27; 95% CI, 1.23–1.30), 40 weeks (aOR 1.18; 95% CI, 1.15–1.22), and 41 weeks (aOR 1.30; 95% CI, 1.25–1.35). Subgroup analyses confirmed similar trends across sex (male: aOR 2.07 at 37 weeks, 1.37 at 38 weeks, 1.12 at 40 weeks, 1.39 at 41 weeks; female: aOR 2.00 at 37 weeks, 1.34 at 38 weeks, 1.12 at 40 weeks, 1.40 at 41 weeks), delivery mode (vaginal delivery: aOR 1.96 at 37 weeks, 1.33 at 38 weeks, 1.10 at 40 weeks, 1.34 at 41 weeks; cesarean section: aOR 2.10 at 37 weeks, 1.37 at 38 weeks, 1.15 at 40 weeks, 1.48 at 41 weeks), and in infants born via elective cesarean section (aOR 2.51 at 37 weeks, 1.37 at 38 weeks, 1.09 at 40 weeks, 1.35 at 41 weeks). These findings highlight associations between gestational age within the term range and NRF risk, suggesting that careful consideration of delivery timing may be important for reducing respiratory complications in term infants.

To construct and validate a prediction model of neurological rehabilitation for patients with osteoporotic spinal fracture combined with spinal cord injury based on the expression of serum neurotrophic factor (Neuritin) and transforming growth factor β1 (TGF-β1 ), so as to provide basis for accurate clinical rehabilitation. A total of 189 patients with osteoporotic spinal fracture combined with spinal cord injury who were admitted to our hospital from January 2022 to September 2024 were included in the study. A random 7:3 score was used as the training set ( n  = 132) and validation set ( n  = 57). In the training set, independent influencing factors were screened by univariate and multivariate Logistic regression analysis, and a Nomogram model was constructed. The model performance was evaluated by using the consistency index (C-index), calibration curve, and receiver operating characteristic curve (ROC), and was verified in the validation set. There was no significant difference in baseline data between the training set and the validation set ( P  > 0.05). Multivariate logistic analysis showed that serum Neuritin (ng/mL, OR = 1.285), TGF-β1 (ng/mL, OR = 0.979), vertebral compression degree (%) (OR = 0.960), spinal canal space-occupying rate (%) (OR = 0.879), 25-hydroxyvitamin D (ng/mL, OR = 1.103), and alkaline phosphatase (U/L, OR = 0.973) were the independent factors for poor neurological rehabilitation (all P  < 0.05). The C-index for the nomogram model was 0.842 and 0.821 in the training and validation sets, respectively, the calibration curve fitted well, and the area under the ROC curve (AUC) was 0.850 (95% CI: 0.75–0.95) and 0.826 (95% CI: 0.65-1.00), respectively. The Nomogram model constructed based on serum levels of Neuritin and TGF-β1 can effectively predict the neurological rehabilitation effects of patients with osteoporotic spinal fracture combined with spinal cord injury, and has potential clinical application value.

The reservoir water level fluctuation zone (WLFZ) is a new and fragile ecosystem that is gaining attention with the construction of large and medium-sized hydropower plants. Compared to the natural riparian zone, it has a greater drop in water level, longer inundation time, more intense impact from alternating wet and dry conditions, and a wider impact on ecological security. The Jinsha River basin is located in the upper reaches of the Yangtze River in China, and several world-class large-scale hydropower projects with dam heights over 100 m have been built, forming a large area of reservoir WLFZ, however, due to the short time since their construction, there are few related studies. In this paper, fixed sample plots were set up in the typical WLFZs of each large reservoir in the Jinsha River basin. In response to the problem of the precipitous terrain and poor accessibility of the Jinsha River basin, a combination of small UAV surveys and field research in July 2020 was used to draw vegetation cover maps and extract topographic data for each site, and quantitatively analyse the community composition, dominant species types, area, coverage, spatial distribution patterns and environmental factors of tolerant vegetation using spatial superposition analysis, neural network models, landscape pattern indices and typical correlation analysis. The results showed that the original drought-tolerant vegetation in the arid river valley WLFZ has evolved into amphibious herbaceous vegetation, with trees and shrubs disappearing and species composition tending to be simpler. 44 species of plants, mainly in the Asteraceae and Gramineae families, were extant, 61% of which were also reported in the Three Gorges Reservoir WLFZ. The water level variation showed convergence in the natural screening process of suitable species in the WLFZ. Moreover, even in the dry valley WLFZs, flood stress showed a more significant filtering effect on vegetation species than drought stress. The vegetation in the WLFZ showed an obvious band-like aggregated distribution along the water level elevation gradient, and the vegetation coverage along the flooding gradient is as follows: upper part of the WLFZ >> middle part > lower part, and mainly concentrated in the gentle area with slope less than 35°. Flooding stress, drought stress and soil substrate deficiency were the main limiting factors for vegetation recovery in the WLFZ. The vegetation restoration of the WLFZ should be adapted to local conditions, and the dominant role of native species should be emphasized. At the early stage of the restoration of the WLFZ, native species should be selected for artificial planting to accelerate the formation of vegetation cover, and gradually advance downwards along the gradient of water level elevation, while for areas of the WLFZ with slopes greater than 35° and large topographic relief, biological engineering measures should be used to help plant establishment, and after a certain stable cover has been formed, natural restoration should be the main focus.

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

More than 30 Cass. (Asteraceae) species have long been used in folk medicine in China. Morphological features and common DNA regions are both not ideal to identify species. As some species contain pyrrolizidine alkaloids, which are hazardous to human and animal health and are involved in metabolic toxification in the liver, it is important to find a better way to distinguish these species. Here, we report complete chloroplast (CP) genomes of six species, , , , , , and , obtained through high-throughput Illumina sequencing technology. These CP genomes showed typical circular tetramerous structure and their sizes range from 151,118 to 151,253 bp. The GC content of each CP genome is 37.5%. Every CP genome contains 134 genes, including 87 protein-coding genes, 37 tRNA genes, eight rRNA genes, and two pseudogenes ( and ). From the mVISTA, there were no potential coding or non-coding regions to distinguish these six species, but the maximum likelihood tree of the six species and other related species showed that the whole CP genome can be used as a super-barcode to identify these six species. This study provides invaluable data for species identification, allowing for future studies on phylogenetic evolution and safe medical applications of .