Xinjiang Production and Construction Corps

IntroductionPopulus pruinosa is the key foundation tree species in desert riparian forests in arid areas of northwestern China. Timely and accurate monitoring of the physiological status of P. pruinosa is crucial for restoring the damaged ecosystem. Anthocyanins are one of the important physiological indicators that reflect the environmental adaptability of P. pruinosa under stress. Existing studies have extensively applied hyperspectral technology for the quantitative prediction of crop leaf pigments. However, research on hyperspectral prediction of anthocyanin concentration in woody halophytes is still lacking, particularly in the integration of spectral preprocessing, species-specific vegetation index construction, and machine learning modeling.MethodsIn this study, the hyperspectral technology was used to estimate the anthocyanin concentration of P. pruinosa leaves collected in five months (June - October) under five groundwater depth conditions (0-2, 2-4, 4-6, 6-8, and 8-10 m). Based on first-order (FD) and second-order (SD) derivative processing, competitive adaptive reweighted sampling (CARS), https://xueshu.baidu.com/usercenter/paper/show?paperid=bea4d6371f19161f21aac22941cc4408&site=xueshu_se shuffled frog leaping algorithm (SFLA), and recursive feature elimination with cross-validation (RFECV) were used to extract spectral features of P. pruinosa leaves to construct the anthocyanin reflectance index, composite index, difference vegetation index, and normalized anthocyanin reflectance index. After that, the top 10 sets of data with high correlation with anthocyanin concentration were selected from each vegetation index to form a total data set (40 sets in total) for modeling. Twelve models were constructed using support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) methods.ResultsThe FD and SD derivative transformations of the spectral reflectance significantly enhanced the correlation with anthocyanin concentration. The feature extraction methods SFLA and RFECV were superior in extracting the bands highly related to anthocyanin concentration, and the vegetation indices constructed based on these two methods had a high correlation with anthocyanin concentration in the red and near-infrared regions. The optimal prediction model was FD-SFLA-SVM (R2 = 0.852, RMSE = 86.851 mg m-2, RPD = 2.596).DiscussionUnlike existing vegetation index-based studies, the research develops a systematic approach to construct vegetation indices and models for estimating the anthocyanin concentration in the woody halophyte P. pruinose in deserts. The research will provide technical support for non-destructive monitoring of the physiological status of P. pruinosa, and also contribute to the restoration of desert riparian ecosystems.

Surge waves generated by landslides can lead to catastrophic consequences, including severe economic losses and lose of life. This study invenstigates the surge waves induced by the Mogangling landslide triggered by the 1786 Moxi earthquake through a combination of numerical simulations and field surveys. A detailed field invenstigation was conducted to characterize the engineering geological features of the Mogangling landslide. Based on the point cloud data, a three-dimensional (3D) landslide model was constructed and used for numerical simulations. The landslide motion was simulated using a granular flow model, while wave propagation was modeled using the renormalisation group (RNG) turbulence model. These two models were coupled to analyze the genaration and evolution of the landslide-induced surge waves. The simulation results indicate that the peak sliding velocity of the landslide approached 20 m/s. As the landslide mass entered the Dadu River, a large landlside-dam formed, temporarily blocking the river and genrating intense surge waves. The peak water velocity increased to 30 m/s, and the maximum wave height reached 81.78 m, which is consistent with historical record-roughly 80 m. The surge waves propagated in a circular pattern toward the opposing riverbank. The findings provide insights for the risk assessment of landslide-induced surge waves.

Distributed Energy Resource (DER)-integrated power grids are vulnerable to cascading effects under concurrent cyber–physical attacks, where even minor disruptions in system states accumulate and amplify over time, leading to significant system failures. Traditional static risk assessment methods are insufficient for modeling these time-varying, dynamic scenarios, particularly in the context of concurrent attacks. This paper presents a dynamic risk assessment framework leveraging time-synchronized co-simulation, which integrates power system and communication network simulations within a unified time framework. Cyber-attack actions in the communication layer are mapped to corresponding physical disturbances in the distribution network, including voltage, frequency, and power variations. Using the resulting system state evolution trajectories, a Markov Decision Process (MDP)-based state transition tree captures the progression of system risk under concurrent attacks. This framework accounts for cumulative risk across different attack paths and identifies critical nodes and high-risk propagation paths within the network. By incorporating a concurrent event detector into the MDP model, the method quantifies evolving risk dynamics, overcoming the limitations of traditional static methods. Case studies on the IEEE 13-node test feeder and IEEE 14-bus system demonstrate that concurrent attacks result in a security risk metric 2.3 times higher than single-point attacks, validating the effectiveness of the proposed approach in identifying vulnerable nodes whose compromise could lead to cascading failures, supporting the risk-aware prioritization of defensive resources.

High-grade serous ovarian carcinoma (HGSOC) is a gynaecological malignancy that is often fatal. Poor prognosis of HGSOC patients is primarily attributed to concealed initial symptoms, diagnostic challenges, postsurgical recurrence , and chemoresistance. Distinct programmed cell death (PCD) patterns play a pivotal role in tumour progression, serving as valuable predictors for postoperative intervention outcomes in HGSOC. Additionally, they provide insights into HGSOC’s pathogenesis and the exploration of immunomodulatory therapeutic mechanisms. Transcriptome and clinical data were collected from TCGA-OV and the GSE26193 databases. We constructed an ovarian carcinoma death score intervention model using eight genes and machine learning algorithms based on 13 PCD modes (apoptosis, necroptosis, pyroptosis, cuproptosis, ferroptosis,entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy, alkaliptosis, oxeiptosis, and disulfidptosis). Three molecular subtypes of HGSOC with different biological processes were identified using unsupervised clustering models. A nomogram was constructed by combining the cell death index (CDI) with clinical features, which exhibited high predictive performance. The correlation between CDI and immune checkpoint genes, components within the tumour microenvironment, and drug therapy sensitivity was analysed. After multiple dataset validation, the prognosis of HGSOC patients with high CDI was relatively poor. CDI and immune checkpoint genes were related to components of the tumour microenvironment. Patients with HGSOC and high CDI may have resistance to standard adjuvant therapy; therefore, targeting these genes could be a potential therapeutic strategy. Finally, we found that our model had better predictive ability than published models. We conducted a comprehensive analysis of 13 PCD patterns and established a novel CDI model, which can evaluate the prognosis of HGSOC and provide a theoretical basis for its clinical treatment.

To clarify the ecological characteristics of herbaceous plants on parabolic dunes in the Ebinur Lake Basin and to support regional ecological conservation, this study focused on herbaceous species with an importance value (IV) > 1%. Standard ecological indices and analytical approaches were used for assessment. The results showed the following. (1) A total of 12 herbaceous species were recorded, belonging to 10 genera and 7 families. The ranking of niche breadth showed no clear qualitative association with IV. (2) Niche overlap (Oik) among species was generally high. Fifty-eight species pairs had Oik > 0.60. Most herbaceous species differed only slightly in their environmental and resource requirements, indicating interspecific competition. (3) Overall species associations were significantly positive. The ratios of positive to negative associations were 12.2 based on the χ2 test, the interspecific association coefficient (AC), and Spearman rank correlation. Species were strongly associated. The community was at the mid-successional stage. (4) Diversity indices followed a normal distribution. The community showed moderate richness and evenness, with pronounced dominance. For future conservation, species with similar ecological preferences and biological traits should be selected. Management should adjust and optimize species composition to improve resource use efficiency and enhance community stability.