Glacier meltwater is an indispensable water supply for billions of people living in the catchments of major Asian rivers. However, the role of glaciers on river runoff regulation is seldom investigated due to the lack of annual glacier mass balance observation. In this study, we employed an albedo-based model with a daily land surface albedo dataset to derive the annual glacier mass balance over the Tuotuo River Basin (TRB). During 2000–2022, an annual glacier mass balance range of −0.89 ± 0.08 to 0.11 ± 0.11 m w.e. was estimated. By comparing with river runoff records from the hydrometric station, the contribution of glacier mass change to river runoff was calculated to be 0.00–31.14% for the studied period, with a mean value of 9.97%. Moreover, we found that the mean contribution in drought years is 20.07%, which is approximately five times that in wet years (4.30%) and twice that in average years (9.49%). Therefore, our results verify that mountain glaciers act as a significant buffer against drought in the TRB, at least during the 2000–2022 period.

Glacier mass balance estimation is important for understanding glacier responses to climate change and for assessing mountain water resources. Data-driven methods are widely used, but their cross-regional transferability remains unclear, especially in High Mountain Asia (HMA), where observations are limited. This study develops a unified framework to compare 16 machine learning and deep learning models across the European Alps and HMA. A degree-day-based monthly decomposition scheme is used to generate physically constrained monthly mass balance estimates. These are used as intermediate supervision signals. All models are trained at the monthly scale, and the outputs are aggregated to annual values for evaluation against observations. In transfer experiments, models are trained on Alpine data and tested in HMA. In joint-training experiments, different proportions of HMA samples are gradually added to the training set to assess the role of target-region information. Results show that machine learning models outperform deep learning models in cross-regional settings. Random Forest and K-Nearest Neighbors remain relatively stable under limited HMA data, while deep learning models are more sensitive to distribution shifts. Adding a small amount of HMA data improves annual prediction performance, highlighting the value of region-specific information. Overall, this study provides guidance for modeling glacier mass balance in data-scarce regions.

Modern grid digitalization connects communication networks, monitoring terminals, service platforms, security devices, and operational data sources. Intrusion detection in this setting requires correlating heterogeneous security data with grid-side contextual evidence. To address class imbalance and cross-domain heterogeneity, this study proposes a heterogeneous feature group integration framework for intrusion detection with grid cybersecurity data. Four semantic feature subspaces are constructed symmetrically: network behaviour, power operation context, zone-derived communication/event topology, and system operation state, ensuring equal structural footing for subsequent modality-specific encoding. Transformer-based encoders model temporal dependencies in network, physical, and system state modalities, while a graph neural network encodes topology-related structural information. The resulting embeddings are integrated by a late fusion classifier for multiclass attack identification; the fusion process treats each feature group symmetrically at the decision level, without imposing a priori dominance among modalities. In the main run, the full model achieves an accuracy of 0.944, a macro F1 score of 0.891, a weighted F1 score of 0.937, a macro precision of 0.929, and a macro recall of 0.878. The corresponding balanced accuracy is 0.878, and the multiclass MCC is 0.924. Class-wise results show reliable performance on Benign, Scan, WebAtk, DDoS, DoS, and Backdoor classes, while Ransomware remains difficult and is frequently confused with WebAtk. Specifically, the Ransomware recall is 0.27, with most errors assigned to WebAtk. Modality analysis further indicates that modality contribution is class dependent: some feature groups have limited standalone discriminative power but provide complementary evidence after fusion. This finding highlights an inherent asymmetry in class-wise utility, which we counterbalance by employing both macro and weighted metrics, offering a symmetric evaluation lens that accounts for both minority and majority classes. These results show that grid-oriented intrusion detection benefits from decision-level integration of heterogeneous feature groups and imbalance-aware evaluation, where symmetric treatment of feature subspaces and evaluation perspectives jointly enhances robustness.

Under the background of global climate warming, glaciers in the Three-River Headwaters Region, as a crucial component of the “Asian Water Tower,” exert profound influences on regional water resource security and ecological stability through their mass balance variations. Due to the scarcity of in situ observations caused by the harsh high-altitude environment, long-term monitoring based on remote sensing techniques is urgently required. In this study, the geodetic method was employed, using the SRTM-C DEM acquired in 2000 as the reference, and recent glacier surface DEMs were generated from high-resolution ZiYuan-3 tri-stereo imagery obtained during 2024–2025. Through refined DEM co-registration, differencing, and systematic error corrections, the glacier mass balance in the Three-River Headwaters Region from 2000 to 2025 was systematically estimated. The results indicate that the glaciers in the study area exhibited an overall negative mass balance during the study period, with significant spatial heterogeneity. Among the sub-regions, the Lancang River source region experienced the most pronounced mass loss (−0.70 ± 0.07 m w.e. yr⁻¹), whereas the Yellow River source region showed the lowest mass loss (−0.37 ± 0.09 m w.e. yr⁻¹). Compared with earlier studies, glacier mass loss has accelerated in recent years and is closely associated with regional climatic characteristics. This study provides a scientific basis for understanding glacier changes and their hydrological and ecological impacts in the Three-River Headwaters Region.