Understanding the flow process of cemented tailings backfill (CTB) is important for successful pumping into underground stopes. This study examines the effects of solid content (SC), cement/tailings (c/t) ratio, and curing time (CT) on rheological and mechanical properties of CTB mixes. The slurry concentration of the mixes was 65, 67, and 69 wt. %, with c/t ratios ranging from 1:4 to 1:20. Unconfined compressive strength (UCS) tests were performed on hardened CTB mixes after curing 3, 7, and 28 days. The rheological properties of CTB slurries are mainly related to SC. The yield stress and viscosity of fresh mixes increase with increasing SC, but the pipeline resistance loss (PRL) also increases with increasing SC. According to the analysis of variance, the SC and flow rate are the most significant parameters which greatly affect the PRL performance. The c/t and CT parameters are the most significant parameters for affecting the shrinkage rate. The findings offer a reference for theoretical optimization for mine filling systems of similar type.

Using solid wastes (SWs) as backfilling material to fill underground mined-out areas (UMOAs) solved the environmental problems caused by SWs and reduced the backfilling cost. In this study, fly ash (FA), gypsum and steel slag (SS) were used to prepare cement-based composites (CBC). The uniaxial compression, computed tomography (CT) and scanning electron microscope (SEM) laboratory experiments were conducted to explore the macro and micromechanical properties of CBC. The findings showed that the uniaxial compressive strength (UCS) of CBC with a curing time of 7 d could reach 6.54 MPa. The increase of SS content reduced the UCS of CBC, while the gypsum and FA content could increase the UCS of CBC. Microscopic studies have shown that the SS particles in CBC have noticeable sedimentation, and the increase of SS content causes the failure mode of CBC from tensile to tensile-shear. These research results can provide a scientific reference for the preparation of backfilling materials.

As mining depth increases, the backfill mining method is more and more widely used in underground mines. The dynamic load generated by the blasting can affect the stability of the cemented tailings backfill (CTB). The CTB samples were prepared to conduct a test of the split Hopkinson pressure bar (SHPB) to investigate the dynamic disturbance of CTB. The present paper discusses dynamical mechanics, energy dissipation, and microstructure analysis of CTB. Micro-computer tomography (micro-CT) scanning of CTB samples after the SHPB test was performed to analyze the evolution of internal cracks. The experimental results showed that when the average strain rate (ASR) increased from 30 to 98 s⁻¹, the dynamic uniaxial compression strength (DUCS) of the CTB showed a trend of first increasing and decreasing with the increase in ASR. The dynamic stress–strain pre-peak curve of CTB directly enters the linear elastic stage. As ASR increases, the absorbed energy of the CTB shows a trend of first increasing and then decreasing. Moreover, according to the micro-CT scanning results, the crack area of CTB accounts for about 16% of the sample near the incident bar and about 1% near the transmitted bar. The crack area ratio is exponentially related to the specimen height. These findings can provide reasonable dynamical CTB strength data selection for underground pillar mining.

Automatic identification of Alzheimer's Disease (AD) through magnetic resonance imaging (MRI) data can effectively assist to doctors diagnose and treat Alzheimer's. Current methods improve the accuracy of AD recognition, but they are insufficient to address the challenge of small interclass and large intraclass differences. Some studies attempt to embed patch-level structure in neural networks which enhance pathologic details, but the enormous size and time complexity render these methods unfavorable. Furthermore, several self-attention mechanisms fail to provide contextual information to represent discriminative regions, which limits the performance of these classifiers. In addition, the current loss function is adversely affected by outliers of class imbalance and may fall into local optimal values. Therefore, we propose a 3D Residual RepVGG Attention network (ResRepANet) stacked with several lightweight blocks to identify the MRI of brain disease, which can also trade off accuracy and flexibility. Specifically, we propose a Non-local Context Spatial Attention block (NCSA) and embed it in our proposed ResRepANet, which aggregates global contextual information in spatial features to improve semantic relevance in discriminative regions. In addition, in order to reduce the influence of outliers, we propose a Gradient Density Multiple-weighting Mechanism (GDMM) to automatically adjust the weights of each MRI image via a normalizing gradient norm. Experiments are conducted on datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle Flagship Study of Aging (AIBL). Experiments on both datasets show that the accuracy, sensitivity, specificity, and Area Under the Curve are consistently better than for state-of-the-art methods.

Engineering unloading activities during deep mineral resource extraction subject the backfill materials to complex true triaxial stress conditions, where their mechanical behavior and damage mechanisms are critical to stope stability. In this article, a true triaxial testing system was employed to conduct unloading tests under different initial intermediate principal stress (σ₂) conditions, aiming to elucidate the influence mechanism of σ₂ on strength, deformation, failure modes, and acoustic emission (AE) characteristics of the backfill, and to establish a corresponding damage constitutive model. The results demonstrate that the σ₂ governs the mechanical response and failure mode of the filling material. Within the tested range, σ₂ nonlinearly enhances both the peak stress, indicating improved load-bearing. As σ₂ increases, acoustic emission activity changes from intermittent to continuous high-intensity ringing counts. The transition from brittle to ductile fracture. Model predictions showed high agreement with experimental data, validating its applicability. This study provides a critical theoretical foundation and modeling framework for assessing the stability of backfill structures under deep well mining conditions and guiding engineering design.