Search Results (9,073)

Underground mine object detection is challenged by low illumination, blur, dust scattering, and repetitive tunnel clutter, which jointly corrupt backbone features, entangle DETR queries, and weaken localization for small objects. Existing enhancement-based and detector-internal methods do not explicitly propagate degradation reliability across features, decoder queries, and box refinement. We propose Spectral-DETR, a detector-internal reliability framework built on RF-DETR. Its central design is a cross-stage reliability pathway that connects Degradation-Aware Frequency Decomposition (DAFD), Degradation-Adaptive Query Contrastive Denoising (DQCD), and Salience-Calibrated Uncertainty with Learned Uncertainty Estimation (SCU+LUE). On Mine-Objects (14 classes, 3081 images), Spectral-DETR achieves an average precision of 0.917 at an intersection-over-union threshold of 0.5 and 0.493 when averaged over thresholds from 0.5 to 0.95, exceeding YOLOv9m by 1.6 and 0.8 percentage points, respectively, under the dataset-specific evaluation protocol. In controlled RF-DETR validation, the three reliability stages improve these two measures from 0.883 to 0.913 and from 0.472 to 0.486, respectively. Spectral-DETR obtains corresponding values of 0.848 and 0.571 on ExDark and 0.973 and 0.495 on ScienceDB. DQCD and SCU remain training-only losses with no inference cost. Full article

In recent years, the agentive role of youth groups in urban regeneration has garnered increasing academic attention. As vital carriers of urban cultural heritage, the ways in which historic districts can leverage youth dynamics to achieve vitality revitalization have emerged as a key research issue. This study selects the Fayuan Temple Historic and Cultural District in Beijing as an empirical case, constructs a public space evaluation system grounded in the concept of “youth-friendliness,” adopts a mixed-method approach integrating field surveys, questionnaire administration, and multi-source data mining, and employs Importance–Performance Analysis (IPA) modeling for deconstruction. Based on behavioral characteristic differences, youth within the district are categorized into two groups: “resident/local youth” and “transient visiting youth.” The comprehensive experience quality scores were 3.559 (Fair) for resident and local youth and 3.85 (Fair) for temporary visitors, with cultural space scoring highest for both groups. Resident youth demonstrate stronger demands for renewal concerning the completeness of cultural facilities, diversity of commercial formats, and street navigability. Transient visiting youth, by contrast, exhibit greater concern for eight factors: pedestrian safety and comfort, static traffic order, diversity of commercial formats, quality of consumption environment, richness of social venues, pleasantness of spatial scale, landscape interactivity, and street navigability. These findings indicate that youth groups with varying interactive relationships with historic districts possess significantly heterogeneous needs regarding public space utilization, and their expectations for historic district regeneration also manifest differentiated characteristics. This provides a scientific basis for formulating precise, multi-layered strategies for district renewal. Full article

Rainfall-induced seepage instability is a major concern for internal waste dump slopes in cold-region open-pit coal mines, where slope performance is influenced by groundwater conditions, moisture state, and seasonal temperature variations. This study investigates the internal waste dump slope of the Chaoyang open-pit coal mine and evaluates its seepage and stability responses under prescribed moisture and temperature states before and after rainfall, together with the effectiveness of drainage control. Soil specimens with moisture contents of 14%, 17.6% (natural), 23%, and 26% were tested at ambient temperature, −5 °C, and −15 °C by uniaxial compression and direct shear tests. The mechanical parameters measured under the prescribed moisture and temperature states were assigned to a GTS NX seepage–stability model. Twenty-four parametric cases, comprising four moisture contents, three temperature states, and pre- and post-rainfall conditions, were evaluated using the strength-reduction method, and an HDPE perforated drainage scheme was subsequently assessed. Under the ambient-temperature parameter state, increasing specimen moisture content from 14% to 26% reduced the pre-rainfall factor of safety from 1.41 to 1.18 and the post-rainfall value from 1.38 to 1.17. Parameter sets obtained from low-temperature-conditioned specimens produced higher calculated factors of safety; however, these cases represent prescribed mechanical states rather than the actual winter condition of the full-scale slope. Under the idealized drainage boundary, the pre- and post-rainfall factors of safety increased from 1.22 and 1.18 to 1.40 and 1.39, respectively. The results demonstrate the relative effects of laboratory-derived mechanical parameters, rainfall-induced seepage, and idealized drainage under the prescribed scenarios. Full article

Preconcentration before grinding is important for reducing unnecessary downstream processing and improving ore utilization. Dual-energy X-ray transmission imaging provides paired responses of ore particles under different energy levels, which can be used for particle-level classification. However, adjacent categories, such as waste rock and low-grade copper ore, may exhibit similar transmission appearances, and discriminative cues may be distributed across both local attenuation details and global transmission patterns. In this study, we propose a dual-energy X-ray image classification method, named the Difference-Guided Cross-Level Feature Fusion Network (DGCF-Net), for three-class copper ore classification. Waste rock and copper ore samples from the Dexing Copper Mine were used to construct a three-class dual-energy X-ray image dataset. DGCF-Net incorporates response-difference cues from paired low- and high-energy images and combines local and global feature representations for ore-particle classification. Experimental results on the constructed dataset show that the proposed method achieved an Overall Accuracy of 0.9570, a Macro-F1 of 0.9664, and an AUC of 0.9953, with 3.6424 M parameters. These results indicate that the proposed method provides effective classification performance on the current dataset, particularly for categories with relatively similar image responses, while its broader practical applicability requires further validation under more realistic operating conditions. Full article

Spontaneous combustion in fire-affected coal seams can degrade coal quality, alter rock mechanical parameters and reduce mining profitability. Traditional pit limit optimization methods ignore coal fire-induced quality degradation, ignore the coupling effect of economic fluctuation and slope stability, and lack quantitative optimization for fire-affected open-pit mines. Here, we optimize loss-reducing mining boundaries for the southern fire-affected highwall in the first mining district of the Dananhu No. 2 Mine, Hami, Xinjiang. The aim is to move beyond binary decisions that either sterilize or fully extract fire-affected reserves. We instead integrate economic return, slope stability and coal-price uncertainty into a single boundary-optimization framework. First, we established a three-dimensional Cartesian coordinate system for the study area. We then modeled and fitted the coal-seam roof and floor using MATLAB-based multiple integration, reducing edge errors in solid surfaces. Laboratory analyses of borehole coal samples defined how calorific value varied with advance distance. These data were used to derive the coal-quality curve. Net mining profit was then formulated as the objective function, replacing the conventional stripping-ratio criterion. Profit was calculated across advance distances to identify the economically optimal boundary. Mechanical parameters of thermally altered rocks were obtained from laboratory deformation tests. Rhino and FLAC3D 6.0 were then used to evaluate three-dimensional slope stability at critical locations. Coal-price perturbation scenarios were finally introduced to test the sensitivity of net profit and optimal advance distance. Under the baseline coal price, the slope remained stable at an advance distance of 193 m. At this boundary, net profit reached a maximum of RMB 676.608 million. The southern surface boundary contracted by 47 m relative to the initial boundary, reducing unnecessary land disturbance. Sensitivity analysis showed that lower coal prices sharply reduced both the optimal advance distance and maximum net profit. When coal price decreased by 30%, the optimal advance distance contracted to approximately 116.9 m. Maximum net profit fell to approximately RMB 248.239 million. Higher coal prices expanded the optimal boundary outward. Once coal price reached approximately 128.7 yuan/t, or 18.5% above baseline, the optimum reached the upper constraint of 240 m. Net profit then increased substantially with further price growth. These results provide a quantitative basis for dynamic boundary optimization and disturbance-reducing extraction in fire-affected open-pit coal mines. Full article

This study focuses on the Northern New District of Liaoyuan City, Jilin Province, and develops an indicator-based relative spatial assessment framework for geological environmental carrying capacity. Fourteen indicators were selected from the geological, ecological, and socio-economic dimensions to characterize spatial differences in regional geological environmental conditions. The weights of the indicators were determined by integrating subjective and objective methods, where the Fuzzy Analytic Hierarchy Process (FAHP) and the CRITIC method were applied respectively, and the final composite weights were obtained through a game theory-based combination weighting approach. Based on the weighted results, ArcGIS was used to perform spatial analysis, and the geological environmental carrying capacity was classified into four levels: excellent, good, moderate, and poor. The results indicate significant spatial heterogeneity in geological environmental carrying capacity. Moderate-capacity areas dominate the study area, with poor-capacity areas mainly distributed in the central and southeastern regions. The proposed framework provides spatial information for identifying areas with different geological environmental conditions and supports differentiated environmental management and planning in mining areas. Full article

Lithium-ion batteries are widely used in various energy sectors, and accurately predicting their early remaining useful life (RUL) is crucial for shortening battery evaluation time and accelerating battery commercialization. However, information on degradation during the early cycling stages of batteries is limited, and it is difficult to fully characterize their lifespan. This study proposes a CNN–Transformer–BiGRU-based method for predicting the early RUL of lithium-ion batteries using Grey Wolf Optimization (GWO). First, using only the first 100 cycles of each battery in the MIT dataset, early degradation features are extracted from the dimensions of capacity and internal resistance, and then standardized. Second, a CNN is employed to extract local degradation features, while the Transformer’s self-attention mechanism is used to capture global correlations, and BiGRU is utilized to further extract bidirectional temporal dependency information. Building on this foundation, GWO is introduced to perform joint optimization of the model’s key hyperparameters to obtain optimal network parameters. Finally, the effectiveness of the proposed method is validated through ablation and comparison experiments. The experimental results show that the proposed model achieved an R² of 0.9633, with RMSE, MAE, and MAPE values of 80.5608 cycles, 63.2524 cycles, and 7.29%, respectively, demonstrating overall prediction performance superior to that of the comparison models. This method can effectively mine degradation information related to battery life from limited early-cycle data, providing an effective approach for the accurate prediction of the early RUL of lithium-ion batteries. Full article

Open-pit coal mining in arid and desert regions causes extensive and persistent surface disturbance, yet long-term monitoring of disturbance and backfilling processes remains challenging. Existing time-series change detection approaches can identify spectral changes but provide limited information on mining disturbance types and their evolution pathways. To address this issue, an automated surface disturbance detection method (Auto-SD) was developed for open-pit coal mines in arid and desert environments. This method integrates disturbance-type identification and temporal information extraction using the tasseled cap brightness (TCB) component to characterize changes associated with surface material exposure and accumulation. Using Landsat imagery from 1990 to 2023, Auto-SD was applied to 89 open-pit coal mines in China’s arid and desert regions, achieving an overall classification accuracy of 0.84. The cumulative disturbed area reached 423.10 km², while the internal dumping area reached 94.25 km², indicating limited backfilling recovery. Disturbance intensified after 2006, whereas backfilling lagged behind, forming a trajectory of rapid expansion, delayed recovery, and gradual stabilization. Spatially, mining areas exhibited a progressive transition from external dumping to internal dumping and backfilling. Furthermore, cumulative pit area generally followed an S-shaped growth pattern with mining duration. These findings provide new insights into long-term mining landscape evolution and support ecological restoration assessment and sustainable resource management in arid mining regions. Full article

Underground coal mining can induce deformation and failure of overlying strata and ground surface, which seriously endangers the safety of human life and property. During mining, the immediate roof rock successively experiences initial caving (fixed support on four sides) and periodic caving (fixed support on three sides and free on one side). Different boundary conditions alter the unloading and deformation processes such as cracking and fracturing of immediate roof rock, thereby affecting its subsequent mechanical behavior of compaction and deformation, and resulting in differences in the movement law of overlying strata. In this paper, the numerical simulation method is adopted to investigate the variation laws of unloading and fracturing characteristics of immediate roof rock under initial caving and periodic caving with thickness-width ratio (t/w), length-width ratio (l/w), unloading stress (σ_u) and specimen strength (σ_c), and the corresponding action mechanism is revealed. The fractal evolution law of fractured immediate roof rock obtained from this study can quantitatively evaluate the compaction characteristics of caved rock, provide refined parameter support for surface subsidence prediction and possess guiding significance for stope surrounding rock control engineering. The results show that the fragments formed after the failure of immediate roof rock are mainly block-strip shaped under both first caving and periodic caving conditions. With the increase in the thickness-width ratio, the flexural rigidity of immediate roof rock increases and crack propagation is restrained, so that the particle-size–mass fractal dimension of fragments increases first and then decreases for the two caving modes. The increase in length-width ratio weakens the propagation of secondary fractures and raises the particle size of fragments, while the overall variation in particle-size–mass fractal dimension is small under the two working conditions. As the unloading stress continuously rises, the coupled tension-shear effect inside the rock gradually intensifies, and the failure mode changes from tension-shear failure to global shear failure. Accordingly, both the particle-size–mass fractal dimension and fractal dimension of crack distribution increase first and then decrease under first caving and periodic caving conditions. The increase in the strength of immediate roof rock raises the energy consumption during rock failure, and large-size fragments are more likely to be generated, which reduces the particle-size–mass fractal dimension and increases the particle size of fragments under both caving modes. Meanwhile, internal micro-fractures continuously initiate and propagate with the growth of rock strength. For specimens with relatively high strength, crack propagation is inhibited and the development of secondary fractures is weakened, leading to an evolution trend that the fractal dimension of crack distribution increases first and then decreases. Under identical parameter conditions, the particle-size distribution and crack complexity for first caving are mainly affected by geometric parameters; the particle size of fragments is primarily controlled by specimen strength; and the unloading stress threshold governs the transition of failure mode. For periodic caving, the crack-initiation location is first determined by asymmetric boundary constraints. The thickness-width ratio dominates the particle-size distribution of fragments, and unloading stress as well as specimen strength further regulate the complexity of cracks. Full article

Background: Renal cell carcinomas (RCCs) represent neoplasms with variable biological behaviour, some of which remain difficult to classify within the current diagnostic framework. Clear cell papillary renal cell tumor (CCPRCT) is now recognised as an indolent entity, whereas RCC with fibromyomatous stroma (RCCFMS) remains a provisional subtype with partially overlapping morphological features. Methods: We analysed a multifocal RCC with clear cell morphology and prominent fibromyomatous stroma via whole-genome and RNA sequencing. The obtained molecular profile was compared with The Cancer Genome Atlas (TCGA) pan-cancer dataset, which includes 885 RCC cases, and histological re-evaluation of 10 identified similar cases was performed. Transcriptional data were mined for potential markers, which were validated in an independent cohort. Results: The 10 TCGA cases with similar transcriptomic features were characterised by diploid genomes, absence of recurrent chromosomal alterations, and lack of VHL mutations. Reduced VHL mRNA expression was observed, with increased methylation at selected CpG sites consistent with possible epigenetic down-regulation. Diagnostic variability was identified during histological re-evaluation of the 10 similar cases by three urological pathologists. Differential expression analysis highlighted cytokeratin 17 (CK17) and collagen 17A1 (COL17A1) as candidate markers. Immunohistochemical evaluation in a small (n = 6) independent CCPRCT cohort demonstrated expression of both markers, whereas tissue microarrays from 257 clear cell and 68 papillary RCC cases were found to be negative. Conclusions: These findings suggest that a subset of renal tumors with overlapping morphological features of CCPRCT and RCCFMS may share common molecular characteristics. CK17 and COL17A1 emerged as candidate markers for recognising these tumors, although their diagnostic sensitivity and specificity require validation across a broader spectrum of renal neoplasms. These observations are exploratory and hypothesis-generating, and further studies in large, well-characterised cohorts are required to clarify the biological and diagnostic significance of this subgroup. Full article

Medical treatment decision making is a complex process that involves integrating multivariate time-oriented data from multiple sources and is often influenced by factors such as patient load. In this study, we propose the Recurring Target Prediction (RTP) Pipeline to support treatment decision making by predicting the next medical action most likely to be administered, based on the historical data from patients in similar contexts. The method transforms raw time-stamped data into symbolic time intervals, incorporating domain knowledge. Each of the patient’s data are segmented by pre-defined trigger conditions (e.g., hypoglycemia), with each segment containing a feature window (historical data as symbolic time intervals); a prediction window (e.g., treatment dosage); and an optional prediction gap between the feature and prediction windows, enabling a future treatment alert. A frequent pattern-mining method is applied to the feature windows, and features generated from the mined patterns (e.g., count within each record and mean duration) are used as input to a Two-Step prediction model. First, a binary classifier predicts whether treatment is necessary, followed by a regression model to predict dosage. Finally, SHapley Additive exPlanations (SHAP) provide insights into the model’s decision making. We have evaluated the pipeline on an Intensive Care Unit (ICU) dataset, across three domains: hypoglycemia, hypokalemia, and hypotension. Key contributions include leveraging the recurrence of medical conditions and events to enrich the dataset, reducing false positives through a Two-Step prediction model, allowing prediction gaps for advance treatment notice, and incorporating SHAP, and introducing a two-level SHAP-based method for aggregating the relative weights of temporal patterns and components, to enhance the model’s interpretability. Full article

Seepage pressure time series of concrete dams are governed by reservoir water level, rainfall, temperature and long-term aging effects, featured by strong nonstationarity and complex multi-scale fluctuations. Existing decomposition–ensemble methods ignore nonlinear coupling among scale components, suffering low prediction accuracy and poor physical interpretability. Current model fusion schemes fail to adapt to differentiated evolution mechanisms of frequency-varying seepage components and cannot fully mine implicit cross-scale nonlinear correlations. To overcome these drawbacks, this study proposes a concrete dam seepage pressure prediction approach integrating ensemble empirical mode decomposition, multi-scale preprocessing, and optimized dual-model selection combining ridge regression and Transformer–BiLSTM. Ensemble empirical mode decomposition adaptively denoises and decouples raw seepage series into high-, medium- and low-frequency IMFs according to oscillation cycles. A normalized Comprehensive Optimization Index is constructed to parallelly train ridge regression and Transformer–BiLSTM for each component and select the optimal submodel dynamically. A fully connected nonlinear fusion layer reconstructs multi-scale predictions to retain inherent component coupling features, replacing traditional simple linear superposition. Engineering cases verify that the proposed model efficiently captures periodic laws of key influencing factors, significantly boosting prediction accuracy and generalization capacity, thus possessing prominent theoretical and practical engineering application values. Full article

With the development of smart classrooms and digital learning platforms, multimodal learning behavior data provide a new sensing basis for understanding students’ cognitive states and psychological risk warnings. However, existing educational data mining methods mainly focus on performance prediction, dropout warning, or surface-level emotion recognition, while continuous and interpretable modeling of deeper cognitive biases and related psychological risks remains insufficient. To address this issue, we propose MLBS-Net, a multimodal learning behavior sensing network for teaching feedback that jointly models students’ textual expressions, behavioral sequences, classroom interactions, and psychological auxiliary signals. MLBS-Net integrates theory-guided textual cognitive bias encoding, temporal behavioral state modeling, and reliability-aware cross-modal fusion to capture psychologically interpretable cognitive patterns, characterize dynamic learning-state changes, and adaptively integrate multimodal information according to data quality and task contribution while providing interpretable feedback for teachers. Experimental results show that MLBS-Net achieves a Macro-F1 of 0.855 for cognitive bias recognition and an AUC of 0.891 for psychological risk warning, outperforming traditional machine learning, unimodal deep learning, and standard multimodal methods. Ablation results further support the effectiveness of theory-guided semantic encoding, temporal behavioral modeling, reliability estimation, and multitask learning. These findings demonstrate that MLBS-Net can jointly characterize cognitive biases and potential psychological risks from multisource learning behaviors, providing a feasible approach for learning-state sensing, risk warning, and interpretable teaching support in smart education. Full article

Classification is a fundamental technique in data mining that predicts categorical labels by analyzing input features. However, training Artificial Neural Networks (ANNs) using traditional methods often encounters challenges, such as getting stuck in local minima and slow convergence. To address these issues, this study proposes a novel hybrid model, IPA-ANN, which integrates the Immune Plasma Algorithm (IPA) to optimize the ANN’s connection weights and biases. The IPA, inspired by the immune plasma treatment process, utilizes a unique donor-receiver mechanism to balance exploration and exploitation in the search space. The proposed model was evaluated on nine benchmark datasets from the UCI repository and compared with 18 state-of-the-art metaheuristic algorithms, including Grey Wolf Optimization (GWO), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Experimental results were analyzed using accuracy, F1-score, confusion matrices, and convergence graphs. The findings indicate that IPA-ANN achieves competitive and stable classification performance across different datasets while demonstrating favorable convergence characteristics in several cases. Furthermore, the study investigates the influence of donor–receiver parameters on the optimization process, highlighting the adaptability of the proposed framework. The reliability of these findings was further examined through repeated stratified 5-fold cross-validation and paired Wilcoxon signed-rank tests with Holm–Bonferroni correction on representative datasets, confirming that a subset of the observed performance differences are statistically significant, and through a computational cost analysis showing that IPA-ANN incurs no additional overhead relative to the majority of the compared algorithms. This study contributes to the literature by presenting the first documented application of IPA in ANN training and by providing a modular infrastructure for future metaheuristic-based ANN optimization studies. Full article

Mining disturbances can promote roof separation, fracture propagation, strata collapse, and water-conducting fracture-zone development, increasing roof water-hazard risk. Conventional apparent-resistivity pseudosection imaging is useful for rapid display; however, restricted electrode deployment limits effective coverage and representation of anomaly position and spatial continuity. Time-lapse resistivity inversion can characterize progressive fracture development, but representation of discontinuous anomalies caused by rupture, fracture connection, or collapse can be affected by inversion model constraints. To address these limitations, this study proposes a full-space apparent-resistivity rapid imaging method based on point-source attenuation fields. Each current electrode is regarded as a point current source, and potential attenuation with distance is used to construct attenuation curves, map responses to target-region grids, fuse multi-source estimates, and extract representative apparent-resistivity values. Numerical simulations and a scaled physical model experiment show that the method improves the spatial continuity of electrical anomaly responses and provides a more direct representation of abrupt electrical changes. Field application indicates that the method can identify mining-related electrical anomalies in roofs and anomalous ranges potentially associated with fracture development. The maximum vertical extent of the electrical anomaly was approximately 37 m, which was broadly consistent with the empirical estimate of approximately 40 m. The proposed approach provides an efficient geoelectrical monitoring tool for roof water-hazard identification and fracture-zone delineation in coal mining. Full article