Search Results (641)

Thermal infrared land-surface temperature (LST) anomalies can support early-stage geothermal reconnaissance, but in humid, vegetated and urbanizing low-temperature regions they are readily affected by land cover, terrain and human activity. This study develops a reliability-aware Landsat 8 framework for Quzhou, eastern China. Landsat 8 Collection 2 winter scenes from 2016 to 2025 were cloud-screened, converted to LST, and composited using a per-pixel median strategy. NDVI, NDBI and NDWI masks reduced vegetation, impervious-surface and water interference. Standardized LST anomalies were integrated with a 3 km local-relief constraint, candidate-patch vectorization, and patch-level scoring based on thermal persistence, terrain context, land-cover reliability, structural proximity and lithology/contact favorability. General and strong winter LST anomalies cover approximately 64.4 and 22.3 km², respectively, with impervious surfaces accounting for 55.97% and 67.65% before reliability screening. The terrain-constrained workflow reduces the candidate area to approximately 22.2 km² and ranks 267 patches, including 27 very-high relative reconnaissance-priority patches. The highest-ranked patches are characterized mainly by combined thermal persistence, lower land-cover interference, favorable fault proximity and favorable lithology/contact settings, indicating relative priority rather than confirmed resources. Integrated reliability screening is therefore essential before using Landsat LST anomalies for geothermal target prioritization. Full article

The Ulungu Depression, located in the northeastern Junggar Basin, adjacent to the Altai Orogenic Belt, exhibits distinctive tectonic relationships and evolutionary mechanisms. Through integrated interpretation of seismic and electromagnetic data, a composite transect was established to characterize the deep-to-shallow geological architecture of the Altai Orogenic Belt–Ulungu Depression system. The tectonic evolution since the Late Paleozoic was reconstructed, revealing three distinct phases: (1) Late Paleozoic peripheral foreland basin development, (2) Mesozoic intracontinental foreland basin formation, and (3) Cenozoic intracontinental foreland basin reactivation. The Late Paleozoic phase records the formation of a peripheral foreland basin in the northeastern Junggar Basin, driven by the Altai-Junggar collision orogeny. During the Mesozoic, intracontinental orogeny along the Altai Belt controlled the development of an intracontinental foreland basin in this region. Paleogene tectonic quiescence facilitated regional subsidence and stable sedimentary deposition. From the Neogene to Quaternary, the Ulungu Depression experienced weak compressional deformation dominated by minor thrust faults, with intermittent regional extensional structures, attributable to the far-field effects of the India-Eurasia collision. This distant tectonic stress primarily localized intracontinental orogeny in the North Tianshan, while the Ulungu Depression, situated farther north, exhibited attenuated strain partitioning. Reconstruction of this multi-phase tectonic evolution provides critical insights into the accretionary orogenic processes of Central Asia. Furthermore, it offers practical implications for hydrocarbon exploration in the Ulungu Depression, particularly regarding structural traps and reservoir distribution patterns. Full article

The Huize Ge-rich Pb-Zn deposit is an important part of the Sichuan–Yunnan–Guizhou carbonate-hosted Pb-Zn metallogenic area and is one of the most representative super-large deposits in the northeastern Yunnan Pb-Zn ore concentration area. The orebodies mainly occur in NE-trending interlayer fault zones. The Pb-Zn mineralization process of this deposit can be divided into the dolomite stage (I), sphalerite-galena stage (II), galena-sphalerite stage (III), and pyrite-calcite stage (IV). Based on a study of the deposit geology, we utilized LA-ICP-MS for in situ microanalysis of trace element compositions and element mapping of sphalerite from different stages to reveal the characteristics of the sphalerite trace element composition and occurrence mechanisms, understand the mineralization process, and constrain the genetic type of the deposit. This research shows that sphalerite color variations result from the multi-factor coupling of multiple trace element contents, element associations, and isomorphic substitutions among elements. Trace elements such as Mn, Fe, Cu, Ga, Ge, Ag, Cd, In, Sn, Sb, and Hg occur in the sphalerite lattice in the form of isomorphic substitutions or nanoscale mineral inclusions, whereas Pb occurs mainly as microscopic mineral inclusions (galena) in sphalerite. From the early to late stages of mineralization (SpI → SpII → SpIII), the mineralization temperature (132–205 °C) and sulfur fugacity (log₁₀ fS₂ = −15.29 to −19.89) both show a gradual decrease. During sphalerite crystallization in different stages, multiple trace elements exhibit coupled multi-element substitutions at the microscale: SpI: Zn²⁺ ↔ (Fe²⁺, Mn²⁺, Cd²⁺), 2Zn²⁺ ↔ 2Ag⁺ + Ge²⁺; SpII: Zn²⁺ ↔ (Fe²⁺, Cd²⁺), 2Zn²⁺ ↔ 2Ag⁺ + Ge²⁺, 3Zn²⁺ ↔ 2Cu²⁺ + Ge²⁺, 3Zn²⁺ ↔ 2(Cu, Ag)²⁺ + Ge²⁺, 2Zn²⁺ ↔ Ga³⁺ + Cu⁺, 2Zn²⁺ ↔ Ga³⁺ + (Cu, Ag)⁺; and SpIII: Zn²⁺ ↔ (Fe²⁺, Mn²⁺), 3Zn²⁺ ↔ 2Cu²⁺ + Ge²⁺, 3Zn²⁺ ↔ 2(Cu, Ag)²⁺ + Ge²⁺). Mn, Fe, and Ge are mainly enriched in SpI; Ga and Ag are mainly enriched in SpII; and Cd is mainly enriched in both SpI and SpII. By comparing the sphalerite trace elements signature of the Huize Ge-rich deposit with those of global typical MVT, SEDEX, VMS, epithermal, and skarn-type Pb-Zn deposits, and considering the deposit’s geological and geochemical characteristics, we suggest that the Huize Pb-Zn deposit is best classified as a medium- to low-temperature, carbonate-hosted Pb-Zn deposit. Full article

Thin, steeply dipping orebodies hosted in kaolinized altered fault zones are difficult to mine safely because of weak rock mass integrity, water sensitivity, and limited self-supporting capacity. This study investigates the F20 ore-bearing altered structural zone at Changtai Mining and develops an artificial floor design for downward drift-and-fill mining. Engineering geological characterization, rock mass quality evaluation, mechanical analysis, and three-dimensional numerical simulation were combined to assess floor-bearing requirements and regional recovery stability. The results show that the wall rocks are grade III, whereas the ore-bearing altered zone is grade IV and represents the controlling weak component. For the preferred 3.5 m × 3.5 m drift, an equivalent artificial floor bearing thickness of about 1.0 m is required. Numerical evaluation indicates that supported drifts remain stable, but crosscut–drift intersections are the main deformation and damage concentration zones. A representative 0.5 m drift offset significantly weakens the load-transfer path of the floor–rock system. The proposed vertically aligned, short drift, rapid backfill scheme with a reinforced composite artificial floor provides a practical basis for safe recovery of weak kaolinized altered orebodies. Full article

Existing single-ended protection schemes for flexible DC transmission lines are negatively affected by traveling wave (TW) refraction and reflection interference and nonlinear overfitting under low-resistance faults. To address this, in this study, line-mode voltage reverse TWs are mathematically analyzed, revealing that internal faults and forward external faults exhibit single- and double-exponential attenuation, respectively. An adaptive constant-value flattening method is proposed to suppress subsequent TW refraction and reflection. Additionally, a composite fitting strategy utilizing Levenberg–Marquardt (LM) and Moore–Penrose pseudoinverse (PINV) algorithms is proposed to fit the measured waveforms, solving the problem of low-resistance overfitting and amplifying residual differences between internal and external faults. Based on these principles, a novel single-ended protection scheme is proposed. Simulations verify that this scheme exhibits a high operating speed and strong robustness against different fault distances, different fault resistances, and noise. Full article

In this work, 2.0 wt.% SiCp/AZ91D magnesium matrix composite was fabricated by stir casting, and its microstructure and properties were optimized through a coupled process of parallel equal-channel angular combined extrusion (PC-ECAP) and T6 heat treatment. The results indicate that the extrusion temperature has a significant influence on the microstructure and mechanical properties of the material. At an extrusion temperature of 350 °C followed by T6 heat treatment, the 2.0 wt.% SiCp/AZ91D composite exhibits a tensile strength of 221 MPa, an elongation of 19.2%, and a product of tensile strength and elongation (PSE) of 4.24 GPa%, which represent increases of 14.5%, 128.6%, and 161.7%, respectively, compared with the as-cast specimen. To elucidate the microscopic mechanism of the enhanced ductility, first-principles calculations were further performed. It is found that Al solute atoms can reduce the electron localization in the Mg–Mg bond region, causing a downward shift of the d-band center, thereby weakening the interatomic bonding strength on the slip plane. This effect is equivalent to reducing the stacking fault energy of non-basal slip. The finding provides a theoretical explanation for the activation of multiple slip systems and the suppression of twinning observed in the experiments. By combining experiments with calculations, this study systematically reveals the mechanism underlying the synergistic regulation of strength and ductility in the composite by PC-ECAP coupled with T6 heat treatment, offering a theoretical basis and process reference for the fabrication of high-performance magnesium matrix composites. Full article

Geochemical anomaly detection plays a critical role in mineral exploration, yet conventional methods are often limited by compositional effects, sensitivity to outliers, and insufficient consideration of spatial relationships. To address these issues, this study proposes an integrated analytical framework that combines compositional data analysis and spatial statistics for robust geochemical anomaly identification. The framework incorporates isometric log-ratio (ILR) transformation to eliminate the closure effect, robust principal component analysis (RPCA) to extract stable geochemical patterns, local indicators of spatial association (LISAs) to characterize spatial clustering, and compositional balance analysis (CoBA) to enhance anomaly signals. The method is applied to the Barkol Lake area in the Eastern Tianshan, a key metallogenic belt within the Central Asian Orogenic Belt. The results reveal significant geochemical anomalies characterized by Cu-associated element assemblages (e.g., Cu–Ni–Cr), which are spatially correlated with major fault zones and volcanic–intrusive complexes. The identified anomalies show strong consistency with known mineral occurrences and delineate several prospective targets for copper polymetallic mineralization. Compared with conventional approaches, the proposed framework demonstrates improved robustness to outliers, enhanced sensitivity to weak anomalies, and better integration of compositional and spatial constraints. Full article

Gramian Angular Field (GAF) encodings transform time series into two-dimensional images suitable for convolutional neural network (CNN) classification. Existing applications typically use either the Gramian Angular Summation Field (GASF) or the Gramian Angular Difference Field (GADF) independently, although these two encodings capture complementary pairwise angular relationships. This paper proposes the Composite Gramian Angular Field (CGAF), a single-image time-series representation obtained by a weighted algebraic combination of the summation and difference GAF components. The weights are optimised using coarse grid search followed by Gaussian-process Bayesian refinement, with all candidate evaluation restricted to training-only inner validation partitions. The selected weights are frozen before held-out test evaluation. CGAF produces a single encoded output image (approximately 0.08 MB, compared with approximately 0.16 MB for retaining separate GASF and GADF images) and encodes at $5.9\pm 0.3$ ms per sample. We evaluate CGAF in three domain-specific settings—EEG cognitive engagement, PTB-DB heartbeat classification, and FordA automotive fault detection—and on a selected subset of 20 datasets from the UCR Time Series Classification Archive. The method is compared with GASF, GADF, recurrence plots, spectrogram-based encodings, and non-image time-series baselines including SVM, ResNet-1D, InceptionTime, and ROCKET. On the evaluated datasets, CGAF consistently improves over the individual GASF and GADF encodings. It achieves macro-F1 $=0.867\pm 0.027$ on the EEG pilot study, heartbeat-segment-level macro-F1 $=0.941\pm 0.018$ on PTB-DB, and test accuracy $=91.2\%$ on FordA. Because patient identifiers are unavailable for PTB-DB, that result does not establish patient-level generalisation. On the selected UCR subset, CGAF outperforms both GASF and GADF on all 20 datasets. It achieves the best overall accuracy among all evaluated methods on 14 of 20 datasets, whereas ROCKET achieves the best overall accuracy on the remaining six datasets. The results suggest that algebraic integration of summation-based and difference-based angular dependencies can improve image-based time-series classification without modifying the CNN backbone or adding gradient-trained parameters. The EEG results should be interpreted as pilot evidence, whereas broader generalisation requires evaluation on the full UCR/UEA archive, additional biomedical cohorts, and further backbone architectures. Full article

To address prevalent industrial challenges, including the high cost of fabricating microstructures via photolithography and 3D printing, impurity residues easily generated by conventional physical/chemical pore-forming techniques, and the limited sensitivity of regular capacitive sensors, this paper innovatively proposes an integrated low-temperature in situ gas foaming strategy using ammonium bicarbonate for the fabrication of porous TPU-based ionic gels. Relying on the complete gaseous decomposition property of ammonium bicarbonate upon heating, a three-dimensionally interconnected continuous porous network is spontaneously constructed inside the polymer matrix. Thermoplastic polyurethane (TPU) is selected as the continuous polymer phase, and [EMIM][TFSI] imidazolium ionic liquid is blended as the ion source to synthesize composite ionic gel substrates. A PDMS composite slurry filled with graphene is employed to prepare flexible substrates, followed by low-temperature oxygen plasma surface modification to introduce polar functional groups such as hydroxyl and carboxyl onto electrode surfaces. A standard sandwich-structured ionic pressure sensor with the configuration of “top modified electrode—porous ionic gel dielectric layer—bottom modified electrode” is finally assembled. The porous framework and modified electrodes constitute a dual synergistic enhancement system: the porous structure markedly reduces the equivalent elastic modulus of the gel and improves its compressive deformation capacity; polar-modified electrodes optimize the interfacial compatibility between electrodes and gels, shorten ion migration paths and lower interfacial contact resistance. Systematic calibration of multiple batches of parallel samples reveals that the as-fabricated sensor achieves a high sensitivity of 25.3 kPa⁻¹ across the full measuring range from 0 to 1000 kPa with a linear fitting coefficient R² = 0.992. The loading response time and unloading recovery time of the device are 60 ms and 80 ms respectively, with a performance degradation of less than 3% after 1000 consecutive loading–unloading cycles, featuring low hysteresis error and excellent signal repeatability. Multi-scenario in vivo wearable tests on human subjects verify that the device can precisely capture subtle fluctuations of radial artery pulse and periodic laryngeal deformation during swallowing, distinguish characteristic waveform patterns of various English words according to differences in vocal cord vibration, and accurately detect bending motions when attached to finger joints. The entire fabrication process adopts common chemical raw materials and standard laboratory equipment without expensive micro-nano processing facilities, featuring convenient raw material procurement and high process fault tolerance, which enables large-area coating-based mass production. This work delivers a novel technical route for the low-cost large-scale production of high-performance ionic flexible sensors and bears significant industrialization reference value for applications in wearable medical monitoring, bionic robotic electronic skin, flexible human–machine interactive touch panels and other related fields. Full article

This paper proposes a novel fault-identification method for micro-coils in relays with forcibly guided contacts, a type of electromechanical elementary component (EMEC), combining a composite analytical model, a 2D thermal model, and a 1D-CNN. A low-order thermal circuit with one central node and four boundary nodes is established, while a two-dimensional anisotropic Poisson equation is used as a high-order calibration model. The two models are coupled through iterative correction of reusable thermal resistances. For thermal aging, enamel-film delamination, and inter-turn short-circuit faults, thermal-conductivity attenuation, asymmetric branch-resistance perturbation, and localized abnormal heat-source injection are introduced to generate physically constrained temperature sequences. Orthogonal centerline temperature distributions are extracted as one-dimensional feature vectors for 1D-CNN classification. Simulation results show that the hybrid model has an error of approximately 1.7% compared with finite-element results, and the trained 1D-CNN achieves 98.13% accuracy on 160 test samples. Experimental reconstruction and deep-feature visualization further verify its ability to distinguish normal, aging, delamination, and local short-circuit states. Full article

To reveal the damage–seepage coupling mechanism of delayed floor water inrush induced by small fault activation under mining-induced stress, a cubic cement mortar specimen containing a persistent small fault was prepared based on similarity theory. Systematic triaxial loading–seepage tests were conducted under different fault fracture zone particle gradations, fracture zone widths, and fault angles, with simultaneous monitoring of stress–strain behavior, acoustic emission (AE) characteristics, and seepage flow evolution. The results show that: ① The peak strength decreases with increasing fracture zone width, but increases with increasing Talbot gradation coefficient (a fractal grading method) and fault angle. The failure mode transitions from shear-dominated to tension–shear composite failure. The spatial localization of AE events corresponds well with macroscopic fracture surfaces, and the AE source amplitude is positively correlated with compressive strength. ② The seepage flow exhibits a nonlinear evolution pattern of “compaction stabilization—stepwise rise—plateau stabilization” during loading. In the early loading stage, compaction of the fracture zone causes a slight decrease in flow. Approaching peak strength, the initiation and propagation of through-going fractures create interconnected seepage channels, leading to a stepwise jump in flow. In the post-peak stage, accompanied by fine particle erosion and framework reconfiguration, the flow tends to stabilize. A larger fracture zone width, smaller gradation coefficient, and smaller fault angle result in a more significant post-peak seepage surge, with the maximum flow rate reaching 3.6 times that of the specimen with a 2 mm wide fracture zone. ③ Grey relational analysis indicates that the fault angle is the most sensitive factor affecting the risk of delayed water inrush (correlation degree 0.788), followed by particle gradation and fracture zone width. The study demonstrates that under monotonic loading conditions, the damage evolution and seepage response of small faults are jointly controlled by their geometric parameters and internal structure, with the fractal grading method effectively quantifying the role of particle gradation. The findings provide a theoretical basis for risk assessment of delayed water inrush from small faults in working faces above confined aquifers. Full article

Fault-injection campaigns are widely used to evaluate silent data corruption (SDC) in AI hardware, but exhaustive campaigns over workloads, dataflows, processing elements, and datapath roles are expensive. This paper presents an architecture-level risk-guided fault-injection prioritization method for systolic AI accelerators. The method ranks candidate transient functional perturbations before downstream validation, with the goal of enriching the discovery of candidates that produce a thresholded relative-output-error outcome under a limited validation budget. The evaluation uses a fixed candidate fault pool: all ranking policies score the same 21,000 candidate faults across 30 workload/dataflow/array configurations, corresponding to five GEMM-derived workloads, three array sizes, and two dataflows. Fault magnitudes are sampled once per candidate and are independent of all ranking scores. Candidate faults are modeled as transient architecture-level perturbations in MAC, accumulator, or forwarding paths. The proposed full-risk score combines activity, composite spatial stress, tensor sensitivity, and a path-class weight. In the proposed architecture-level simulation environment and under the fixed-pool protocol, the proposed method achieves the highest mean top-10% SDC-proxy lift, AUPRC, NDCG@10%, and rank correlation with relative output error among the evaluated principle-based ranking policies. At the calibrated threshold, it achieves a mean top-10% lift of 5.65× [4.91, 6.38], compared with 4.61× for AVF-like exposure and 4.33× for output sensitivity. Paired configuration-level tests, threshold sensitivity, and outcome-model sensitivity analyses characterize the result while showing that the proposed score is not universally dominant under every synthetic outcome assumption. The method is intended as a front-end architecture-level screening tool for validation prioritization, not as a replacement for RTL, gate-level, FPGA, or silicon reliability signoff. Full article

Hyperspectral remote sensing enables precise identification of alteration mineral through spectral–image integration and high-resolution capabilities. However, vegetation interference significantly hinders the extraction of alteration information in vegetated areas, thereby posing challenges to the reliable identification of alteration minerals. This study employs GF-5B satellite AHSI imagery acquired in the Huzhou region of Zhejiang Province, China, to address this challenge via a novel Zonal Adaptive Vegetation Suppression Technique (ZAVST). By constructing segmented statistical models that links reflectance characteristics across multiple spectral bands to NDVI values, ZAVST demonstrates an enhanced capability to mitigate vegetation obscuration effects on subsurface lithological features while substantially improving the identification of subtle spectral signatures characteristic of mineralization. Results reveal distinct spatial patterns: Fe-bearing alteration minerals (hematite, pyrite) align along NE-trending faults and volcanic basin margins; Al-OH alterations (montmorillonite, kaolinite) cluster near intrusive contacts; Mg-OH alterations (chlorite, epidote) occur at interfaces between carbonate sequences and concealed intrusions. Composite alteration anomalies exhibiting stacked mineral signatures (up to four distinct types) were identified across the region, demonstrating a strong spatial correlation with known mineralization centers. By integrating alteration zonation, structural lineaments, stratigraphy, geochemical anomalies, and orebody records, this study delineated four priority targets: Lijiaxiang Town, eastern Meixi Town, Miaoxi Town, and the central Moganshan Volcanic Basin. Full article

This paper proposes an integrated attitude control framework for flexible spacecraft subject to external disturbances, rigid–flexible dynamic coupling, and actuator faults. The control framework combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with an adaptive fault-tolerant (AFT) compensator. First, a rigid–flexible coupling dynamic model is formulated using Modified Rodrigues Parameters. Second, an observer-based TD3 attitude controller is designed, where a hierarchical reward function incorporating the observer-estimated flexible modal displacement $\widehat{\mathit{\eta }}$ is constructed to train the agent for simultaneous attitude convergence and vibration suppression. Third, a composite fault-tolerant control structure is developed by integrating the trained TD3 policy with an adaptive sliding mode compensator that handles both partial loss-of-effectiveness faults and time-varying additive faults. The proposed framework is evaluated under a progressive five-scenario uncertainty evaluation framework encompassing measurement noise, parameter mismatch, external disturbances, and actuator faults. Simulation results demonstrate that (i) the $\widehat{\mathit{\eta }}$-augmented reward enables substantial improvements in vibration suppression over the baseline reward, achieving a better balance between pointing accuracy and vibration attenuation; (ii) under the most demanding fault scenario, the AFT compensator proves essential for precise convergence, and the composite TD3+AFT architecture achieves the best overall performance among the four compared control schemes. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article