Search Results (675)

Polyester yarn bundle geogrids are widely used materials in flexible retaining structures due to their high toughness and high-strength mechanical properties. To investigate the mechanical characteristics and the interfacial mechanical properties of these geogrids, a series of pull-out tests were conducted under different pull-out rates and filling water contents. Based on the test results, a DEM-FDM coupled numerical model for pull-out behavior was established to analyze the pull-out deformation behavior of the geogrids. Combined with the theoretical analysis of the load-bearing characteristics of the geogrids, the reinforcement mechanism of polyester yarn bundle geogrids was revealed. The results show that there exists a critical pull-out rate of 1 mm/min that maximizes the pull-out resistance; the interface friction angle decreases with an increase in pull-out rate, while the interface cohesion shows an opposite trend. The filling water content presents a more significant weakening effect on the soil–geogrid interface strength under low stress, resulting in a strain-softening type of pull-out curve. Unlike fine-ribbed plastic geogrids, the sliding frictional resistance of polyester yarn bundle geogrids accounts for 80% of the total pull-out resistance during the pull-out process. The mechanical interlocking force, which arises from the bulges on the mid-section of transverse ribs and the downward bending of longitudinal rib edges, is subject to dynamic changes in the course of the pull-out process. The geogrid exhibits overall shear failure under low normal stress (${\sigma }_n<$ 200 kPa) and penetration shear failure under high normal stress (${\sigma }_n\ge$ 200 kPa). In practical engineering installation, polyester yarn bundle geogrids should be placed as parallel as possible to maximize the frictional resistance with filled soil and should take care of the geogrid joints for enhanced durability of the geogrids. Full article

Background: Whole-slide imaging and algorithmic advances have moved computational pathology from research to routine consideration. Despite notable successes, real-world deployment remains limited by generalization, validation gaps, and human-factor risks, which can be amplified in resource-constrained settings. Content/Scope: This narrative review and implementation perspective summarizes clinically proximate AI capabilities in cancer pathology, including lesion detection, metastasis triage, mitosis counting, immunomarker quantification, and prediction of selected molecular alterations from routine histology. We also summarize recurring failure modes, dataset leakage, stain/batch/site shifts, misleading explanation overlays, calibration errors, and automation bias, and distinguish applications supported by external retrospective validation, prospective reader-assistance or real-world studies, and regulatory-cleared use. We translate these evidence patterns into a practical checklist covering dataset design, external and temporal validation, robustness testing, calibration and uncertainty handling, explainability sanity checks, and workflow-safety design. Equity Focus: We propose a stepwise adoption pathway for low- and middle-income countries: prioritize narrow, high-impact use cases; match compute and storage requirements to local infrastructure; standardize pre-analytics; pool validation cohorts; and embed quality management, privacy protections, and audit trails. Conclusions: AI can already serve as a reliable second reader for selected tasks, reducing variance and freeing expert time. Safe, equitable deployment requires disciplined validation, calibrated uncertainty, and guardrails against human-factor failure. With pragmatic scoping and shared infrastructure, pathology programs can realize benefits while preserving trust and accountability. Full article

With the increase in mining intensity and scale, the damage to groundwater resources and surface ecology caused by coal mining has become the main problem facing coal development. Coal mining can cause a redistribution of stress field and stress concentration in local areas of overlying rock, resulting in varying degrees of movement and damage to the overlying rock. Quantitative analysis of the degree of migration and damage in different areas of overlying rock and zoning control is crucial for achieving loss reduction and green mining. In this paper, the overburden damage is divided into regions according to the different causes of formation, regional characteristics of severity, and other factors, and the specific calculation method is given. UDEC7.0 numerical simulation software is used to simulate the overlying rock damage, and the best mining parameters are provided through the area changes in different zones. The research conclusions are as follows: according to the different damage states of overburden rock, the damage of overburden rock can be divided into four parts: I, caving fracture zone, II, fracture development zone, III, sliding failure zone, and IV, slight failure zone. In the four zones, the damage in zones II and IV is relatively light. During the mining process, attention should be given to controlling the development of Zone I to prevent it from abnormally enlarging; for Zone II, hydraulic fracturing can be used when there is a thick, hard key layer that poses a water inrush risk; for Zone III, the focus should be on preventing surface step fractures caused by it. For example, when a thick, hard key layer is present in Zone II, hydraulic fracturing can be applied to avoid large area hanging roofs and severe rock pressure. When the mining height is low, it mainly affects the proportion of regions I and III. With the increase in mining height, the main affected region becomes the II region. The larger the mining height is, the larger the proportion of the II region. With the increase in propulsion speed, the impact range on the surface increases, but the area with severe damage is relatively reduced. With the increase in mining width, the proportion of relatively seriously damaged areas increased. On-site measurements have shown that when the speeds of 120,401 and 22,207 working faces are slow, the rock layer pressure shows a dense state, the overburden fracture is more fully developed, and the area proportion of I and II zones is increased, which reflects the phenomenon of dense surface fracture development on the surface. When the advancing speed is large, the area proportions of zones III and IV increase, and the damage scope decreases. The on-site testing verified the conclusions drawn from theoretical analysis and numerical simulation, which can guide other mines under similar conditions to achieve safe and green production. Full article

For marine development in harsh sea states, floating-body salvage equipment serves as critical support infrastructure. Aiming at the challenges of nonlinear dead-zone, model uncertainty, and actuator failures in the wave compensation systems of such equipment, this paper proposes an intelligent fault-tolerant control method based on neural networks. First, the dead-zone nonlinearity of the hydraulic system is compensated using an inverse model approach. Then, neural networks are employed to online learn unmodeled dynamics, while adaptive laws are designed to handle partial actuator failures and Lyapunov theory is used to prove the global stability of the closed-loop system, effectively enhancing the robustness and fault-tolerance of the wave compensation system under complex sea conditions. Unlike existing studies that rely on accurate system models, the proposed method integrates data-driven learning with model-based compensation. This integration enables adaptive handling of wave disturbances, model uncertainties, and actuator faults, thereby overcoming the strong model dependence and complex observer design inherent in traditional sliding-mode fault-tolerant control. Simulation and experiment results show that the method ensures high-precision dynamic tracking and compensation performance under various sea conditions. Full article

Background: With extensive research and development in the past decade, the affordability of Poly (ADP-ribose) polymerase (PARP) inhibitor therapy has drastically improved. Homologous recombination deficiency (HRD), a key biomarker, has been identified as an important guiding factor for PARP inhibitor therapeutic decisions in breast and ovarian cancer. However, identification of patients who will respond to Poly (ADP-ribose) polymerase (PARP) inhibitor therapy is challenging due to the lack of a unifying morphological phenotype. Current HRD testing via next-generation sequencing (NGS) is tissue-dependent, has high failure rates, misses relevant HRD genes, and involves longer turn-around times. Methods: To overcome these limitations, we developed a multimodal AI model, TRINITY, combining imaging, image-based transcriptome data, and clinico-molecular data, to examine whole-slide images (WSIs) obtained from hematoxylin and eosin (H&E)-stained samples to non-invasively predict HRD status. Results: The TRINITY model, tested on 316 TCGA breast and OV samples, presented a sensitivity of 0.77 and 0.91, NPV of 0.94 and 0.86, PPV of 0.63 and 0.58, specificity of 0.89 and 0.47, and AUC-ROC of 0.91 and 0.72, respectively. The model also yielded a similar outcome in a blind study of 74 samples, with a sensitivity of 81.2, NPV of 0.85, PPV of 0.77, specificity of 0.81, and high AUC-ROC value of 0.89, showing its promising preliminary evidence of predicting HRD status on external cohorts. Conclusions: These findings demonstrate TRINITY’s potential as a rapid, cost-effective, and tissue-sparing alternative to conventional NGS testing. While promising, further validation is needed to establish its generalizability across broader cancer types. Full article

The microstructure and wear behaviors of Mg₂Si/Al composites with 0 wt.%, 5 wt.%, and 10 wt.% SiC particles were studied using XRD, OM observation, SEM observation, EDS analysis, an extraction experiment, a hardness test, and the dry sliding wear test. It is shown by the results that after the addition of 10 wt.% SiC particles, the population of primary Mg₂Si particles increased, while the mean size of these particles reduced from 40 ± 10 μm (in the SiC-free composite) to 25 ± 8 μm. Both the matrix and the eutectic structure were refined. The tetrakaidecahedral morphologies of Mg₂Si crystals were confirmed by the results of extraction tests. The wear test results with GCr15 steel as the friction pair show that the Mg₂Si/Al composite with 10 wt.% SiC particles displayed more favorable wear resistance than the specimens with 0 wt.% and 5 wt.% SiC particle additions under both constant load and constant sliding velocity conditions. Under applied loads of 10 N, 20 N, and 30 N at a fixed sliding speed of 300 r/min, the wear rate of the Mg₂Si-Al composites reinforced with 10 wt.% SiC particles was 36.01%, 48.29%, and 23.32% lower than that of the SiC-free composites, respectively. When the sliding speed was set to 300 r/min, 550 r/min, 750 r/min, and 1000 r/min under a constant applied load of 20 N, the wear rate of the 10 wt.% SiC-reinforced Mg₂Si-Al composites was reduced by 40.37%, 40.87%, 26.20%, and 25.78%, respectively, compared with the SiC-free counterparts. The wear failure mechanisms of (Mg₂Si + SiC_P)/Al composites were mainly adhesive wear and abrasive wear, but the proportion of oxidation wear increased after the addition of the SiC particles. Full article

Accurately assessing collapse risks of high-elevation, concealed rock mass blocks within the steep cliffs of Bijiashan, Three Gorges Reservoir Area, is challenging. This study employed a multidimensional approach—integrating airborne Light Detection and Ranging (LiDAR), the transient electromagnetic method (TEM), close-range photogrammetry, horizontal drilling, and borehole optical imaging—to characterize the rock mass structure of the WD1 cliff belt and delineate 52 individual blocks. Stability analysis incorporated stereographic projection for macro-scale assessment and employed mechanical models specific to three primary failure modes (toppling, sliding, falling). Finite element strength reduction quantified the stress–strain response of a representative block under natural and rainstorm conditions. Particle Flow Code (PFC) simulated dynamic instability of the exceptionally large block W1-37. Results indicate the WD1 rock mass is highly fractured, with base sections prone to weakness. Toppling failure dominates (90.4%). Under rainstorm conditions, the average Factor of Safety (FOS) decreased by 14.7%, and 73.1% of the blocks that were stable under natural conditions were destabilized—specifically transitioning to marginally stable or substable states—often triggering chain-reaction instability characterized by “crack propagation—base buckling”. W1-37 exhibited staged failure under rainstorm: “strain localization at fissure tips—penetration of basal cracks—overturning of the upper rock mass”. Its frontal rock reached a peak sliding velocity of 15.17 m/s, indicative of base-breaking toppling. The integrated “multi-technology survey—multi-method evaluation—multi-scale simulation” framework provides a quantitative basis for risk assessment of rock mass disasters in the Three Gorges Reservoir Area and offers a technical paradigm for similar high-steep canyon regions. Full article

As shale gas development extends into deeper formations, the unclear brittle-ductile transition (BDT) mechanism and low fracturing efficiency have emerged as critical bottlenecks, posing challenges to the sustainable and economical utilization of this clean energy resource. This study, focusing on the Liangshang Formation shale of Sichuan Basin’s Pingye-1 Well, pioneers a paradigm shift by identifying Poisson’s ratio ($\mathsf{\nu }$) as the master variable governing this transition. Triaxial tests reveal that $\mathsf{\nu }$ systematically increases with depth, directly regulating the failure mode shift from brittle fracture to ductile flow. Building on this, we innovatively propose the Poisson’s Ratio-regulated Energy-based Brittleness Index (PNE-BI) model. This model achieves a decoupled diagnosis of BDT by quantifying how ν intrinsically orchestrates the energy redistribution between elastic storage and plastic dissipation, utilizing ν as the sole governing variable to regulate energy weighting for rapid and accurate distinction between brittle, transitional, and ductile states. Experiments confirm the $\mathsf{\nu }$-dominated energy evolution: Low $\mathsf{\nu }$ rocks favor elastic energy accumulation, while high $\mathsf{\nu }$ rocks (>0.22) exhibit a dramatic 1520% surge in plastic dissipation, dominating energy consumption (35.9%) and confirming that $\mathsf{\nu }$ enhances ductility by reducing intergranular sliding barriers. Compared to traditional multi-variable models, the PNE-BI model utilizes $\mathsf{\nu }$ values readily obtained from conventional well logs, providing a transformative field-ready tool that significantly reduces the experimental footprint and promotes resource efficiency. It guides toughened fracturing fluid design in ductile zones to suppress premature closure and optimizes injection rates in brittle zones to prevent fracture runaway, thereby enhancing operational longevity and minimizing environmental impact. This work offers a groundbreaking and sustainable solution for boosting the efficiency of mid-deep shale gas development, contributing directly to more responsible and cleaner energy extraction. Full article

The energy efficiency and hover stability of unmanned aerial vehicles are critical factors, since improper battery utilization and unstable control are major sources of operational failures and accidents. The proportional–integral–derivative (PID) controller, which is applied in approximately 97% of multirotor unmanned aerial vehicle (UAV) systems, is widely used due to its simplicity; however, it is sensitive to external disturbances and often fails to ensure optimal energy utilization, resulting in reduced flight time. Therefore, the experimental investigation of advanced control methods in a real physical environment is well justified. The objective of the present research is the comparative evaluation of seven control strategies—PID, linear quadratic controller with integral action (LQI), model predictive control (MPC), sliding mode control (SMC), backstepping control, fractional-order PID (FOPID), and H∞ control—using a single-degree-of-freedom drone test platform in a MATLAB R2023b-Arduino hardware-in-the-loop (HIL) environment. Although the theoretical advantages and model-based results of the aforementioned control methods are well documented, the number of real-time comparative HIL experiments conducted under identical physical conditions remains limited. Consequently, only a small amount of unified and directly comparable experimental data is available regarding the performance of different controllers. The measurements were performed at a reference height of 120 mm under disturbance-free conditions and under wind loading with a velocity of 10 km/h applied at an angle of 45°. The controller performance was evaluated based on hover accuracy, settling time, overshoot, and real-time measured power consumption. The results indicate that modern control strategies provide significantly improved energy efficiency and faster stabilization compared to the PID controller in both disturbance-free and wind-loaded test scenarios. The investigations confirm that several advanced controllers can be applied more effectively than the PID controller to enhance hover stability and reduce energy consumption. Full article

Cooperative control of multi-agent systems (MASs) is essential in engineering applications. However, malicious attacks and uncertainties can drive MASs to failure. Regrettably, prior work on resilient control of MASs rarely addresses uncertainties and malicious attacks concurrently. In this article, the resilient leader–follower consensus control problem is studied for non-linear MASs with cyber-physical attacks and uncertainties, and a novel resilient model reference adaptive sliding mode control (MRASMC) strategy is proposed. The stability of the MASs is proven via the Lyapunov theory, and the effectiveness of the proposed control framework is validated by numerical simulations. Full article

Carbon–metal composite NiCrFeC coatings, prepared with and without controlled oxygen addition, were investigated to evaluate the influence of oxygen on the structure, mechanical response, and tribological performance. X-ray diffraction revealed that oxygen-containing films (NiCrFeC + O₂) exhibit a mixed metallic–oxide microstructure with CrNi, CrO, and NiO phases, whereas oxygen-free coatings show only CrNi crystalline peaks. The incorporation of oxygen led to a substantial increase in nano-hardness, from 0.84 GPa for NiCrFeC to 1.59 GPa for NiCrFeC + O₂. Scratch testing up to 100 N indicated improved adhesion and higher critical loads for the oxygen-rich coatings. Tribological measurements performed under dry sliding conditions using a sapphire ball showed a significant reduction in friction: NiCrFeC + O₂ stabilized at ~0.20, while NiCrFeC exhibited values between 0.25 and 0.35 at 0.5 N and 0.4–0.5 at 1 N, accompanied by non-uniform sliding due to coating failure. Wear-track analysis confirmed shallower penetration depths and narrower wear scars for NiCrFeC + O₂, despite similar initial roughness (~35 nm). These findings demonstrate that oxygen incorporation enhances hardness, adhesion, and wear resistance while substantially lowering friction, making NiCrFeC + O₂ coatings promising for low-friction dry-sliding applications. Full article

High rock slopes are extensively distributed in areas of major engineering constructions, such as transportation infrastructure, hydraulic projects, and mining operations. The stability and failure evolution mechanism during their multi-stage excavation process have consistently been a crucial research topic in geotechnical engineering. In this paper, a series of two-dimensional rock slope models, incorporating various combinations of slope height and slope angle, were established utilizing the Discrete Element Method (DEM) software PFC2D. This systematic investigation delves into the meso-mechanical response of the slopes during multi-stage excavation. The Parallel Bond Model (PBM) was employed to simulate the contact and fracture behavior between particles. Parameter calibration was performed to ensure that the simulation results align with the actual mechanical properties of the rock mass. The research primarily focuses on analyzing the evolution of displacement, the failure modes, and the changing characteristics of the force chain structure under different geometric conditions. The results indicate that as both the slope height and slope angle increase, the inter-particle deformation of the slope intensifies significantly, and the shear band progressively extends deeper into the slope mass. The failure mode transitions from shallow localized sliding to deep-seated overall failure. Prior to instability, the force chain system exhibits an evolutionary pattern characterized by “bundling–reconfiguration–fracturing,” serving as a critical indicator for characterizing the micro-scale failure mechanism of the slope body. Full article

This study proposes a novel high-performance computational framework to address the computational challenges in probabilistic large-deformation landslide analysis. By integrating a GPU-accelerated material point method (MPM) solver with a parallelized covariance matrix decomposition (CMD) algorithm for decomposing symmetric matrices, the framework achieves exceptional efficiency, demonstrating speedups of up to 532× (MPM solver) and 120× (random field generation) compared to traditional serial methods. Leveraging this efficiency, extensive Monte Carlo simulations (MCSs) were conducted to quantify the effects of spatial variability in soil properties on landslide behaviors. Quantitative results indicate that runout and influence distances follow normal distributions, while sliding mass volume exhibits log-normal characteristics. Crucially, deterministic analysis was found to systematically underestimate the hazard; the probabilistic mean sliding volume significantly exceeded the deterministic value, with 73–80% of stochastic realizations producing larger failures. Furthermore, sensitivity analyses reveal that increasing the coefficient of variation (COV) and the cross-correlation coefficient (from −0.5 to 0.5) leads to a monotonic increase in both the mean and standard deviation of large-deformation metrics. These findings confirm that positive parameter correlation amplifies failure risk, providing a rigorous physics-based basis for conservative landslide hazard assessment. Full article

The scour protection performance of the conical structure under different slope angles, α, was investigated through numerical simulations. By solving the Navier–Stokes (N–S) equations, using the Renormalization Group (RNG) k–ε turbulence model and the Meyer-Peter and Müller (MPM) sediment transport formula, the scour protection performance, undermining process, and the flow field around the devices were fully analyzed at different slope angles. The findings indicate that the conical scour protection provides effective protection against scour damage. As the slope angle increases, greater scour depth is observed around the structure. A critical slope angle was identified between 30° and 40°, slope angle effects are obvious below the threshold; otherwise, it minimized. Undermining is the main cause of failure of such stiff scour protection, mainly driven by flow contraction and sand sliding. Upstream undermining beneath the structure is more pronounced, while the downstream undermining is largely related to the near-bed flow separation point. The critical undermining point (CUP) is proposed based on the undermining curve to distinguish the undermining state, which is critical in scour protection and structural stability. Full article

Crack type classification in concrete structures is essential for assessing structural integrity, yet traditional visual inspections and RA–AF parameter-based Acoustic Emission (AE) methods suffer from subjectivity and limited ability to capture temporal signal dependencies. This study proposes TemporalAE-Net, a self-attention-based machine learning framework designed to classify tensile and shear cracks while explicitly incorporating the temporal evolution of AE signals. AE data were collected from axial tension tests, shear-failure tests, and four-point bending tests on reinforced concrete beams, and a sliding-window reconstruction method was used to transform sequential AE signals into two-dimensional temporal matrices. TemporalAE-Net integrates one-dimensional convolution for local feature extraction and multi-head self-attention for global temporal correlation learning, followed by multilayer perceptron classification. The proposed model achieved an accuracy of 99.72%, outperforming both its ablated variants without convolutional or attention modules and conventional time-series architectures. Generalization tests on 12 unseen specimens yielded 100% correct classifications, and predictions for reinforced concrete beams closely matched established crack-evolution patterns, with shear cracks detected approximately 15 s prior to visual observation. These results demonstrate that TemporalAE-Net effectively captures temporal dependencies in AE signals. Moreover, it provides accurate and efficient tensile–shear crack identification, making it suitable for real-time structural health monitoring applications. Full article