Search Results (72)

Mixed ground conditions cause sudden fluctuations in the tunnel boring machine (TBM) advance rate (AR). Accurate forecasting is necessary for tunneling safety. Existing data-driven models, however, often treat the excavation process as an isolated event. They ignore the physical memory effect of rock–machine interactions. They also lack the ability to diagnose abnormal AR drops. To address these issues, an interpretable forecasting framework is proposed. First, a Selection–Processing (SP) system is established to standardize data handling and quantify geological heterogeneity. Second, a Time-Series Structure (TSS) network is developed to construct a one-ring-ahead input block using the current completed-ring state and CCF/PACF-guided historical windows. The framework is validated on the Shenzhen–Dayawan Intercity Line. The optimized GWO-LSTM model achieves high accuracy (R² = 0.977, MAE = 2.15, RMSE = 3.07). Compared with the no-TSS reference scheme, the MAE and RMSE decrease from 2.7081 and 3.6045 to 2.1496 and 3.0724, respectively. Furthermore, Shapley Additive Explanations (SHAP) are applied for ring-by-ring anomaly diagnosis. Local SHAP analysis indicates that both current-state variables and selected lagged variables provide diagnostic information for AR fluctuations. The identified lags are interpreted as project-specific memory indicators rather than universal physical delay constants. This method provides model-based diagnostic clues for associating sudden AR drops with specific operational or geological factors. The proposed framework provides a transparent and practical tool for TBM performance prediction and field diagnosis. Full article

With the transition to deep coal mining, the transparent detection of hidden geological hazards in the floor strata is fundamental for production safety. In mine seismic exploration, gliding waves—inhomogeneous plane waves propagating along the coal–rock interface—offer a unique advantage for penetrating high-velocity floors via the skin effect, overcoming the total reflection limitations of conventional in-seam waves. This study investigates the propagation laws and anomaly response characteristics of floor gliding waves using super-critical incidence theory and high-order staggered-grid finite difference simulations. The results demonstrate that the apparent velocities of gliding P and S-waves are bounded by those of the coal and host rock, exhibiting minimal dispersion. Quantitative analysis using a penetration depth model reveals that while penetration depth is frequency-dependent—with lower frequencies providing deeper reach—high-frequency components remain essential for high-resolution imaging. Crucially, the proposed method was validated through a field Case Study at the 11123 working face. By utilizing a specialized deep-hole excitation strategy to ensure super-critical incidence, the inversion successfully identified a hidden fault extending up to 60 m below the floor, which was subsequently confirmed by rock roadway excavation. These findings establish a robust physical basis for designing underground floor-detection systems and provide a significant theoretical reference for addressing detection blind spots in deep mining environments. Full article

Objective: Rho-associated protein kinase (ROCK) inhibitors have recently emerged as promising agents for the treatment of corneal endothelial dysfunction. Because corneal transparency critically depends on endothelial cell function, endothelial failure can lead to persistent visual impairment. However, clinical evidence regarding the use of topical ROCK inhibition in various etiologies of endothelial decompensation remains limited. The aim of this study was to evaluate changes in central corneal thickness (CCT), best-corrected visual acuity (BCVA), and treatment-related adverse events in eyes with corneal edema of different etiologies treated with fixed-combination drops of netarsudil 0.02%/latanoprost 0.005%, Roclanda^®. Methods: In this prospective, uncontrolled, exploratory case series, we investigated the effects of topical ROCK inhibition on corneal endothelial cell function in 13 eyes of 11 patients with persistent, nonhealing corneal edema following intraocular procedures. Patients were treated with topical Roclanda^® once daily for three months. Clinical evaluation included BCVA, CCT, and safety assessment. Changes in CCT and BCVA were assessed before therapy, and after 1 and 3 months of treatment. Results: Mean baseline CCT was 782.8 µm and decreased significantly by 71.0 µm at 1 month and by 120.2 µm at 3 months (p = 0.0074 and 0.0012, respectively). Complete resolution of corneal edema was achieved in 38% of eyes. Mean BCVA improved from 0.744 before treatment to 0.518 logMAR at 3 months (p = 0.0026), with 46.2% of eyes gaining two or more Snellen lines. The analysis including only one eye per patient showed similar results, with statistically significant reductions in CCT at both 1 and 3 months and a significant improvement in BCVA at 3 months after the exclusion of the second eye in bilaterally included patients. Treatment was well tolerated; with mild conjunctival hyperemia as the most common adverse effect, while reticular epithelial corneal edema occurred in one eye and resolved after the completion of the treatment. Conclusions: In this prospective, exploratory case series of patients with nonhealing corneal edema, 3 months of a fixed-dose netarsudil 0.02%/latanoprost 0.005% treatment resulted in significant reduction in CCT, as well as clinically important improvement in BCVA. These exploratory findings cannot explain the mechanism of action, but suggest a potential therapeutic role for ROCK inhibitors in eyes with nonhealing corneal edema and possibly residual endothelial reserve. Larger controlled studies are needed to confirm these observations and further define indications for treatment. Full article

Aiming to address the challenge of the high-precision monitoring of underground coal and rock fractures, this paper proposes and verifies a roadway full-section synchronous monitoring method utilizing a Wi-Fi wireless sensor network. To address the inherent difficulties of detecting complex rock mass fractures through surface sensors, our methodology employs a synchronized array of surface-mounted vibration sensors covering key mechanical structural points. The feasibility of this approach is technically substantiated through the strict implementation of rigid coupling techniques—utilizing industrial-grade epoxy resin and customized metal mechanical fixtures—combined with hardware low-pass filtering to eliminate air gap attenuation and maximize the signal-to-noise ratio. Using this validated setup, we successfully extracted and manually verified 63 high-fidelity rupture events. The data reliability is further demonstrated through a comprehensive Python-based processing pipeline that calculates 17-dimensional time–frequency characteristics. Statistical analysis confirms that the extracted data strictly conforms to the physical laws of rock fracture, evidenced by a significant negative correlation between maximum amplitude and dominant frequency (r = −0.84, p < 0.001). Unsupervised clustering of these signals reveals excellent inter-class separability. By transparently substantiating the data acquisition and verification process, this study provides a publicly shared pilot dataset and methodology for algorithm evaluation and preliminary dynamic disaster mechanism exploration. Full article

Accurate characterization of rock mechanical parameters in heterogeneous geological formations remains challenging because lithological variations alter the relationship between logging signals and geomechanical responses. Existing approaches, including empirical formulas, pure machine learning models, and feature-augmented learning methods, often compress these variations into a single predictor, which can lead to biased estimates. To address this issue, this study proposes a heterogeneity-aware residual learning framework for rock mechanical parameter characterization from well logs. The method separates the prediction into a global component and a lithotype-conditioned correction, allowing lithological effects to be represented as structured residual behavior. This framework was developed and validated on deep coal-bearing formations in the Ordos Basin. By accounting for lithology-controlled response shifts, it produces predictions that better follow observed geological controls. Cross-well validation demonstrates reduced lithotype-induced bias and stable generalization within the studied formation. Further analysis shows that the performance gain is linked to the residual decomposition structure rather than to the addition of lithotype information alone. Compared with single-stage feature augmentation, the main advantage of the proposed framework is its ability to reduce systematic bias in lithological transition zones while preserving a transparent global–residual structure. Its demonstrated applicability is limited to wells within the studied coal-bearing formation, and broader transferability requires further validation. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

Reliable preliminary assessment of stress redistribution and rock mass stability is a critical step in tunnel design, providing guidance before detailed numerical modeling and support design are undertaken. This study presents RockStressCalc, a Python-based computational framework that integrates classical elastic stress–displacement analysis with empirical rock mass strength evaluation for circular tunnels within a transparent analytical workflow. The tool combines Kirsch’s closed-form solution for stress redistribution around a circular opening under anisotropic in situ stress conditions with the generalized Hoek–Brown criterion to enable spatially resolved evaluation of elastic strength reserve. The framework assumes a homogeneous, isotropic, linear–elastic rock mass under plane strain conditions and introduces a Stability Factor as a stress-based indicator of proximity to initial yield. The analytical implementation is verified against finite-element simulations performed in Plaxis2D under equivalent elastic assumptions. The maximum stress difference at the excavation boundary remained below 10%, while displacement deviations were below approximately 4%. In addition, comparison between the analytical far-field Stability Factor and the numerical strength reduction multiplier demonstrated close agreement, confirming consistency between the analytical and finite-element formulations under uniform stress conditions. The results show that RockStressCalc provides a computationally efficient analytical baseline suitable for rapid parametric evaluation, sensitivity studies, educational use, and independent verification of numerical models in early-stage tunnel design. By emphasizing explicit coupling of stress redistribution and strength evaluation within a reproducible framework, rather than introducing new constitutive models, the proposed approach offers practical engineering value as a screening and benchmarking tool and provides a foundation for future probabilistic or extended tunnel stability analyses. Full article

Geologic carbon storage (GCS) is expanding rapidly as a cornerstone decarbonization option, but its climate value depends on maintaining long-term containment of CO₂ and displaced formation brine. Legacy wells—many drilled and abandoned before modern barrier standards—remain one of the most credible and controllable pathways for unintended upward migration. To support transparent, fit-for-purpose risk screening, this study benchmarks three leakage-modeling philosophies across a common six-layer scenario: (i) a reservoir-scale analytical solution for layered aquifers, (ii) a semi-analytical pressure-transient model that captures rock–fluid compressibility and breakthrough time, and (iii) a new mechanistic wellbore-scale model that explicitly represents dominant annular failure pathways (micro-annuli, cement fractures, casing breaches, and cement–formation interface flow) with pathway-specific hydraulic losses. Results show that model choice and physics assumptions drive order-of-magnitude differences in predicted brine rates: after 1000 days, the analytical model predicts ~1.7 bbls/day, the pressure-transient model exceeds 8 bbls/day, whereas the mechanistic model yields damage-dependent outcomes (~0.2–0.4 bbls/day for moderate–severe cement damage and up to ~3.5 bbls/day for open-channel conditions). These findings demonstrate that neglecting wellbore hydraulic resistance can systematically overstate leakage risk, while mechanistic pathway representation enables more realistic, condition-dependent screening. Future work will focus on model calibration to field/monitoring data, probabilistic parameterization of defect geometries, and extension to multiphase/reactive leakage to support operational decision-making and regulatory assurance. Full article

This systematic review synthesizes passive and passive-first cooling strategies for greenhouses in hot–arid climates, organizing evidence across four domains: Airflow & Ventilation, Shading & Radiative Control, Thermal Storage & Ground Coupling, and Structural Design & Geometry. Drawing on the project corpus, we analyze 10–13 distinct techniques including ridge and side natural ventilation, windcatchers and solar chimneys, external shade nets, NIR-selective and transparent radiative-cooling films, and dynamic PV shading; earth-to-air heat exchangers (EAHE/GAHT), rock-bed sensible storage, phase-change materials (PCMs), and sunken or buried envelopes; as well as roof slope and shape, span number, and orientation. Across studies, cooling outcomes are reported as peak or daytime indoor air temperature reductions, defined relative either to outdoor conditions or to a control greenhouse, with the reference frame and temporal aggregation specified in the synthesis. Typical outcomes include ≈3–7 °C daytime reduction for optimized ventilation, ≈2–4 °C for shading and spectral covers while preserving PAR, ≈5–7 °C intake cooling for EAHE with winter pre-heating, and up to ≈14 °C peak attenuation for rock-bed storage under favorable conditions. Structural choices consistently amplify these effects by sustaining pressure head and limiting thermal heterogeneity. Performance is strongly context-dependent—governed by wind regime, diurnal amplitude, dust and UV exposure, and crop-specific light and temperature thresholds—and the most robust results arise from stacked, site-specific designs that combine skin-level radiative rejection, buoyancy-supportive geometry, and ground or latent buffering with minimal active backup. Smart controllers that modulate vents, shading, and targeted fogging or fans based on VPD or temperature differentials improve stability and reduce water and energy use by engaging actuation only when passive capacity is exceeded. We recommend standardized composite metrics encompassing temperature moderation, humidity stability, PAR availability, and water and energy use per unit yield to enable fair cross-study comparison, multi-season validation, and policy adoption. Collectively, the synthesized techniques provide a practical palette for improved greenhouse climate management under hot and arid conditions. Full article

This paper examines the role of digital technologies in advancing archeological research, using two complex, stratified case studies—the Villa Reale of Haghia Triada in Crete and the rock-cut churches of Pantalica in Sicily—as reference points. Both sites, despite their geographical and chronological differences, present comparable challenges: fragmented evidence, incomplete documentation, and the need for multi-scalar interpretation. By integrating photogrammetry, LiDAR scanning, and other 3D acquisition techniques with 3D modeling approaches, this paper explores how digital workflows can both preserve and reinterpret fragile archeological contexts. The aim of this research is to critically assess the methodological potential and epistemological implications of these tools, emphasizing transparency, reproducibility, and their communicative value for scholarly communities and wider audiences. Full article

Rockbursts are one of the most critical geomechanical hazards during the construction of deep tunnels under high in situ stress conditions, as they can compromise worker safety, damage infrastructure, and disrupt excavation continuity. Despite extensive research on rockburst mechanisms and mitigation, the causal analysis of individual events remains challenging due to the complex interaction between seismicity, geological conditions, stress redistribution, and operational factors. This study proposes a structured and multidisciplinary methodology for the causal analysis of rockbursts in deep high-stress tunnels. The methodology integrates seismicity analysis, geological and geotechnical characterization, operational assessment, field damage inspection, and hypothesis-driven interpretation to systematically reconstruct the sequence of processes leading to rockburst occurrence. The proposed approach is applied to a rockburst that occurred in 2020 in the Conveyor Belt tunnel (TC) of the Andes Norte Project, El Teniente Division, Codelco (Chile). The event reached a local magnitude of Mw = 1.7 and caused significant damage to tunnel support systems. Results indicate that the rockburst was associated with excavation- and blasting-induced stress redistribution, leading to the activation of a sub-horizontal rupture plane and subsequent damage propagation toward the excavated tunnel. The methodology provides a transparent and adaptable analytical framework for integrating multidisciplinary data into a coherent causal interpretation. Although demonstrated using a competent and brittle rock mass, the framework can be adapted to other deep tunneling projects under high-stress conditions by adjusting the governing parameters according to site-specific geological, geomechanical, and operational characteristics. The proposed approach supports improved understanding of rockburst mechanisms and informed decision-making for seismic risk management in deep underground excavations. Full article

This study investigates the mineralogical and physical properties of serpentinite from the Monteferrato area (Tuscany, Italy) to evaluate its potential use in Tuscany architectural restoration. The research addresses the need to identify replacement materials compatible with historic stones while preserving their original features. Representative specimens from the Bagnolo quarry were analysed through physical testing and a wide range of mineralogical and geochemical techniques, including polarised light microscopy, X-ray diffraction, electron probe micro-analysis, whole-rock chemistry, and fibre quantification. The results show a mineralogical composition dominated by serpentine-group minerals and magnetite, with physical properties generally consistent across samples. Measured capillary water absorption ranges from 3.27 to 5.27 g/m²·s^0.5, open porosity from 5.25% to 8.93%, apparent densities range from 2.49 to 2.56 g/cm³, and imbibition coefficient from 2.16% to 3.71%. Comparative analysis with serpentinite from historic sources (Figline di Prato quarry, Tuscany) and from monuments (Baptistery of San Giovanni, Florence) demonstrates close compositional and textural affinities, supporting the suitability of the rock from the studied quarry for restoration purposes in Tuscany monuments. However, chrysotile concentrations up to 14,153 mg/kg, exceeding Italian regulatory thresholds, represent a critical limitation. This not only requires the implementation of strict safety measures but also raises serious concerns regarding the practical feasibility of using this stone in conservation projects. More broadly, the presence of asbestiform minerals in serpentinites highlights a significant and often underestimated health risk associated with their extraction, processing, and use. Despite its importance, detailed fibre count data are rarely published or made publicly accessible, hindering both transparent risk assessment and informed decision-making. By integrating petrographic, mineralogical, and physical–mechanical characterisation with fibre quantification, this study not only assesses the technical suitability of Monteferrato serpentinites for restoration of Tuscan monuments but also contributes to a more responsible and evidence-based approach to their use, emphasising the urgent need for transparency and health protection in conservation practices. Full article

Data-driven modeling methods have been preliminarily applied in the development of tight-gas reservoirs, demonstrating unique advantages in post-fracturing productivity prediction. However, most of the established predictive models are “black-box” models, which provide productivity predictions based on a set of input parameters without revealing the internal prediction mechanisms. This lack of transparency reduces the credibility and practical utility of such models. To address the challenges of poor performance and low trustworthiness of “black-box” machine learning models, this study explores a data-driven approach to “black-box” predictive modeling by integrating ensemble learning with interpretability methods. The results indicate the following: The post-fracturing productivity prediction model for tight-gas reservoirs developed in this study, based on ensemble learning, achieves a goodness of fit of 0.923, representing a 26.09% improvement compared to the best-performing individual machine learning model. The stacking ensemble model predicts post-fracturing productivity for horizontal wells more accurately and effectively mitigates the prediction biases of individual machine learning models. An interpretability method for the “black-box” ensemble learning-based productivity prediction model was established, revealing the ranked importance of factors influencing post-fracturing productivity: reservoir properties, controllable operational parameters, and rock mechanics. This ranking aligns with the results of orthogonal experiments from mechanism-driven numerical models, providing mutual validation and enhancing the credibility of the ensemble learning-based productivity prediction model. In conclusion, this study integrates mechanistic numerical models and data-driven models to explore the influence of various factors on post-fracturing productivity. The cross-validation of results from both approaches underscores the reliability of the findings, offering theoretical and methodological support for the design of fracturing schemes and the iterative advancement of fracturing technologies in tight-gas reservoirs. Full article

Technological advancements, shifting consumer preferences, and societal changes drive the cosmetics industry to evolve continuously. The cosmetics industry is experiencing a renaissance, with new ingredients that are more environmentally friendly, natural, and transparent in terms of sourcing and manufacturing and, last but not least, which are also multifunctional. The use of technology in cosmetics has been rising, including AI (artificial intelligence) and AR (augmented reality) for virtual try-ons, skin analysis tools, and smart beauty devices that provide at-home skincare treatments. Meanwhile, fermented cosmetic ingredients are becoming increasingly popular in the beauty industry due to their improved efficacy and skin benefits. The benefits include enhanced absorption, improved stability (due to the self-produced preservatives), microbiome-friendliness (supporting the skin’s microbiome), and anti-inflammatory and soothing effects. The most common cosmetic ingredients produced by microorganisms are fermented rice, soy, green tea, fruits, and vegetables. Our laboratory investigates a mineral rock called alginite, which has shown many benefits in other fields, such as agriculture and cosmetics (e.g., as a facemask). It has been proven that alginite combined with LAB (lactic acid-producing bacteria) probiotics is beneficial for health and can increase biomass production. However, cell lysates with alginite have never been investigated for cosmetic purposes. This study aimed to investigate the potential of alginite, a mineral rock, in enhancing the cosmetic properties of LAB lysates, specifically focusing on antioxidant effects, skin-whitening properties, and, in preliminary tests, skin-moisturising effects. LAB strains were cultured with and without alginite, and the resulting cell lysates were analysed for these cosmetic applications. The preliminary results suggested that alginite may boost the hydrating effect of LAB lysate, increasing it tenfold compared to LAB lysate alone. The antioxidant effect was enhanced fivefold in the case of Lactobacillus acidophilus when cultured with alginite. However, no significant effect was observed on mushroom tyrosinase inhibition, suggesting no impact on pigment formation. Further research is needed to fully understand the mechanisms underlying these effects and to explore potential applications in cosmetic formulations. Limitations of this study include the focus on specific LAB strains and the need for in vivo studies to confirm the observed effects on human skin. Full article

Accurate and efficient rock mass characterization is essential in geotechnical engineering, yet traditional tunnel face mapping remains time consuming, subjective, and potentially hazardous. Recent advances in digital technologies and AI offer automation opportunities, but many existing solutions are hindered by slow 3D scanning, computationally intensive processing, and limited integration flexibility. This paper presents Tunnel Rapid AI Classification (TRaiC), an open-source MATLAB-based platform for rapid and automated tunnel face mapping. TRaiC integrates single-shot 360° panoramic photography, AI-powered discontinuity detection, 3D textured digital twin generation, rock mass discontinuity characterization, and Retrieval-Augmented Generation with Large Language Models (RAG-LLM) for automated geological interpretation and standardized reporting. The modular eight-stage workflow includes simplified 3D modeling, trace segmentation, 3D joint network analysis, and rock mass classification using RMR, with outputs optimized for Geo-BIM integration. Initial evaluations indicate substantial reductions in processing time and expert assessment workload. Producing a lightweight yet high-fidelity digital twin, TRaiC enables computational efficiency, transparency, and reproducibility, serving as a foundation for future AI-assisted geotechnical engineering research. Its graphical user interface and well-structured open-source code make it accessible to users ranging from beginners to advanced researchers. Full article