Search Results (1,371)

To reveal the meso-mechanical essence of deep rock mass failure and capture precursor information, this study focuses on soft rock failure mechanisms. Based on the discontinuous medium discrete element method (DEM), we employed digital image correlation (DIC) technology, acoustic emission (AE) monitoring, and particle flow code (PFC) numerical simulation to investigate the failure evolution characteristics and AE quantitative representation of soft rocks. Key findings include the following: Localized high-strain zones emerge on specimen surfaces before macroscopic crack visualization, with crack tip positions guiding both high-strain zones and crack propagation directions. Strong force chain evolution exhibits high consistency with the macroscopic stress response—as stress increases and damage progresses, force chains concentrate near macroscopic fracture surfaces, aligning with crack propagation directions, while numerous short force chains coalesce into longer chains. The spatial and temporal distribution characteristics of acoustic emissions were explored, and the damage types were quantitatively characterized, with ring-down counts demonstrating four distinct stages: sporadic, gradual increase, stepwise growth, and surge. Shear failures predominantly occurred along macroscopic fracture surfaces. At the same time, there is a phenomenon of acoustic emission silence in front of the stress peak in the surrounding rock of deep soft rock roadway, as a potential precursor indicator for engineering disaster early warning. These findings provide critical theoretical support for deep engineering disaster prediction. Full article

To compensate for the instability of renewable energy sources during China’s energy transition, large thermal power plants must provide critical operational flexibility, primarily through deep peaking. To investigate the combustion performance and wear and tear of a 600 MW pulverized coal boiler under deep peaking, the gas–solid flow characteristics and distributions of flue gas temperature, wall heat flux, and wall wear rate in a 600 MW tangentially fired pulverized coal boiler under variable loads (353 MW, 431 MW, 519 MW, and 600 MW) are investigated in this study employing computational fluid dynamics numerical simulation method. Results demonstrate that increasing the boiler load significantly amplifies gas velocity, wall heat flux, and wall wear rate. The maximum gas velocity in the furnace rises from 20.9 m·s⁻¹ (353 MW) to 37.6 m·s⁻¹ (600 MW), with tangential airflow forming a low-velocity central zone and high-velocity peripheral regions. Meanwhile, the tangential circle diameter expands by ~15% as the load increases. The flue gas temperature distribution exhibits a “low-high-low” profile along the furnace height. As the load increases from 353 MW to 600 MW, the primary combustion zone’s peak temperature rises from 1750 K to 1980 K, accompanied by a ~30% expansion in the coverage area of the high-temperature zone. Wall heat flux correlates strongly with temperature distribution, peaking at 2.29 × 10⁵ W·m⁻² (353 MW) and 2.75 × 10⁵ W·m⁻² (600 MW) in the primary combustion zone. Wear analysis highlights severe erosion in the economizer due to elevated flue gas velocities, with wall wear rates escalating from 3.29 × 10⁻⁷ kg·m⁻²·s⁻¹ (353 MW) to 1.23 × 10⁻⁵ kg·m⁻²·s⁻¹ (600 MW), representing a 40-fold increase under full-load conditions. Mitigation strategies, including ash removal optimization, anti-wear covers, and thermal spray coatings, are proposed to enhance operational safety. This work provides critical insights into flow field optimization and wear management for large-scale coal-fired boilers under flexible load operation. Full article

The Qiubudong silver deposit on the western margin of the Fuping ore cluster in the central North China Craton is a representative breccia-type deposit characterized by relatively high-grade ores, thick mineralized zones, and extensive alteration, indicating considerable potential for economic resource development and further exploration. Previous studies on this deposit have not addressed its genetic mineralogical characteristics. This study focuses on pyrite and quartz to investigate their typomorphic features, such as crystal morphology, trace element composition, thermoelectric properties, and luminescence characteristics, and their implications for ore-forming processes. Pyrite crystals are predominantly cubic in early stages, while pentagonal dodecahedral and cubic–dodecahedral combinations peak during the main mineralization stage. The pyrite is sulfur-deficient and iron-rich, enriched in Au, and relatively high in Ag, Cu, Pb, and Bi contents during the main ore-forming stage. Rare earth element (REE) concentrations are low, with weak LREE-HREE fractionation and a strong negative Eu anomaly. The thermoelectric coefficient of pyrite ranges from −328.9 to +335.6 μV/°C, with a mean of +197.63 μV/°C; P-type conduction dominates, with an occurrence rate of 58%–100% and an average of 88.78%. A weak–low temperature and a strong–high temperature peak characterize quartz thermoluminescence during the main mineralization stage. Fluid inclusions in quartz include liquid-rich, vapor-rich, and two-phase types, with salinities ranging from 10.11% to 12.62% NaCl equiv. (average 11.16%) and densities from 0.91 to 0.95 g/cm³ (average 0.90 g/cm³). The ore-forming fluids are interpreted as F-rich, low-salinity, low-density hydrothermal fluids of volcanic origin at medium–low temperatures. The abundance of pentagonal dodecahedral pyrite, low Co/Ni ratios, high Cu contents, and complex quartz thermoluminescence signatures are key mineralogical indicators for deep prospecting. Combined with thermoelectric data and morphological analysis, the depth interval around 800 m between drill holes ZK3204 and ZK3201 has high mineralization potential. This study fills a research gap on the genetic mineralogy of the Qiubudong deposit and provides a scientific basis for deep exploration. Full article

Vertical GaN P-i-N diodes exhibit excellent high-voltage performance, fast switching speed, and low conduction losses, making them highly attractive for power applications. However, their breakdown voltage is severely constrained by electric field crowding at device edges. Using silvaco tcad (2019) tools, this work systematically evaluates multiple edge termination techniques, including deep-etched mesa, beveled mesa, and field-plate configurations with both vertical and inclined mesa structures. We present an optimized multi-drift-layer GaN P-i-N diode incorporating field-plate termination and analyze its electrical performance in detail. This study covers forward conduction characteristics including on-state voltage, on-resistance, and their temperature dependence, reverse breakdown behavior examining voltage capability and electric field distribution under different temperatures, and switching performance addressing both forward recovery phenomena, i.e., voltage overshoot and carrier injection dynamics, and reverse recovery characteristics including peak current and recovery time. The comprehensive analysis offers practical design guidelines for developing high-performance GaN power devices. Full article

The Mutanchis, known by their derogatory exonymic term ‘Lepcha’, are autochthonous to Sikkim, India. The name ‘Mutanchi’ derives from the phrase ‘Mutanchi Rumkup Rongkup’, eliciting the response ‘Achulay’, meaning ‘Beloved children of It-bu-mu, who have come from the snowy peaks’. The nomenclature prompts an ontological understanding rooted in the community’s eco-geographical context. Despite possessing a well-developed script categorised within the Tibeto-Burman language family, the Mutanchis remain a largely oral community. Their diminishing, scarcely documented repository of Mutanchi clan narratives underscores this orality. As a Mutanchi, I recognise these narratives as a medium for expressing Indigenous value systems upheld by my community and specific villages. Mutanchi clan narratives embody spiritual and cultural significance, yet their fantastic rationale reveals complex epistemological tensions. Ideally, each Mutanchi clan reveres a chyu (peak), lhep (cave), and doh (lake), which are propitiated annually and on specific occasions. The transmigration of an apil (soul) is tied to these three sacred spatial geographies, unique to each clan. Additionally, clan etiological explanations, situated within natural or supernatural habitats, manifest beliefs, values, and norms rooted in a deep ecology. This article presents an ecosophical study of selected Mutanchi clan narratives from Dzongu, North Sikkim—a region that partially lies within the UNESCO Khangchendzonga Man-Biosphere Reserve. Conducted in close consultation with clan members and in adherence to the ethical protocols, this study examines clans in Dzongu governed by Indigenous knowledge systems embedded in their narratives, highlighting biocentric perspectives that shape Mutanchi lifeways. Full article

Stylolites are ubiquitous diagenetic products in carbonate rocks. They play a significant role in enhancing or reducing fluid flow in subsurface reservoirs. This study unravels the relationship between stylolite networks, carbonate microfacies, and the elemental geochemistry of Upper Cretaceous limestones of the Kometan Formation (shallow to moderately deep marine) in Northern Iraq. Stylolites exhibit diverse morphologies across mud- and grain-supported limestone facies. Statistical analyses of stylolite spacing, wavelength, amplitude, and their intersections and connectivity indicate that grain size, sorting, and mineral composition are key parameters that determine the geometrical properties of the stylolites and stylolite networks. Stylolites typically exhibit weak connectivity and considerable vertical spacing when hosted in packstone facies with moderate grain sorting. Conversely, mud-supported limestones, marked by poor sorting and high textural heterogeneity, host well-developed stylolite networks characterized by high amplitude and frequent intersections, indicating significant dissolution and deformation processes. Stylolites in mud-supported facies are closely spaced and present heightened amplitudes and intensified junctions, with suture and sharp-peak type. This study unveils that stylolites can potentially enhance porosity in the studied formation. Full article

Asphalt–rubber binders (A-RBs) have a long and deep history of use; however, little is known regarding the interrelated chemical behaviors and miscibility of rubber with the asphalt fractions [saturates, aromatics, resins, and asphaltenes (SARA)]. This study comprehensively attempted to address this knowledge deficiency by employing Fourier transform infrared spectroscopy (FTIR) to investigate the chemical evolution of A-RBs. A-RB interacted at 190 °C and 3000 min⁻¹ for 8 h was deemed to have the optimal rheological performance. FTIR of the liquid fractions of A-RB 190–3000 showed a prominent chemical shift in the SARA fractions, with new peaks that showed rubber polybutadiene (PB) and polystyrene migration into asphaltenes. Meanwhile, decreases in peaks with C–H aromatic bending and S=O stretching for the A-RB 190–3000 saturates showed that the rubber absorbed low-molecular-weight maltenes during swelling. Peaks associated with C=C aromatic appeared in saturates and aromatics, respectively, emphasizing that unsaturated components migrated from the rubber into the asphalt. Thermal analysis showed that rubber dissolution for this sample reached 82%. While a PB peak existed in asphaltenes of A-RB 220–3000, its intensity was diminished by depolymerization, thus compromising the integrity of the migrated rubber structure and generating less rheological enhancement. This study concludes that FTIR characterization of SARA fractions offers valuable insights into the interactions between asphalt and rubber, and that regulated processing conditions are essential for enhancing binder performance. Full article

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods—including wavelet transforms, moving averages, and low-pass filtering—exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (±20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10–30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15–25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing. Full article

Background/Objectives: Vertebral degenerative features are promising but often subjectively scored indicators for adult age estimation. We evaluated an objective surface roughness metric, the “average distance to the fitted ellipse” score (DS), calculated automatically for every vertebra from C7 to S1 on routine CT images. Methods: CT scans of 176 adults (94 males, 82 females; 21–94 years) were retrospectively analyzed. For each vertebra, the mean orthogonal deviation of the anterior superior endplate from an ideal ellipse was extracted. Sex-specific multiple linear regression served as a baseline; support vector regression (SVR), random forest (RF), k-nearest neighbors (k-NN), and Gaussian naïve-Bayes pseudo-regressor (GNB-R) were tuned with 10-fold cross-validation and evaluated on a 20% hold-out set. Performance was quantified with the standard error of the estimate (SEE). Results: DS values correlated moderately to strongly with age (peak r = 0.60 at L3–L5). Linear regression explained 40% (males) and 47% (females) of age variance (SEE ≈ 11–12 years). Non-parametric learners improved precision: RF achieved an SEE of 8.49 years in males (R² = 0.47), whereas k-NN attained 10.8 years (R² = 0.45) in women. Conclusions: Automated analysis of vertebral cortical roughness provides a transparent, observer-independent means of estimating adult age with accuracy approaching that of more complex deep learning pipelines. Streamlining image preparation and validating the approach across diverse populations are the next steps toward forensic adoption. Full article

The Jidong Oilfield No. 2 Underground Gas Storage (UGS), located in an active fault zone in Northern China, is a key facility for ensuring natural gas supply and peak regulation in the Beijing–Tianjin–Hebei region. To evaluate the effectiveness of a combined surface and shallow borehole monitoring system under deep reservoir conditions, a 90-day microseismic monitoring trial was conducted over a full injection cycle using 16 surface stations and 1 shallow borehole station. A total of 35 low-magnitude microseismic events were identified and located using beamforming techniques. Results show that event frequency correlates positively with wellhead pressure variations instead of the injection volume, suggesting that stress perturbations predominantly control microseismic triggering. Events were mainly concentrated near the bottom of injection wells, with an average location error of approximately 87.5 m and generally shallow focal depths, revealing limitations in vertical resolution. To enhance long-term monitoring performance, this study recommends deploying geophones closer to the reservoir, constructing a 3D velocity model, applying AI-based phase picking, expanding array coverage, and developing a microseismic-injection coupling early warning system. These findings provide technical guidance for the design and deployment of long-term monitoring systems for deep reservoir conversions into UGS facilities. Full article

Against the backdrop of the increasing penetration rate of new energy year by year, power systems face a continuously growing demand for flexibility. Under the structure of such a new power system, it is essential not only to introduce diverse flexible power sources but also to explore the flexible regulation capabilities of existing conventional power sources. To fully utilize the flexibility of thermal power units (TPUs), this study proposes a real-time optimal scheduling strategy for a wind–thermal energy-storage integrated system with an adaptive time division and variable objectives. Based on the evaluation results of the real-time flexible supply–demand relationship within a regional power grid, the operation modes of TPUs are categorized into three types: economic mode, peak shaving mode, and coordination mode. For each operation mode, corresponding optimization objectives are defined, and an energy storage control strategy is developed to assist in the peak shaving of TPUs. While effectively harnessing the flexibility of TPUs, the proposed method reduces both the frequency and capacity of TPUs entering deep peak shaving. Using data from a province in Northwest China as a case study, simulation calculations and analyses demonstrate that the proposed method increases renewable energy consumption by 314.37 MWh while decreasing system economic benefits by CNY 129,000. Compared with traditional scheduling methods for TPUs to accommodate renewable energy, the system benefit increases by CNY 297,000, and an additional 13.53 MWh of peak wind power is accommodated. These results confirm that the proposed scheduling strategy can significantly enhance the system’s ability to integrate new energy while maintaining its economic efficiency. Full article

This study investigates the application of the Dueling Double Deep Q-Network (3DQN) algorithm to optimize traffic signal control at a major urban intersection in Danang City, Vietnam. The objective is to enhance signal timing efficiency in response to mixed traffic flow and real-world traffic dynamics. A simulation environment was developed using the Simulation of Urban Mobility (SUMO) software version 1.11, incorporating both a fixed-time signal controller and two 3DQN models trained with 1 million (1M-Step) and 5 million (5M-Step) iterations. The models were evaluated using randomized traffic demand scenarios ranging from 50% to 150% of baseline traffic volumes. The results demonstrate that the 3DQN models outperform the fixed-time controller, significantly reducing vehicle delays, with the 5M-Step model achieving average waiting times of under five minutes. To further assess the model’s responsiveness to real-time conditions, traffic flow data were collected using YOLOv8 for object detection and SORT for vehicle tracking from live camera feeds, and integrated into the SUMO-3DQN simulation. The findings highlight the robustness and adaptability of the 3DQN approach, particularly under peak traffic conditions, underscoring its potential for deployment in intelligent urban traffic management systems. Full article

Climate change and air pollution are reshaping viral circulation patterns and increasing host vulnerability, amplifying the burden of respiratory illness in early childhood. This narrative review synthesizes current evidence on how environmental exposures, particularly to nitrogen dioxide, ozone, and fine particulate matter, contribute to the incidence and severity of bronchiolitis, with a focus on biological mechanisms, epidemiological trends, and public health implications. Bronchiolitis remains one of the leading causes of hospitalization in infancy, with Respiratory Syncytial Virus (RSV) being responsible for the majority of severe cases. Airborne pollutants penetrate deep into the airways, triggering inflammation, compromising mucosal defenses, and impairing immune function, especially in infants with pre-existing vulnerabilities. These interactions can intensify the clinical course of viral infections and contribute to more severe disease presentations. Children in urban areas exposed to high levels of traffic-related emissions are disproportionately affected, underscoring the need for integrated public health interventions. These include stricter emission controls, urban design strategies to reduce exposure, and real-time health alerts during pollution peaks. Prevention strategies should also address indoor air quality and promote risk awareness among families and caregivers. Further research is needed to standardize exposure assessments, clarify dose–response relationships, and deepen our understanding of how pollution interacts with viral immunity. Bronchiolitis emerges as a sentinel condition at the crossroads of climate, environment, and pediatric health, highlighting the urgent need for collaboration across clinical medicine, epidemiology, and environmental science. Full article

The increasing volatility in energy markets, particularly in Greece where electricity costs reached a peak of 236 EUR/MWh in 2022, underscores the urgent need for accurate short-term load forecasting models. In this study, the application of deep learning techniques, specifically Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to forecast hourly electricity demand is investigated. The proposed models were trained on historical load data from the Greek power system spanning the years 2013 to 2016. Various deep learning architectures were implemented and their forecasting performances using statistical metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were evaluated. The experiments utilized multiple time horizons (1 h, 2 h, 24 h) and input sequence lengths (6 h to 168 h) to assess model accuracy and robustness. The best performing GRU model achieved an RMSE of 83.2 MWh and a MAPE of 1.17% for 1 h ahead forecasting, outperforming both LSTM and RNN in terms of both accuracy and computational efficiency. The predicted values were integrated into a dynamic Power BI dashboard, to enable real-time visualization and decision support. These findings demonstrate the potential of deep learning architectures, particularly GRUs, for operational load forecasting and their applicability to intelligent energy systems in a market-strained environment. Full article