Search Results (4,444)

Underground coal fires (UCFs) pose a persistent environmental and economic threat to both the built and natural worlds. In Colorado, 38 known coal fires are currently monitored by the Colorado Division of Reclamation, Mining, and Safety, many of which are in the immediate vicinity of communities and transportation infrastructure. The Axial underground coal mine fire in northwestern Colorado has been active for over 70 years and has a documented history of surface impacts, including wildfire ignition and UCF-induced slope instability near a major highway corridor. Subsurface investigations indicate active combustion in multiple coal seams, contributing to complex and evolving surface deformation. Unmanned Aerial System (UAS)-based optical surveys acquired between 2018 and 2025 were used to assess terrain changes and slope instability at the Axial site. Structure-from-motion photogrammetry was used to generate three-dimensional point clouds and orthomosaics, and surface deformation was quantified using the Multiscale Model-to-Model Cloud Comparison (M3C2) algorithm. Orthomosaic products were additionally evaluated to characterize the development of geomorphic features and cross-validate the interpretation of M3C2-derived deformation patterns. Repeat UAS surveys effectively identified changes in unstable and hazardous terrain caused by UCFs. Results reveal progressive subsidence, fracture development, and localized slope instability associated with ongoing subsurface combustion. The findings provide critical information for risk mitigation and illustrate both the capabilities and challenges of using UAS photogrammetry for long-term monitoring of geohazards associated with legacy coal mine fires. The study further emphasizes the importance of georeferencing strategies, including ground control points and real-time kinematic positioning, to ensure consistent and reliable multi-temporal change detection. Full article

Diagnosing cervical spine instability with flexion-extension radiographs is challenging, as current guidelines are based on limited cadaver studies and do not adequately account for level, vertebral size, or patient effort. There is a need for automated cervical instability metrics anchored to normative reference data, accompanied by evidence on how often abnormal findings occur in real clinical populations and which soft-tissue injury patterns they can detect. We developed and evaluated fully automated, radiographic-based cervical intervertebral motion (IVM) metrics—adapted from prior lumbar methods—using an FDA-cleared analysis pipeline that segments C2–C7 and derives rotation, translation, disc heights, and regression-based instability indices. Normative reference data were first established from flexion-extension radiographs of 341 asymptomatic volunteers after excluding radiographically degenerated levels. Abnormality prevalence was then estimated in two symptomatic cohorts: pooled preoperative clinical-trial radiographs and 881 patients with symptoms attributed to motor-vehicle accidents, excluding levels with <5° rotation to reduce unreliable data due to insufficiently stressed spines. Finally, potential diagnostic performance was assessed in a controlled cadaveric ligament-sectioning model (12 cadavers) using ROC analysis and Youden’s J thresholds. Across clinical cohorts, objective IVM abnormalities were uncommon. Prevalence increased when studies demonstrated adequate total C2–C7 motion, emphasizing the importance of patient effort. In cadavers, vertical instability metrics were most discriminative (AUC 0.96–0.97) with high sensitivity (0.89) and perfect specificity at optimal thresholds, whereas translation changed minimally with sectioning. These results support regression-based instability indices as promising candidates for standardized, physiology-guided cervical instability assessment. Full article

Estimating the post-mortem interval (PMI) using microRNAs (miRNAs) in vitreous humor (VH) is a promising technique in forensic pathology. However, the reliability of quantitative Real-Time PCR (qPCR) data in this matrix is currently constrained by a critical methodological challenge: the lack of a rigorously validated endogenous reference gene (normalizer) capable of correcting for non-biological variations without being influenced by decomposition. This study aimed to identify a robust reference gene for VH analysis by performing a comparative validation of two candidates proposed in the literature: miR-222-3p and miR-96-5p. VH samples were collected from 47 forensic autopsy cases with estimated PMIs ranging from 3 to 24 h. The validation process assessed three key parameters: amplification detectability, expression stability (Coefficient of Variation, CV), and statistical independence from both the PMI and the pre-analytical freezing interval using regression models. MiR-222-3p was rejected as a normalizer due to poor detectability, failing to reach the detection threshold (Cq < 35) in 61.7% of cases (29/47). Conversely, hsa-miR-96-5p was validated as a stable reference gene. It demonstrated high detectability and expression stability (CV = 9.07%) among valid samples. Crucially, linear regression analysis showed no significant correlation between hsa-miR-96-5p levels and either the PMI (p = 0.69) and the pre-freezing time (p = 0.70). This study demonstrates that miR-222-3p is unsuitable for forensic casework in VH due to instability. We identified and validated hsa-miR-96-5p as a robust endogenous reference gene. Its adoption is recommended to standardize future molecular thanatochronology studies and improve the accuracy of PMI estimation models. Full article

The evaluation of student performance often relies on basic spreadsheet outputs that provide limited insight into item functioning. This study presents a methodological demonstration showing how widely available spreadsheet software can be transformed into a practical environment for psychometric analysis. Using a simulated dataset of 40 students responding to 20 dichotomous items, spreadsheet formulas were developed to compute descriptive statistics and Classical Test Theory (CTT) indices, including item difficulty, discrimination, and corrected item–total correlations. The demonstration was extended to Item Response Theory (IRT) through the implementation of 1PL, 2PL, and 3PL logistic models using forward-calculated item parameters. A smaller dataset of 10 students and 10 items was used to illustrate the interpretability of the indices and the generation of Item Characteristic Curves (ICCs). Results show that spreadsheets can support teachers in in-terpreting test data beyond total scores, enabling the identification of weak items, refinement of distractors, and construction of small-scale item banks aligned with competence-based curricula. The approach contributes to Sustainable Development Goal 4 (SDG 4) by promoting accessible, equitable, and high-quality assessment practices. Limitations include the instability of IRT parameter estimation in small samples and the need for teacher training. Future research should apply the approach to real classroom data, explore automation within spreadsheet environments, and examine the integration of artificial intelligence for adaptive assessment. Full article

Volatile phytochemicals such as perillaldehyde (PAH) exhibit antimicrobial and anti-inflammatory activities relevant to wound repair; however, topical use is limited by volatility, chemical instability, and potential irritation associated with burst exposure. Here, we developed a nano-in-hydrogel dressing by encapsulating PAH into lipid nanoparticles (PAH-L) and incorporating them into a carbomer hydrogel (PAH-L-G). PAH-L showed a uniform nanoscale size distribution, high encapsulation efficiency, and good colloidal stability. After gel incorporation, PAH-L-G formed an interconnected porous network with rapid swelling and a more sustained release profile than free PAH or PAH-L. Hemocompatibility and cytocompatibility assays indicated low hemolysis and high fibroblast viability. In a full-thickness rat wound model, PAH-L-G accelerated wound closure and improved histological regeneration without obvious local irritation. Overall, the lipid-nanoparticle-in-hydrogel strategy stabilizes PAH and enables controlled topical delivery, supporting PAH-L-G as a promising wound dressing platform. Full article

Tsunami-induced scour around coastal embankments and nearshore structures is a primary cause of structural instability and failure. However, the hydrodynamic mechanisms by which coastal vegetation mitigates this scour remain insufficiently understood. This study employs three-dimensional numerical simulations to investigate the influence of rigid and flexible vegetation on overflow-induced scour downstream of embankments and local scour around structures under tsunami-like inundation. The simulations were conducted using Ansys Fluent 2021R2, utilizing the Volume of Fluid (VOF) method to capture the free surface and the RNG $k-\epsilon$ turbulence model within the Reynolds-averaged Navier–Stokes (RANS) framework. Computational geometries were reconstructed from laboratory experiments, and the model’s reliability was validated against measured water surface profiles. The results demonstrated that vegetation significantly alters flow dynamics, velocity distributions, vortex structures, and both the magnitude and patterns of bed shear stress within scour holes. Specifically, in overflow-induced scour, vegetation suppresses scour intensity by inducing backwater effects, enhancing momentum diffusion, attenuating flow impingement on the bed, and reducing peak bed shear stress. Conversely, for local scour around structures, vegetation increases upstream water depth while intensifying downstream wake vortices, leading to scour hole elongation—particularly under dense and tall vegetation. These findings offer novel insights into the hydrodynamics of vegetation-induced scour mitigation and provide guidelines for optimizing vegetation configurations to enhance the tsunami resilience of coastal infrastructure. Full article

The mining industry still faces major environmental and socioeconomic problems as a result of tailings dam failures, which highlights the urgent need for improved monitoring and early-warning systems. This research offers practical recommendations for improved monitoring and safer design practices, in addition to investigating the use of digital image processing (DIP) as a non-invasive technique for tracking slope deformation in tailings dam models subjected to incremental pore water pressure increases. To replicate real-world conditions as closely as possible, a scaled laboratory embankment was built using coarse and fine tailings. During controlled pore-pressure loading, more than 500 high-resolution photos were taken, recording the entire deformation sequence from initial displacement to slope failure. The images were processed using Mathematica to generate pixel-by-pixel displacement fields and vector plots, providing a detailed visualization of deformation mechanisms. The findings demonstrated that DIP accurately detects and measures surface displacement, revealing the mechanisms, direction, and intensity of deformation. This study illustrates the extensive potential of DIP for real-time monitoring by directly connecting slope instability triggered by incremental pore water pressure with visual indications of slope deformation. While the results confirm the strong potential of DIP for deformation monitoring with a minimum detectable displacement of approximately 1.0 mm under controlled laboratory conditions, its field application may be affected by scale effects, variable lighting, and environmental occlusion. The mining industry benefits greatly from the insights gained through in-depth image analysis, which promotes safer tailings dam design and management. Overall, DIP can provide a reliable, scalable foundation for real-time deformation monitoring in operational tailings dams, where continuous image-based measurements can help identify early signs of instability and support proactive risk management. Full article

In mountain areas, long linear transport infrastructures (roads, motorways, railways, etc.) are exposed to numerous natural hazards, especially hydrological and gravity-driven events such as slope instabilities, rockfalls, or torrential hazards. These phenomena can damage infrastructure, or even lead to the destruction of large sections, causing a risk for users and a deterioration of service. Infrastructure managers face several difficulties in handling these risks. One of them is identifying and representing them, due to the scale of the infrastructure, which is composed of numerous structures and exposed to multiple hazards. In this context, a model is proposed to represent all potential failure scenarios for such infrastructures. This model is based on system reliability analysis methods: functional analysis, failure mode and effect analysis (FMEA), and fault tree analysis (FTA). It is intended to be applied to a linear infrastructure, several kilometres long, exposed to various hazards. The proposed approach allows for the identification of all possible failure modes, including damage to structures and its functional consequences. Its applicability is being tested on a simple case study. Full article

Hierarchical federated learning (HFL) combined with the Mobile Edge Computing (MEC) paradigm has attracted extensive research interest in the Industrial Internet of Things (IIoT) due to its ability to deploy computational resources near edge devices and effectively reduce communication overhead. However, in real-world applications, the dynamic participation of edge devices and their diverse training objectives can lead to instability in model convergence, affecting overall system performance. To address this challenge, this paper proposes a device selection strategy based on task completion probability to determine participating devices dynamically in each training round. Furthermore, to balance system resource consumption and model performance, we formulate an optimization objective to minimize the loss function under resource constraints. By leveraging theoretical analysis, we reformulate the objective as a loss upper bound minimization problem related to resource allocation, which is subsequently decomposed into multiple subproblems for iterative solving. Simulation results demonstrate that the proposed method achieves superior resource efficiency and training stability. Compared to the state-of-the-art HFL method, DSRA-HFL reduces the average training delay by approximately 18% and energy consumption by 22% under dynamic conditions, while maintaining a competitive model accuracy. This validates the effectiveness of our joint optimization strategy in practical IIoT scenarios. Full article

Natural gas pipeline networks are subject to supply instability due to random fluctuations. Current forecasting methodologies often suffer from limited accuracy, inadequate uncertainty quantification, and poor integration with dynamic network evaluation mechanisms. To address these challenges, this study presents an integrated framework that bridges short-term demand forecasting with supply assurance assessment. A deep learning model that combines a graph convolutional network and a bidirectional long short-term memory network is developed to produce accurate 72 h demand forecasts. Forecasting uncertainty is quantified using the cumulative distribution function. Based on the probabilistic forecasts, a supply assurance evaluation model is constructed that accounts for the dynamic regulation capability of line pack. The comprehensive indicator system incorporates key metrics such as user satisfaction and the line pack demand−storage ratio. A case study was conducted with the proposed method based on a regional real-world pipeline network. The results demonstrate that the proposed model outperforms conventional baselines, achieving a mean absolute percentage error of less than 1%. The uncertainty quantification captures the risk probability associated with demand fluctuations. The proposed evaluation method identifies vulnerable sections and assesses supply margins under various scenarios, thus providing effective decision support for operational scheduling and supply assurance. Full article

The urinary sodium-to-potassium (Na/K) ratio is a clinically established predictor of blood pressure and cardiovascular risk. This study aimed to develop and rigorously evaluate machine learning models for estimating the urinary Na/K ratio using four easily obtainable physiological variables: body weight, systolic blood pressure, diastolic blood pressure, and pulse rate. A dataset of 82 participants was analyzed under a nested cross-validation framework to ensure strict generalization assessment. We first designed an attention-based deep learning model (MIDIP: Multi-Integrated Deep Ion Prediction). Although MIDIP showed reduced training error, nested validation revealed performance instability, indicating overfitting in this small-sample setting. We then compared classical machine learning models and ensemble strategies. Among all configurations, simple averaging of Random Forest, Gradient Boosting, and Linear Regression (Group A) achieved the best performance (MAE = 1.756, RMSE = 2.349, R² = 0.390). In contrast, incorporating a Transformer model (Group B) degraded performance (MAE = 1.855, R² = 0.294). Similarly, adaptive weighting (AWE) did not improve accuracy (Group A: MAE = 1.836, R² = 0.266; Group B: MAE = 2.133, R² = 0.035). These results demonstrate that, under limited sample conditions (N = 82), model simplicity and equal-weight ensemble integration provide superior generalization compared to attention-based or adaptively weighted deep architectures. The findings underscore the importance of strict validation and controlled model complexity when developing clinically applicable prediction models from small datasets. Full article

Electrical Discharge Machining (EDM) enables precision post-weld finishing of AISI 304 stainless steel, but stochastic spark overlaps make the fatigue-critical maximum peak-to-valley height (R_max) difficult to predict. This study develops a validated physics-based framework quantifying how crater overlap governs R_max evolution. Experiments on unwelded AISI 304 cylinders—proxying weld metal while excluding heat-affected zone (HAZ) effects—used Central Composite Design (20 trials, 900–9380 μJ discharge energies). Profilometry and scanning electron microscopy (SEM) correlated the crater size, overlap intensity, micro-cracking, and R_max escalation from 18 to 85 μm. Primary and secondary crater formation under minimum and maximum overlap configurations were simulated using a 2D axisymmetric finite element model with Gaussian heat flux and temperature-dependent thermophysical properties. The predictive metric R_max,num = (d_initial + d_secondary)/2 achieved 11–19% average error against the experimental R_max,exp, with complementary valley depth (R_v) validation at 13% error. The Specimen 7 outlier (~50% error) reveals the limitations of deterministic modelling under stochastic debris accumulation and plasma instability at intermediate energies. Crater overlap generates secondary dimples, sharp inter-crater peaks, and rim micro-crack networks, driving the 4.7-fold R_max increase—approaching International Institute of Welding (IIW) fatigue thresholds (<25 μm for high-cycle categories). The framework explicitly links the discharge energy, plasma channel radius (R_pc), and overlap geometry to surface topography, enabling process optimization (I·t_on < 60 A·s maintains R_max < 25 μm). Mesh independence (<2.5% convergence) and six centre-point replicates (CV = 4.2%) confirm robustness. This validated upper-bound R_max predictor supports the digital co-optimization of welding and EDM parameters for aerospace/energy applications, with planned extensions to stochastic 3D models incorporating adaptive remeshing and real weld topographies. Full article

The transition toward carbon-neutral energy systems has revived interest in nuclear technologies, particularly small and micro modular reactors (SMRs and MMRs) as flexible, safe and efficient alternatives to conventional large-scale power plans. In the Czech Republic, Centrum výzkumu Řez (CVŘ) is developing Energy Well (EW), a molten salt-cooled micro modular reactor concept employing FLiBe (Fluoride Lithium Beryllium) as primary and secondary coolant and a supercritical CO₂ (sCO₂) tertiary loop. A dedicated experimental facility was built to reproduce EW operating conditions and provide critical data on thermohydraulic behavior, fuel properties and heat-transfer mechanisms. This paper presents the development and assessment of a TRACE (TRAC/RELAP Advanced Computational Engine) model of the experimental facility, including specific methodologies for the main heater and the heat exchanger. Model accuracy was assessed through comparison with experimental commissioning data. The simulations demonstrated overall model consistency, especially regarding the heat exchanger and the main heater general performances, while some discrepancies were observed inside the main heater graphitic core. Other discrepancies were observed along the loop, mainly resulting from modeling simplifications and lack of information regarding certain experimental loop phenomena. In particular, the pressure calculation showed large inconsistencies mainly connected to the complexity of pressure measurements in molten salt circuits and the lack of specific head loss correlations. This study also helped identify broader issues in both the code (persistent error in generating CO₂ property tables and instabilities resulting from FLiBe interactions with non-condensable gases) and the experimental loop (defect in the heat exchanger filling and uncertainties on sensors location), also contributing to resolving sensor-related inconsistencies in the facility. Results confirm TRACE as a reliable tool for modeling molten salt systems, regarding the temperature distribution and the heat transfer. However, depending on the specific experimental case, this paper introduces specific limitations, such as some inconsistencies in the pressure drops distribution, in order to support the future development of TRACE code. Beyond technical advances, this work provides unique experimental data and fosters international collaboration in advancing SMR and molten salt reactor technologies. Full article

This work presents a high-performance piezoelectric MEMS yaw gyroscope fabricated on a single-crystal silicon platform, which achieves a quality factor of 75 k—the highest reported to date among silicon-based piezoelectric gyroscopes. The device employs a wide annular resonator that operates at 132 kHz in the in-plane wineglass mode. To maximize transduction efficiency, we develop an analytical model that relates output charge to the area-integrated in-plane stress under modal deformation, and we use this model to guide parametric optimization of the annular width. The resulting geometry simultaneously enhances the mechanical quality factor and the piezoelectric coupling. A back-etching fabrication process is used to eliminate front-side release holes, thereby preserving structural continuity and suppressing thermoelastic damping. In open-loop rate mode operation with a native frequency split of 28 Hz, the gyroscope demonstrates an angle random walk of 0.34°/√h and a bias instability of 8.19°/h. These performance metrics are comparable to those of state-of-the-art lead zirconate titanate (PZT)-based annular gyroscopes, while the use of lead-free aluminum nitride as the transduction material ensures compliance with RoHS environmental regulations. Full article

Land-use and land-cover change plays a critical role in shaping urban expansion patterns and modifying near-surface soil conditions, hydrological behaviour, and geomorphological stability in rapidly developing regions. This study presents a comparative modelling framework to analyze long-term land-use change and its implications for regional-scale geotechnical risk screening by integrating historical land-use classification, Markov transition analysis, and machine learning–based spatial simulation. Landsat imagery from 1985 and 2024 was classified using a Support Vector Machine approach, and future land-use projections for 2063 were generated using both the TerrSet Land Change Modeler (LCM) and the GeoFLUS model under identical transition demands. Spatial driving variables included topographic, hydrological, and accessibility-related factors that influence soil behaviour and urban suitability. The results reveal sustained urban expansion primarily driven by the systematic conversion of agricultural land into built-up surfaces, while forested areas and water bodies exhibit high class persistence, as indicated by dominant diagonal values in the Markov transition matrix. Although both models reproduce consistent directional trends, they generate distinct spatial allocation patterns, with LCM producing compact and centralized growth and GeoFLUS generating more spatially dispersed expansion. These differences lead to contrasting implications for potential settlement, flooding, and slope instability zones. By treating future land-use maps as alternative geotechnical screening scenarios rather than fixed predictions, this study demonstrates how model uncertainty can be incorporated into hazard-sensitive planning. The proposed framework supports preliminary geotechnical zoning and infrastructure planning by identifying robust development corridors and spatial uncertainty zones where detailed site investigations may be prioritized. The methodology is transferable to other rapidly urbanizing regions facing complex soil and geomorphological constraints. Full article