Search Results (1,285)

Reliable emission monitoring is essential for effective environmental regulation and the operation of carbon markets. However, high-frequency CO₂ data from Continuous Emission Monitoring Systems (CEMS) and material-based monitoring often contain inconsistencies arising from operational variability, sensor drift, and data-processing errors. This study develops a transparent statistical framework to screen the quality of CO₂ emission data by integrating CEMS measurements with material-based estimates in a coal-fired power plant. A correlation ratio between the two monitoring approaches is used as a process-level indicator, and four statistical tests, Mann–Whitney U, Bootstrap, Levene, and Dip tests, are applied to detect distributional deviations associated with anomalous behavior. Using one year of high-resolution data, we evaluate the influence of reference dataset size, anomaly magnitude, and anomaly duration on detection performance. The results show that approximately 700 reference samples are sufficient to establish a stable baseline. Anomalies corresponding to daily emission deviations of about 4% or higher, when sustained over several days, can be reliably identified as anomalous at the monthly scale. A composite risk score is further developed to support monthly data screening and risk-based verification. The proposed framework provides a practical tool to improve the reliability of emission data and supports more transparent and efficient environmental monitoring and regulatory oversight. Full article

Universities play a dual role as sites of learning and as institutions that certify student competence through assessment. The rapid diffusion of generative artificial intelligence (GenAI) challenges this certification function by altering the conditions under which assessment evidence is produced. When powerful AI tools are widely available, grades may increasingly reflect a combination of individual understanding and external cognitive support rather than solely independent competence. This study examines how changes in assessment format interact with GenAI availability to reshape observable performance outcomes in higher education. Using exam grade data from a compulsory undergraduate course delivered over five years (2021–2025; N = 1066), the study exploits a naturally occurring change in assessment conditions as a natural experiment. From 2021 to 2024, the course was assessed using an AI-permissive take-home examination, while in 2025 the assessment shifted to an AI-restricted, supervised in-person examination. Course content, intended learning outcomes, grading criteria, examiner continuity, and the structural design of the examination tasks remained stable across cohorts. The results reveal a pronounced shift in grade distributions coinciding with the format change. Failure rates increased sharply in 2025, mid-range grades declined, and the proportion of top grades remained largely unchanged. Statistical analysis indicates a significant association between examination period and grade outcomes (χ²(5, N = 1066) = 60.62, p < 0.001), with a small-to-moderate effect size (Cramér’s V = 0.24), driven primarily by the increase in failing grades. These findings suggest that AI-permissive and AI-restricted assessment formats may not be measurement-equivalent under conditions of widespread GenAI use. The results raise concerns about construct validity and the credibility of grades as signals of independent competence, while also highlighting tensions between certification credibility and assessment authenticity. Full article

Granular deposition and grading strongly influence pore-space topology and hence hydraulic conductivity in natural and engineered porous media, yet quantitative links between deposition sequence, particle-scale morphology, pore-network descriptors, and permeability anisotropy remain incomplete. Here, we develop a process-based digital porous-media framework that couples discrete element method (DEM) deposition with pore-network characterization and Darcy-scale permeability evaluation. Two deposition sequences—normal grading (coarse-to-fine) and reverse grading (fine-to-coarse)—are simulated using bi-disperse particle sets with controlled size ratios. To further isolate the role of particle morphology, particle irregularity is parameterized by a Perlin-noise-based shape perturbation factor and incorporated into the DEM-generated packings. For each packing, pore networks are extracted and quantified in terms of pore/throat size distributions and connectivity, while pore-space complexity is measured via box-counting fractal dimension. Single-phase flow is solved under imposed pressure gradient, and intrinsic permeability is computed along three orthogonal directions to evaluate anisotropy. Results show that increasing size contrast reduces porosity, shifts pore and throat distributions toward smaller characteristic radii, increases pore-space fractal dimension, and yields a monotonic permeability reduction. For identical size ratios, reverse grading consistently yields higher permeability than normal grading, suggesting that deposition sequence exerts a strong control on the continuity and efficiency of effective flow pathways at the sample scale. Increasing particle irregularity decreases permeability and systematically modifies permeability anisotropy, transitioning from weak horizontal anisotropy toward near-isotropy and, at strong irregularity, toward preferential vertical permeability. The proposed framework provides a reproducible route to relate depositional history and particle morphology to pore-network structure and directional permeability, offering implications for filtration, packed-bed design, and sedimentary reservoir characterization. Full article

Handling missing data is a central challenge in quantitative research, particularly when datasets exhibit complex dependency structures, such as nonlinear relationships and interactions. Multiple imputation (MI) via fully conditional specification (FCS), as implemented in the MICE R package, is widely used but relies on user-specified models that may fail to capture complex dependency structures, especially in high-dimensional settings, or on more sophisticated algorithms that are considered data-hungry. This paper investigates the performance of TabPFN, a transformer-based, pretrained foundation model developed for tabular prediction tasks, for MI. TabPFN is pretrained on millions of synthetic datasets and approximates posterior predictive distributions without dataset-specific retraining, offering a compelling solution for imputing complex missing data in small to moderately sized samples. We conduct a simulation study focusing on univariate missingness in a continuous outcome with complete predictors, comparing TabPFN with standard MI methods. Performance is evaluated using bias, standard error, and coverage of the marginal mean estimand across a range of data-generating and missingness mechanisms. Our results show that TabPFN yields competitive or superior performance relative to Classification and Regression Trees and Predictive Mean Matching. These findings highlight TabPFN as a promising tool for missing data imputation, with particular relevance to health research. Full article

This study introduces a computational method for designing realistic, geometrically controlled three-dimensional (3-D) micromodels of porous media to investigate fluid flow in hydrocarbon reservoirs. The methodology utilizes a virtual framework of cubes where an arbitrary, continuous 3-D pore network is generated via two-dimensional (2-D) sketches. A key strength of this deterministic, cube-by-cube approach is the ability to independently control porosity and permeability by adjusting channel size and connectivity, facilitating the systematic study of spatial heterogeneity. Six digital models were developed with porosities ranging from 18.4% to 44.4%. Unlike traditional stochastic algorithms, this explicit geometric control enabled the accurate extraction of pore volume distributions and the establishment of a robust power-law relationship between localized porosity and specific surface area. Statistical analysis confirmed a linear correlation between porosity and pore dimensions. While focusing on design and validation, these models are 3-D printable and provide exact boundary conditions for CFD simulations. Single-phase simulations confirmed the capability to decouple absolute permeability from porosity. Consequently, this framework bridges the gap between numerical simulations and physical laboratory experiments to optimize Enhanced Oil Recovery (EOR) processes. Full article

Fractures as critical discontinuous structural planes in rock masses, directly govern their stability and serve as the core controlling factor in rock mechanics engineering. Existing deep learning models for fracture extraction face persistent challenges, including imbalanced integration of deep and shallow features, limited suppression of background noise, inadequate multi-scale feature representation, and large parameter sizes—making it difficult to strike a balance between detection accuracy and deployment efficiency. Focusing on the Wanshanshan quarry in Yunnan, this study first constructs a high-precision digital model using close-range photogrammetry and 3D real-scene reconstruction. A lightweight yet high-accuracy intelligent detection method, termed MSEM-Deeplabv3+, is then proposed for rock mass fracture extraction. The model adopts lightweight MobileNetV2 as the backbone network, incorporating inverted residual modules and depthwise separable convolutions, resulting in a parameter size of only 6.02 MB and FLOPs of 30.170 G—substantially reducing computational overhead. Furthermore, the proposed MAGF (Multi-Scale Attention Gated Fusion) and SCSA (Spatial-Channel Synergistic Attention) modules are integrated to enhance the representation of fracture details and semantic consistency while effectively suppressing multi-source and multi-scale background interference. Experimental results demonstrate that the proposed model achieves an mPA of 89.69%, mIoU of 83.71%, F1-Score of 90.41%, and Kappa coefficient of 80.81%, outperforming the classic Deeplabv3+ model by 5.81%, 6.18%, 4.53%, and 9.2%, respectively. It also significantly surpasses benchmark models such as U-Net and HRNet. The method accurately captures fine and continuous fracture details, preserves the spatial distribution of long-range continuous fractures, and maintains robust performance on the CFD cross-scene dataset, showcasing strong adaptability and generalization capability. This approach effectively mitigates the risks associated with manual high-altitude inspections and provides a lightweight, high-precision, non-contact intelligent solution for fracture detection in high-steep rock slopes. Full article

Mine water treatment remains a long-term challenge due to high suspended solids, wide particle size distributions, and inflow variability, all of which stress solid–liquid separation systems. Hydrocyclones and filtration often fail not from insufficient capacity, but from the inability to handle dynamic influent behavior. This review integrates existing studies and reinterprets mine water treatment as a system performance issue, focusing on maintaining operability under fluctuating conditions. Evidence shows that high-solids mine water behaves as a concentrated multiphase flow, where particle interactions and flow changes lead to gradual shifts in separation behavior. For example, hydrocyclone efficiency ranges from 85 to 95%, and pressure drop increases by 0.5–5 kPa/h under continuous operation. Wear, clogging, and flow redistribution develop together, impacting the operational window of integrated treatment units. Key gaps remain in system performance under fluctuating loads and reliable performance under high-solids loading. The complexity of these interactions often leads to significant operational risk and performance variability in real-world conditions. Future research should focus on dynamic control strategies, multi-stage pre-separation, and advanced filtration designs to enhance system performance, long-term stability, and adaptability in real mining environments. Emerging technologies and new system configurations may further improve efficiency and reduce operational failure risks under extreme conditions. Full article

With the continuous renewal of urban greening, pollen released by allergenic tree species has become a prominent environmental issue affecting residents’ health. However, existing research still lacks city-wide, rapidly replicable methods for identifying allergenic tree species and assessing exposure risks. Taking Beijing’s central urban districts as a case study, this research establishes a method for the automated identification of allergenic tree species and the assessment of pollen exposure risks based on high-resolution satellite imagery. This study coupled tree species distribution results derived from model inference with population density per unit area to delineate three tiers of exposure risk zones. Subsequently, these risk zones were overlaid with the road network within the study area to determine the distribution of roads with low, medium, and high exposure risk. Public transport stop locations were then introduced as a proxy variable for areas of high population mobility. Lorenz curves and Gini coefficients were calculated to quantify the spatial equity of pollen exposure risk. The results indicate that the model reliably identifies target tree species, with approximately 117,000 valid targets. Exposure risks exhibit significant clustering characteristics and can form continuous expansions along road networks. Incorporating population factors shows minimal change in risk concentration, suggesting pollen exposure risk is primarily driven by the spatial clustering of allergenic tree species and their accessibility within road networks. This risk is highly correlated with the spatial distribution patterns and accessibility characteristics of allergenic tree species, rather than being solely determined by population size. This study provides foundational data and methodological support for urban tree species identification, pollen exposure risk management, and optimised greening configurations. Full article

To enhance the early-age properties of steel slag cement mortar and promote the resource utilization of metallurgical solid waste, in this study, nano-SiO₂ (KH-NS) was modified using a KH550 silane coupling agent. The hydration kinetics and microstructure evolution were systematically analyzed by means of a macroscopic performance test (setting time and compressive strength) and multi-scale microscopic characterization (characterized by Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, X-ray Diffraction, Thermogravimetry-Differential Thermal Analysis, and isothermal calorimetry). The influence mechanism of its content on the early performance of the steel slag cement system was systematically studied. Research findings indicate that at a given dosage, increasing the proportion of KH-NS results in a shorter setting time for steel slag mortar. When the KH-NS dosage reaches 1.5%, the initial and final setting times of steel slag mortar decrease by 24.21% and 21.20%, respectively. The addition of KH-NS effectively enhances the compressive strength of mortar, with a particularly pronounced effect on early strength prior to 14 h of curing. At a KH-NS dosage of 1.5%, the onset of the accelerated phase of hydration heat release in steel slag cement mortar is advanced by 2.5 h. Mechanistic studies indicate that KH-NS accelerates cement hydration by promoting C₃S dissolution and C-S-H gel nucleation through interactions between surface silanol groups (Si-OH) and amino groups (-NH₂). Furthermore, KH-NS refines the pore structure via a micro-aggregate filling effect, reducing the number of harmful pores and improving the pore size distribution. KH-NS continuously consumes Ca(OH)₂ through pozzolanic reactions to generate C-S-H, with its reactivity increasing with higher dosage. Research confirms that KH-NS significantly enhances the early strength and density of steel slag mortar, providing both theoretical justification and technical support for developing low-carbon building materials based on solid waste with high dosage. Full article

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

Rapid and real-time monitoring of blood glucose concentration is critical for the diagnosis and management of diabetes, while conventional invasive detection methods suffer from inconvenience and discomfort, making non-invasive detection a research hotspot. In this study, a dual complementary split-ring resonator (DS-CSRR) operating at 3.3 GHz was designed and fabricated for non-invasive glucose concentration detection, aiming to address the problems of low sensitivity and large size of existing microwave glucose sensors. The sensor was fabricated on a low-cost FR4 dielectric substrate with dimensions of 20 × 30 × 0.8 mm³, and two U-shaped slots were incorporated into the traditional DS-CSRR structure to realize cross-polarization excitation. This design not only enhances the interaction between the electric field and glucose solution but also optimizes the quality factor (Q) and electric field distribution of the resonator without changing the overall size. Compared with the traditional DS-CSRR, the Q factor of the modified structure is increased to 130 under no-load conditions. The transmission coefficient Signal Port 2 to Port 1 (S21) of the sensor loaded with glucose solutions of different concentrations was measured using a vector network analyzer (VNA). The experimental results show a good linear frequency shift with the increase in glucose concentration, with a measured sensitivity of 1.95 kHz/(mg·dL⁻¹). In addition, the sensor is characterized by miniaturization, low cost and easy fabrication due to the adoption of standard PCB fabrication processes. This study successfully demonstrates a non-invasive microwave sensor with high sensitivity for glucose concentration detection, which has promising application potential in personal continuous glucose monitoring, and also provides a useful design strategy for the development of miniaturized high-sensitivity microwave biosensors. Full article

For shipboard CCUS facilities, the integration of chemical absorption columns is constrained by a limited vertical envelope, which motivates packings with axially stretched or compressed Kelvin cells to support compact layout and flow control. This study employs computational fluid dynamics to investigate microscale flow and mass transfer characteristics in Kelvin cells. A comparison among the regular Kelvin cell (RKC), the vertically elongated Kelvin cell (VEKC), and the vertically compressed Kelvin cell (VCKC) indicates that axial stretching and compression modify internal flow distributions and gas–liquid mass transfer during CO₂ absorption. The liquid distribution transitions from a film along the struts with localized accumulation at the nodes in RKC to a continuous columnar stream in VEKC, and then to a stable hollow cylindrical liquid film promoted by lateral redistribution in VCKC. VCKC promotes a stable and expanded liquid film, whereas VEKC tends to induce columnar flow. Reducing the cell size and porosity improves mass transfer efficiency, and the liquid load governs mass transfer flux. These findings provide theoretical guidance for the design and optimization of compact packings for process intensification in shipboard carbon-capture applications. Full article

In the insurance literature, accurately predicting extreme losses has been a persistent and important problem. Recently, under the modelling framework of weighted distributions, several finite-mixture size-biased distributions, including size-biased Weibull and size-biased truncated log-normal distributions, have gained popularity for modelling heavy-tailed insurance claim data. In this study, unlike existing models, we explicitly account for the individual heterogeneity commonly observed in insurance claims by treating the order of size-biased weighting as a continuous latent variable, thereby constructing a mixed size-biased distribution. In particular, we study the various distributional properties of the mixed log-normal distribution with a truncated normal prior, which serves as a conjugate prior for the size-biased log-normal model. For applications in non-life insurance, we discuss the Bayesian credibility premium and present an estimation of a regression model via the EM algorithm. We further conduct a real-data analysis using insurance loss data, comparing goodness-of-fit and tail risk measures with those of standard heavy-tailed distributions. Full article

This study investigates the vertical variation and temporal characteristics and indicates the sources of black carbon (BC) and brown carbon (BrC) within particulate matter fraction PM₁ during light (November–December 2024) and heavy (January–February 2025) haze episodes in Bangkok, Thailand, a topic where data are still limited data regarding Southeast Asian megacities. Continuous measurements were conducted at 30 and 110 m above ground level, together with particle size distribution measurement, micrometeorological observations, and backward air mass trajectory analysis. During the haze periods, the highest particle number concentrations occurred in the 0.3–0.4 µm size range, indicating dominant contributions from combustion-related emissions and secondary aerosol formation. Mean PM₁ mass concentrations during the heavy haze episodes were more than 2.5 times higher than those during light haze. BC concentrations increased substantially during heavy haze, while the BC fraction of PM₁ remained relatively constant (~10%). In contrast, the BrC fraction reached nearly 20%, reflecting an increasing influence of biomass burning emissions associated with regional transport. Combined analyses of BC/BrC relationships, wind-direction dependence, and air mass trajectories demonstrate mixed contributions from local fossil fuel combustion and long-range transport of biomass burning aerosols during severe haze events. Full article

As an effective technical approach for ecological environment protection in mining areas and coal resource recovery under buildings, railways and water bodies, solid backfill coal mining technology has been widely applied. When gangue was used as backfill material and placed into the goaf, its compression characteristics and crushing behavior were found to directly affect the control effect of overlying strata deformation. In this study, combined with the compression characteristics of gangue solid waste backfill materials, eight kinds of gangue solid waste backfill materials with different particle size gradations were adopted as research objects. From the perspectives of stress–strain compaction characteristics, the coupling relationship between internal crushing and acoustic emission (AE), relative density in the compacted state and particle size distribution, the hierarchical crushing behavior, and the AE response characteristics of gangue solid waste backfill materials under different gradation schemes were systematically revealed, and the optimal gradation parameters for different layers were determined. The results showed that the compaction process of gangue solid waste backfill materials could be divided into three stages: initial compression, rapid compaction and plastic compaction. During the compaction process, internal crushing was mainly concentrated in the middle layer. In the initial stage of the test, the AE intensity of the middle layer was measured to be higher than 78%, and the AE intensity remained above 50% in the compacted state. When the specimen was compressed to 220 mm, all eight gradation schemes exhibited the characteristic that the proportion of locating points and energy level in the middle layer were much higher than those in the upper and lower layers. With the continuous increase in axial pressure, the intensive area of crushing events was observed to migrate in the order of middle layer → upper layer → lower layer. With the continuous increase in axial pressure, the intensive area of crushing events was observed to migrate in the order of middle layer → upper layer → lower layer. The findings obtained in this study have provided a theoretical basis and experimental support for the gradation optimization of gangue solid waste backfill materials and roof deformation control in solid backfill coal mining engineering. Full article