Search Results (50)

Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO₂), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO₂ concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article

Hyperspectral image change detection (HSI-CD) provides substantial value in environmental monitoring, urban planning and other fields. In recent years, deep-learning based HSI-CD methods have made remarkable progress due to their powerful nonlinear feature learning capabilities, yet they face several challenges: mixed-pixel phenomenon affecting pixel-level detection accuracy; heterogeneous spatial scales of change targets where coarse-grained features fail to preserve fine-grained details; and dependence on high-quality labels. To address these challenges, this paper introduces WSCDNet, a weakly supervised HSI-CD network employing coarse-to-fine feature learning, with key innovations including: (1) A dual-branch detection framework integrating binary and multiclass change detection at the sub-pixel level that enhances collaborative optimization through a cross-feature coupling module; (2) introduction of multi-granularity aggregation and difference feature enhancement module for detecting easily confused regions, which effectively improves the model’s detection accuracy; and (3) proposal of a weakly supervised learning strategy, reducing model sensitivity to noisy pseudo-labels through decision-level consistency measurement and sample filtering mechanisms. Experimental results demonstrate that WSCDNet effectively enhances the accuracy and robustness of HSI-CD tasks, exhibiting superior performance under complex scenarios and weakly supervised conditions. Full article

This article provides a comprehensive analysis of the acoustic properties of recycled rubber crumb, examined in two forms—loose granular and compacted specimens. The aim was to compare their acoustic properties depending on the size of the fraction, the thickness of the sample, and the degree of compaction, with measurements performed using a model BSWA SW433 impedance tube in the frequency band 100–2500 Hz. Experimental samples of recycled rubber crumb were prepared with various thicknesses (2, 4.5, and 7 cm) and of various fractions (0–4 mm), and the granular samples were compacted under a pressure of 250–750 kPa. The results showed that the highest transmission loss (TL) is achieved by fine fractions at higher pressure and with greater sample thickness; Fraction 1 (below 1 mm) at a pressure of 750 kPa and a thickness of 7 cm had the best acoustic properties. Through regression analysis, mathematical models of the dependence of transmission loss on the monitored parameters for all types of samples (granular/compacted) were created. The regression analysis confirmed that the thickness, pressure, and size of the fraction significantly affect the acoustic properties of the material. Recycled rubber crumb therefore represents an efficient and environmentally sustainable alternative to traditional insulation materials, and optimizing its parameters enables a wide range of practical acoustic applications in construction, transport infrastructure, and manufacturing industries. Full article

The isothermal bainitic transformation kinetics, microstructure, and mechanical properties of the quenched low-carbon high-strength steel have been investigated via dilatometric measurements, microstructural characterization, and mechanical tests. The results show that the pre-transformed isothermal bainite promotes martensitic transformation, increasing the martensitic transformation temperature, and enhancing the transformation rate. The microstructure of the 400 °C isothermal steel consists predominantly of lath bainite ferrite with dot/slender M-A constituents, whereas the steel treated at 450 °C contains a combination of martensite/lath bainite and granular bainite. The presence of massive M-A constituents contributes to brittle fracture as these constituents tend to promote crack initiation. Hence, the 450 °C treatment, which leads to the formation of massive M-A constituents, induces brittleness, while the finer M-A constituents formed at 400 °C exert minimal influence on the toughness and result in a more stable microstructure owing to their small size and the surrounding fine lath microstructure. The differences in microstructure and properties between the steels treated at 400 °C and 450 °C illustrate the importance of controlling the quenching cooling rate in the high-temperature bainitic transformation region during thick plate quenching processes. Full article

The paper presents experimental results regarding heat transfer in granular materials in the cylindrical domain during heating by the outer surface of the container. Sensors (K-type thermocouples) were used to measure the temperature changes at several points inside granular material (the fine-grained table salt was used in the experiment). Knowledge of measurement data allows the verification of a mathematical model (based on Fourier’s law) to describe the macroscopic heat conduction in granular materials. An iterative algorithm for the inverse heat conduction problem consisting of the estimation of the thermal diffusivity coefficient of granular material, the parameters of initial boundary conditions and the position of the thermocouple tips during the experiment was developed. Several computational simulations were performed. Based on the experimental results and the computational simulation results, one can conclude that the analytical solution of the direct heat conduction problem calculated for the optimal values obtained from the inverse heat conduction problem gave us the confirmation of the validity of Fourier’s model. Full article

Earth materials are recognized for their excellent thermal and hygrothermal properties but exhibit low mechanical resistance. Binder stabilization improves compressive strength but often increases the carbon footprint. This study evaluates the mechanical, thermal, hygrothermal, and environmental properties of 12 stabilized earth concrete formulations. The samples were prepared using four types of excavated earths (A, B, C, and D) with varying granular distributions and chemical compositions, stabilized with three industrial binders: two low-carbon activated GGBS-based binders (LN and LW) and a CEM II cement. The samples were cured at 20 °C and 100% relative humidity. Density, porosity, thermal conductivity, specific heat capacity, and Moisture Buffer Value (MBV) were measured at 28 days of curing, using standard methods from concrete and geotechnical fields, while compressive strength tests were performed at 7, 28, and 90 days. The results revealed that gravel-rich earths (A and B) demonstrated higher densities and compressive strengths compared to fine-rich earths (C and D). GGBS-stabilized earths exhibited superior mechanical performance (1.7–14.8 MPa) compared to cement-stabilized earths (0.8–3.8 MPa). Despite low binder content (7%), thermal and hygrothermal properties were largely influenced by the earth’s composition. Thermal conductivity (0.48–0.59 W·m⁻¹·K⁻¹), volumetric heat capacity (1661–2031 J·m⁻³·K⁻¹), and MBV (0.9–1.9 g·m⁻²·%RH⁻¹) were consistent with raw earth values, supporting thermal inertia and humidity regulation. The carbon footprint analysis showed that both LN and LW binders had the lowest emissions (29–34 kg CO₂·eq/m³), with LN binders demonstrating consistent normalized performance (5.2–6.2 kg CO₂·eq/m³·/MPa) and LW binders exhibiting superior mechanical performance and a lower normalized indicator (2.3–5.4 kg CO₂·eq/m³/MPa). Conversely, CEM II-stabilized formulations displayed the highest emissions (70–86 kg CO₂·eq/m³) and the least favorable compressive strength-to-carbon ratios. These findings emphasize the potential of stabilized earth concretes, particularly those with low-carbon GGBS binders, for sustainable and energy-efficient construction practices. Full article

This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. The primary objective is to evaluate PLI’s performance against Gradient-Weighted Class Activation Mapping (Grad-CAM) and achieve fine-grained interpretability and improved localization precision. The methodology leverages the VGG19 convolutional neural network architecture and utilizes three publicly available COVID-19 chest radiograph datasets, consisting of over 1000 labeled images, which were preprocessed through resizing, normalization, and augmentation to ensure robustness and generalizability. The experiments focused on key performance metrics, including interpretability, structural similarity (SSIM), diagnostic precision, mean squared error (MSE), and computational efficiency. The results demonstrate that PLI significantly outperforms Grad-CAM in all measured dimensions. PLI produced detailed pixel-level heatmaps with higher SSIM scores, reduced MSE, and faster inference times, showcasing its ability to provide granular insights into localized diagnostic features while maintaining computational efficiency. In contrast, Grad-CAM’s explanations often lack the granularity required for clinical reliability. By integrating fuzzy logic to enhance visual and numerical explanations, PLI can deliver interpretable outputs that align with clinical expectations, enabling practitioners to make informed decisions with higher confidence. This work establishes PLI as a robust tool for bridging gaps in AI model transparency and clinical usability. By addressing the challenges of interpretability and accuracy simultaneously, PLI contributes to advancing the integration of AI in healthcare and sets a foundation for broader applications in other high-stake domains. Full article

Calorimetry at the High Luminosity LHC (HL-LHC) faces many challenges, particularly in the forward direction, such as radiation tolerance and large in-time event pileup. To meet these challenges, the CMS Collaboration is preparing to replace its current endcap calorimeters from the HL-LHC era with a high-granularity calorimeter (HGCAL), featuring an unprecedented transverse and longitudinal segmentation, for both the electromagnetic and hadronic compartments, with 5D information (space–time–energy) read out. The proposed design uses silicon sensors for the electromagnetic section (with fluences above ${10}^{16} {\mathrm{n}}_{eq}{\mathrm{/cm}}^2$) and high-irradiation regions (with fluences above ${10}^{14} {\mathrm{n}}_{eq}{\mathrm{/cm}}^2$) of the hadronic section, while in the low-irradiation regions of the hadronic section, plastic scintillator tiles equipped with on-tile silicon photomultipliers (SiPMs) are used. Full HGCAL will have approximately 6 million silicon sensor channels and about 280 thousand channels of scintillator tiles. This will allow for particle-flow-type calorimetry, where the fine structure of showers can be measured and used to enhance particle identification, energy resolution and pileup rejection. In this overview we present the ideas behind HGCAL, the current status of the project, results of the beam tests and the challenges that lie ahead. Full article

The physicochemical and adsorption properties of granular sorbents based on natural bentonite and modified sorbents based on it have been studied. It was found that modification of natural bentonite with iron (III) polyhydroxocations (mod. 1_Fe_5 GA) and aluminum (III) (mod. 1_Al_5 GA) by the “co-precipitation” method leads to a change in their chemical composition, structure, and sorption properties. It is shown that modified sorbents based on natural bentonite are finely porous (nanostructured) objects with a predominance of pores measuring 1.5–8.0 nm, with a specific surface area of 55–65 m²/g. Modification of bentonite with iron (III) and aluminum compounds by the “co-precipitation” method also leads to an increase in the sorption capacity of the obtained sorbents with respect to bichromate and arsenate anions and nickel cations by 5-10 times compared with natural bentonite. The obtained sorption isotherms were classified as Langmuir type isotherms. Kinetic analysis showed that at the initial stage the sorption process is controlled by an external diffusion factor, i.e. refers to the diffusion of sorbent from solution into a liquid film on the surface of the sorbent. Then the sorption process begins to proceed in a mixed diffusion mode, when it limits both the external diffusion factor and the internal diffusion factor (the diffusion of the sorbent to the active centers through the system of pores and capillaries). To determine the contribution of the chemical stage to the rate of adsorption of bichromate and arsenate anions and nickel(II) cations with the studied granular sorbents, kinetic curves were processed using the equations of chemical kinetics (pseudo-second-order model). As a result, it was found that the adsorption of the studied anions by modified sorbents based on natural bentonite is best described by a pseudo-second-order kinetic model. It is shown that the use of natural bentonite for the development of technology for the production of granular sorbents based on it has an undeniable advantage, firstly, in terms of its chemical and structural properties, it is easily and effectively modified, and secondly, having astringent properties, granules are easily made on its basis, which turn into ceramics during high-temperature firing. The result is a granular sorbent with high physical and mechanical properties. Since bentonite is an environmentally friendly product, the technology of recycling spent sorbents is also greatly simplified. Full article

Controlled interlayer temperature has a profound impact on both the microstructure and mechanical properties of the deposited components. In this study, thin-walled structures made of high-strength low-alloy steel were fabricated using the submerged-arc additive manufacturing process. The effects of varying temperature on the microstructure and mechanical properties of the components were studied. The results showed that the cooling rate within T_8/5 decreased as the interlayer temperature increased, which caused the microstructure to transition from a fine-grained structure dominated by bainitic ferrite and granular bainite to a coarse-grained structure dominated by polygonal ferrite. The measurement of mechanical properties showed that due to the influence of the fine-grained structure, the components with low interlayer temperatures exhibit excellent hardness, high strength, and outstanding ductility and toughness. Furthermore, a faster cooling rate disrupts the stability of carbon diffusion, resulting in the development of increased quantities of residual austenitic films within the components with controlled low interlayer temperatures. This augmentation in residual austenite films strengthens the components’ ductility and toughness, enabling the deposited components to exhibit exceptional impact toughness in low-temperature environments. Full article

The primary concern of synthetic aperture radar (SAR) images is speckle noise, an inherent property. The creation of speckle noise is in a granular form and its nature is multiplicative. To reduce such noise from the radar images, the researchers’ primary motive is to suppress granular pattern while preserving the quality of the obtained images, thereby facilitating easier feature extraction and classification. Existing speckle-noise reduction methods often fail to preserve fine details such as edges and textures. This study proposes a fusion-based method that integrates non-linear transform-based thresholding with advanced noise reduction techniques. The proposed method is implemented on two simulated SAR images at noise variance levels of σ = from 5 to 40. The fundamental and most significant step is to analyze the effect of granular patterns in radar images before despeckling. Different performance metrics, classified into with-reference and without-reference indexes, are considered to investigate the effectiveness of the proposed despeckle method. The Signal-to-Noise Ratio (SNR) for SAR-1 at σ = 20 was observed at 16.22 dB, outperforming the next best result of 12.89 dB from the Log Compression filter. The Universal Image Quality Index (UIQI) reached 0.6987, indicating high visual quality retention across various noise levels. The proposed despeckling method demonstrated superior performance in comparison to different filters, achieving a Peak Signal-to-Noise Ratio (PSNR) improvement of up to 29.37 dB on SAR-2 at a noise variance of σ = 5, significantly higher than the best filter method’s 26.70 dB. Additionally, the method achieved a Structural Similarity Index Measure (SSIM) of 0.6538, indicating superior image quality preservation. Full article

The discrete element method (DEM) is largely used to simulate the geotechnical behavior of granular materials. However, numerical modeling with this type of code is expensive and time consuming, especially when fine particles are involved. This leads researchers to make use of different approaches to shorten the time of calculation without verifying the stability and reliability of numerical results, even though a compromise between the time of calculation and accuracy is commonly claimed. The particle size distribution (PSD) curve of studied granular material is completely ignored or arbitrarily cut. It is unclear if the ensued numerical results are still representative of the studied granular materials. Additionally, one can see a large number of numerical models established on a basis of calibration by ignoring the physical meaning and even measured values of some model parameters. The representativeness and reliability of the obtained numerical results are questionable. All these partly contribute to reducing the public’s confidence in numerical modeling. In this study, a methodology is illustrated to obtain an optimal DEM model, which minimizes the time of calculation and ensures stable and reliable numerical results for the mechanical behavior of a waste rock. The results indicate that the PSD curve of the studied waste rock can indeed be cut by excluding a portion of fine particles, while the Young’s modulus of the waste rock particles can also be decreased to accelerate the numerical calculations. A physical explanation of why the time of calculation can be shortened by reducing the Young’s modulus of waste rock particles is provided for the first time. Overall, the PSD cut, reduction in Young’s modulus, and time step must be determined through sensitivity analyses to ensure stable and reliable results with the shortest time of calculation. In addition, it is important to minimize the number of model parameters determined through the process of calibration, especially for those having physical meanings. In this study, the only model parameter having a clear physical meaning but difficult to measure is the rolling resistance coefficient for repose angle tests on the studied waste rock. Its value has to be obtained through a process of calibration against some experimental results. The validity and predictability of the calibrated numerical model have been successfully verified against additional experimental results. Full article

The work conducted in this study was designed to establish achievable testing tolerances for non-destructive pavement density measurements using Density Profiling Systems (DPSs). Nine and six sensors were used to determine the precision of repeatability and reproducibility in the laboratory and the field, respectively. A minimum of six sensors (considered in this study as independent laboratories) were needed to comply with the minimum number of participants required in the current ASTM standard practice (ASTM E691). The methodology included the development of laboratory precision evaluation with a total of nine sensors and two different mixtures (9.5 mm fine-graded mix, 19.0 mm coarse-graded mix) compacted at four density levels (97%, 94%, 91%, and 88% of G_mm). For the field portion of this study, pavement sections built at the National Center for Asphalt Technology (NCAT) Test Track in 2021 served as experimental variables. These sections were built with fine-graded asphalt mixtures and open-graded mixes as wearing courses. Additionally, the pavement sections included three underlying materials: new asphalt (binder layer), milled asphalt surface, and granular base, with thicknesses ranging from 3.8 to 13.9 cm. Density profile testing was conducted at two locations: within the mat (center of the lane) and along the joint. Computed precision statements regarding dielectric values within and between laboratories were about double for field results compared to laboratory results. However, when converted to density, the statements were significantly below the reported statements for Bulk Specific Gravity and Vacuum Sealing in the laboratory and Nuclear and Electromagnetic density gauges in the field. Full article

In contemporary bulk material transportation systems, closed screw conveyors have become prevalent. These conveyors, enclosed within troughs or cylindrical bodies, effectively mitigate environmental contamination and material toxicity during transit. Their hermetic design prevents material dispersion by wind, thereby minimizing losses and preserving the integrity of raw materials, particularly those with potential health implications such as urea and cement. Consequently, employing a screw conveyor constitutes a prudent safety measure. Despite the widespread use of screw conveyors, a comprehensive understanding of the behavior of material particles within these systems remains elusive and subject to discrepancies across various methodologies. Presently, a multitude of calculation methods and applications exist, resulting in disparities between theoretical computations and practical implementation. Drawing upon Alan W. Roberts’ meticulously devised calculation methodology, renowned for its precision, the authors have developed a swift computational tool utilizing VBA Excel software 2023. Additionally, EDEM simulation software was employed to model granular material behavior. The ensuing calculations guided the selection of optimized technical dimensions for the screw conveyor, which were then fabricated and subjected to real-world testing at the Vinh Tan thermal power plant. Remarkably, the achieved output capacity demonstrated a mere 7% deviation from calculations performed with the VBA program and a 2% variation from those conducted via EDEM simulation. Furthermore, a comprehensive graph depicting the relationship between screw conveyor speed and capacity has been provided, affording a means to finely tune throughput with exceptional accuracy along the production line. The results obtained provide the basis for the development of a device that meets the required capacity specifications accurately and precisely on the first attempt. This accomplishment satisfies stringent capacity standards without the need for any adjustments or modifications, all while ensuring minimal cost and time efficiency. Full article

Given a textual query, text-based person re-identification is supposed to search for the targeted pedestrian images from a large-scale visual database. Due to the inherent heterogeneity between different modalities, it is challenging to measure the cross-modal affinity between visual and textual data. Existing works typically employ single-granular methods to extract local features and align image regions with relevant words/phrases. Nevertheless, the limited robustness of single-granular methods cannot adapt to the imprecision and variances of visual and textual features, which are usually influenced by the background clutter, position transformation, posture diversity, and occlusion in surveillance videos, thereby leading to the deterioration of cross-modal matching accuracy. In this paper, we propose a Pyramidal Multi-Granular matching network (PMG) that incorporates a gradual transition process between the coarsest global information and the finest local information by a coarse-to-fine pyramidal method for multi-granular cross-modal features extraction and affinities learning. For each body part of a pedestrian, PMG is adequate in ensuring the integrity of local information while minimizing the surrounding interference signals at a certain scale and can adapt to capture discriminative signals of different body parts and achieve semantically alignment between image strips with relevant textual descriptions, thus suppressing the variances of feature extraction and improving the robustness of feature matching. Comprehensive experiments are conducted on the CUHK-PEDES and RSTPReid datasets to validate the effectiveness of the proposed method and results show that PMG outperforms state-of-the-art (SOTA) methods significantly and yields competitive accuracy of cross-modal retrieval. Full article