Search Results (32,883)

The mitigation of traffic noise is essential for the development of sustainable and livable urban environments, a goal that is directly contingent on addressing tire-pavement interaction noise (TPIN) as the dominant acoustic pollutant at medium to high vehicle speeds. This comprehensive review addresses a critical gap in the literature by systematically analyzing the damping properties of pavement systems through a unified, multi-scale framework—from the molecular-scale viscoelasticity of asphalt binders to the composite performance of asphalt mixtures. The analysis begins by synthesizing state-of-the-art testing and characterization methodologies, which establish a clear connection between macroscopic damping performance and the underlying viscoelastic mechanisms coupled with the microscopic morphology of the binders. Subsequently, the review critically assesses the influence of critical factors, such as polymer modifiers including rubber and Styrene-Butadiene-Styrene (SBS), temperature, and loading frequency. This examination elucidates how these variables govern molecular mobility and relaxation processes to ultimately determine damping efficacy. A central and synthesizing conclusion emphasizes the paramount importance of the asphalt binder’s properties, which serve as the primary determinant of the composite mixture’s overall acoustic performance. By delineating this structure-property-performance relationship across different scales, the review consolidates a foundational scientific framework to guide the rational design and informed material selection for next-generation asphalt pavements. The insights presented not only advance the fundamental understanding of damping mechanisms in pavement materials but also provide actionable strategies for creating quieter and more sustainable transportation infrastructures. Full article

Wooden waste railroad ties preserved with coal tar creosote oil represent a specific source of polluting substances. The aim of this study was to investigate and compare extraction capacity due to solvent extraction of fifteen frequently used organic solvents for the purpose of decontamination treatment of waste wooden railroad ties, while recovering wood for reuse. Pure organic solvents, ethanol 96%, propan-2-ol, deionized water, dichloromethane, acetone, n-hexane, mixture n-hexane/acetone (V/V = 1/1), cyclohexane, methanol, N,N-dimethyl formamide, toluene, ethyl acetate, acetonitrile, amyl acetate, medical gasoline, n-pentane and n-butyl acetate were for leaching pollutants from waste railroad ties. The highest extraction capacity was achieved using dichloromethane, where 7.50 to 7.89 wt.% of total sixteen polycyclic aromatic hydrocarbons were extracted from waste railroad tie chips. The most promising solvents for the treatment exhibited extraction efficiency which decreases in a series dichloromethane > n-hexane/acetone > acetone > methanol > ethanol 96% > propan-2-ol > cyclohexane > toluene > n-hexane. Solvent extraction represents a novel approach for treatment of wooden waste railroad ties. The experiments are based on the search for a management process for the treatment of wood waste railroad ties that is simple, low energy consumption, efficient and could potentially be applied for large scale. Full article

An ongoing challenge in pelagic oceanography and limnology is to quantify and understand the distribution of suspended particles and particle aggregates with sufficient temporal and spatial fidelity to understand their dynamics. These particles include biotic (mesoplankton, organic fragments, fecal pellets, etc.) and abiotic (dusts, precipitates, sediments and flocks, anthropogenic materials, etc.) matter and their aggregates (i.e., marine snow), which form a large part of the total particulate matter > 200 μm in size in the ocean. The transport of organic material from surface waters to the deep-sea floor is of particular interest, as it is recognized as a key factor controlling the global carbon cycle and hence, a critical process influencing the sequestration of carbon dioxide from the atmosphere. Here we describe the development of an oceanographic instrument, the Pelagic Laser Tomographer (PLT), that uses high-resolution optical technology, coupled with post-processing analysis, to scan the 3D content of the water column to detect and quantify 3D distributions of small particles. Existing optical instruments typically trade sampling volume for spatial resolution or require large, complex platforms. The PLT addresses this gap by combining high-resolution laser-sheet imaging with large effective sampling volumes in a compact, deployable system. The PLT can generate spatial distributions of small particles (~100 µm and larger) across large water volumes (order 100–1000 m³) during a typical deployment, and allow measurements of particle patchiness over spatial scales to less than 1 mm. The instrument’s small size (6 kg), high resolution (~100 µm in each 3000 cm² tomographic image slice), and analysis software provide a tool for pelagic studies that have typically been limited by high cost, data storage, resolution, and mechanical constraints, all usually necessitating bulky instrumentation and infrequent deployment, typically requiring a large research vessel. Full article

Surface reconstruction from 3D point clouds has a wide range of applications. In this paper, we focus on the reconstruction from raw, sparse point clouds. Although some existing methods work on this topic, the results often suffer from geometric defects. To solve this problem, we propose a novel method that optimizes a neural network (referred to as signed distance function) to fit the Signed Distance Field (SDF) from sparse point clouds. The signed distance function is optimized by projecting query points to its iso-surface accordingly. Our key idea is to encourage both the direction and distance of projection to be correct through the supervision provided by a nearest neighbor prior. In addition, we mitigate the error propagated from the prior function by augmenting the low-frequency components in the input. In our implementation, the nearest neighbor prior is trained with a large-scale local geometry dataset, and the positional encoding with a specified spectrum is used as a regularization for the optimization process. Experiments on the ShapeNetCore dataset demonstrate that our method achieves better accuracy than SDF-based methods while preserving smoothness. Full article

Soil erosion is a major threat to the sustainable productivity of arable lands, making the accurate prediction of soil erodibility essential for effective soil conservation planning. Soil erodibility is strongly controlled by intrinsic soil properties that regulate aggregate resistance and detachment processes under erosive forces. In this study, machine learning (ML) models, including the Multi-layer Perceptron Regressor (MLP), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost), were applied to predict the soil erodibility factor (K-factor). A comprehensive set of soil properties, including soil texture, clay ratio (CR), organic matter (OM), aggregate stability (AS), mean weight diameter (MWD), dispersion ratio (DR), modified clay ratio (MCR), and critical level of organic matter (CLOM), was analyzed using 110 soil samples collected from the northern part of Adana Province, Türkiye. The observed K-factor was calculated using the RUSLE equation, and ML-based predictions were spatially mapped using Geographic Information Systems (GISs). The mean K-factor values for arable, forest, and horticultural land uses were 0.065, 0.071, and 0.109 t h MJ⁻¹ mm⁻¹, respectively. Among the tested models, XGBoost showed the best predictive performance, with the lowest MAE (0.0051) and RMSE (0.0110) and the highest R² (0.9458). Furthermore, the XGBoost algorithm identified the CR as the most influential variable, closely followed by clay and MCR content. These results highlight the potential of ML-based approaches to support erosion risk assessment and soil management strategies at the regional scale. Full article

In this study, small-scale experiments were performed to examine fuel distribution effects on discrete flame spread behavior under natural convection and ambient wind. To this end, birch rod arrays with regularly varying column number (n) and array spacing (S) were designed. The results indicate that fuel distribution exerts a comparable influence on flame spread under both natural convection and ambient wind conditions. The flame spread rate (V_f), flame length (L_f), and mass loss rate (MLR) are insensitive to changes in S but have an exponential relationship with n. Based on the mass conservation law, prediction correlations for the mass loss rate based on S and n in the stable flame spread stage are proposed. We discovered that nondimensional mass loss has a power law dependence on the fuel coverage rate. In addition, radiative heat transfer dominates the flame spread process for the discrete array. Horizontal flame spread across discrete rod arrays exhibits critical spacing under natural convection. Finally, we established a comprehensive heat transfer model for flame spread under natural convection conditions and obtained a derivation of a critical sustainability criterion for the discrete flame spread process, which considers radiative and convective heat transfer. Full article

Floating offshore wind turbines (FOWTs) constitute a pivotal offshore renewable energy technology, offering a sustainable and eco-friendly solution for large-scale marine power generation. Their low-carbon emission characteristics are highly aligned with global sustainable development goals, playing a crucial role in promoting energy structure transformation and reducing reliance on fossil fuels. This paper presents a numerical study on the coupled dynamic behavior of a semi-submersible FOWT during its mooring system installation. The proposed methodology incorporates environmental loads from incident waves, wind, and currents. Those forces act on not only the floating platform but also on the three tugboats employed throughout the installation procedure. Detailed evaluations of forces and motion responses are conducted across successive stages of the operation. The findings demonstrated the feasibility of the proposed mooring installation process for FOWTs while offering critical insights into suitable installation weather windows and motion responses of both the platform and tugboats. Furthermore, the novel installation scheme presented herein offers practical guidance for future engineering applications. Full article

This study presents the development and first manufacturing realization of the ECOTIRE concept, a modular and sustainable tire system featuring a removable tread mechanically interlocked with a reusable casing. The concept aims to reduce rubber waste and improve recyclability by eliminating adhesive bonding and enabling tread replacement. Building on previous experimental and numerical studies that validated the interlocking performance, this work focuses on producing a scaled prototype using a low-cost molding process, which can serve as the basis for accessible and sustainable manufacturing. VMQ silicone rubber was selected as an eco-friendly material due to its durability, thermal stability, and processing versatility. A custom two-part aluminum mold was designed to replicate the optimized interlocking geometry, enabling accurate casting, curing, and assembly. The resulting prototype achieved precise fit, dimensional uniformity, and easy disassembly, confirming the manufacturing feasibility of the ECOTIRE concept and demonstrating its potential to support circular economy strategies through reduced material waste and extended tire component lifetimes. Full article

In the context of the ongoing digital revolution in manufacturing and the simultaneous advancement toward dual carbon objectives, this study investigates the role of intelligent technological advancements, particularly industrial robotics, in improving firm-level energy efficiency. Utilizing panel data from Chinese listed companies spanning the period 2012–2023, the research assesses the relationship between exposure to industrial robots and corporate energy efficiency metrics. The empirical analysis demonstrates that greater exposure to industry-level robotization substantially boosts corporate energy performance, verifying that intelligent modernization and green transition can be mutually reinforcing. This positive effect is particularly pronounced among superstar firms, in more competitive industries, and for capital-intensive enterprises. Mechanism analysis reveals that, first, robotization processes generate a scale effect that effectively dilutes the fixed energy consumption per unit of product. Second, the diffusion of robots intensifies market competition, creating a competition effect that compels all firms within the industry to optimize costs and management with a focus on energy conservation. This study demonstrates that enhancing human capital within organizations significantly amplifies the beneficial impact of robotic integration on energy efficiency metrics. By providing empirical data from an emerging market context, this research not only elucidates the role of industrial robots but also offers policy-relevant insights for developed economies navigating the concurrent challenges of industrial modernization and environmental sustainability. Full article

Machine learning (ML)-powered vision for robotic inspection has accelerated with smart manufacturing, enabling automated defect detection and classification and real-time process optimization. This review provides insight into the current landscape and state-of-the-art practices in smart manufacturing quality control (QC). More than 50 studies spanning across automotive, aerospace, assembly, and general manufacturing sectors demonstrate that ML-powered vision is technically viable for robotic inspection in manufacturing. The accuracy of defect detection and classification frequently exceeds 95%, with some vision systems achieving 98–100% accuracy in controlled environments. The vision systems use predominantly self-designed convolutional neural network (CNN) architectures, YOLO variants, or traditional ML vision models. However, 77% of implementations remain at the prototype or pilot scale, revealing systematic deployment barriers. A discussion is provided to address the specifics of the vision systems and the challenges that these technologies continue to face. Finally, recommendations for future directions in ML-powered vision for robotic inspection in manufacturing are provided. Full article

The application of bioactive nanoemulsions in the meat industry has attracted great interest due to their ability to improve the stability, bioavailability, and functionality of bioactive compounds, contributing to the extension of the shelf-life of highly perishable products, such as sausages and meat patties. Thus, this review provides a critical analysis of the application of nanoemulsions in sausages and meat patties, with emphasis on their mechanisms of action, formulation strategies, and performance in improving oxidative stability and microbial safety. Nanoemulsions, typically characterized by droplet sizes below 200 nm, increase interfacial area and penetration into meat matrices, resulting in reductions of 30–60% in lipid oxidation markers and decreases of 1–2 log CFU/g in spoilage and pathogenic microorganisms. Preparation and stabilization approaches, including high-energy and low-energy methods, are summarized, and the influence of nanoemulsion characteristics on texture, color, pH, and sensory perception is discussed. Particular attention is given to technological barriers, such as scale-up feasibility, stability during processing and storage, interactions with meat proteins, as well as regulatory and labeling considerations related to nano-enabled foods. Overall, the current evidence indicates that NEs represent a viable strategy to replace synthetic preservatives while supporting clean-label product development; however, further research on safety assessment, optimal dosing, and consumer acceptance is still required for broader industrial implementation. Full article

In mineral processing, visual-based online particle size analysis systems depend on high-precision image segmentation to accurately quantify ore particle size distribution, thereby optimizing crushing and sorting operations. However, due to multi-scale variations, severe adhesion, and occlusion within ore particle clusters, existing segmentation models often exhibit undersegmentation and misclassification, leading to blurred boundaries and limited generalization. To address these challenges, this paper proposes a novel semantic segmentation model named VTC-Net. The model employs VGG16 as the backbone encoder, integrates Transformer modules in deeper layers to capture global contextual dependencies, and incorporates a Convolutional Block Attention Module (CBAM) at the fourth stage to enhance focus on critical regions such as adhesion edges. BatchNorm layers are used to stabilize training. Experiments on ore image datasets show that VTC-Net outperforms mainstream models such as UNet and DeepLabV3 in key metrics, including MIoU (89.90%) and pixel accuracy (96.80%). Ablation studies confirm the effectiveness and complementary role of each module. Visual analysis further demonstrates that the model identifies ore contours and adhesion areas more accurately, significantly improving segmentation robustness and precision under complex operational conditions. Full article

This classroom-based case study examines how an AI-mediated Socratic dialogue, implemented through ChatGPT, can support students’ engagement and perceived learning in undergraduate thermodynamics. Conducted in a first-year engineering physics course at a private university in northern Mexico, the activity invited small student groups to interact with structured prompts designed to promote inquiry, collaboration, and reflective reasoning about the adiabatic process. Rather than functioning as a source of answers, ChatGPT was intentionally positioned as a mediating scaffold for Socratic questioning, prompting students to articulate, examine, and refine their reasoning. A mixed-methods approach was employed, combining a 10-item Likert-scale survey with construct-level statistical analysis of two focal dimensions: perception of learning and engagement, including an exploratory comparison by gender. Results indicated consistently high levels of perceived learning and engagement across the cohort, with average scores above 4.5 out of 5. At the construct level, no statistically significant gender differences were observed, although a single item revealed higher perceived learning among female students. Overall, the findings suggest that the educational value of ChatGPT in this context emerged from its integration within a Socratic, inquiry-oriented pedagogical design, rather than from the technology alone. These results contribute to ongoing discussions on the responsible and pedagogically grounded integration of generative AI in physics education and align with Sustainable Development Goal 4 (Quality Education). Full article

The Failure Assessment Diagram (FAD), as a significant method for evaluating the suitability of defective metallic structures, has been subject to considerable debate regarding its applicability in assessing ring welded joints for high-grade steel and large-diameter pipelines. To address this issue, this study first designed and conducted two sets of full-scale pressure-tension tests on large-diameter X80 pipeline ring welded joints, considering factors such as different welding processes, joint configurations, defect dimensions, and locations. Subsequently, three widely adopted failure assessment diagram methodologies—BS 7910, API 579, and API 1104—were selected. Corresponding assessment curves were established based on material performance parameters obtained from the ring weld tests. Finally, predictive outcomes from each assessment method were compared against experimental data to investigate the applicability of failure assessment diagrams for evaluating high-strength, large-diameter, thick-walled ring welds. The research findings indicate that, under the specific material and defect assessment conditions employed in this study, the API 1104 assessment results exhibited significant conservatism (two sets matched). Conversely, the BS 7910 and API 579 assessment results showed a high degree of agreement with the experimental data (eight sets matched), with the BS 7910 assessment providing a relatively higher safety margin compared to API 579. The data from this study provides valuable experimental reference for selecting assessment methods under specific conditions, such as similar materials, defects, and loading patterns. Full article

Hydraulic fracturing is crucial for the effective development of unconventional oil and gas reservoirs. This paper systematically reviews the damage issues caused by conventional fracturing fluids in tight unconventional reservoirs, highlighting problems such as significant formation damage and high risks of scale deposition and plugging. To address these shortcomings, a phase-change self-propping fracturing fluid is proposed and compared with a guar gum fracturing fluid and slickwater fracturing fluid. The self-propping fluid offers advantages of low damage and low fluid loss. It can undergo a phase transition to form solid particles that effectively prop the fractures, thereby significantly reducing damage such as reservoir pore structure blockage. This study demonstrates that the phase-change self-propping fracturing fluid is well-suited for tight, low-permeability reservoirs due to its ability to minimize formation damage. Furthermore, the reservoir damage evaluation methodology established in this work provides an effective means for analyzing damage mechanisms and assessing effectiveness during the fracturing process. Full article