Search Results (1,918)

The application of fully recycled aggregate concrete (FRAC) promotes sustainable construction, but its mechanical properties are often unstable due to the high absorption and variability of recycled aggregates. This study investigates the effect of saturation degrees of recycled coarse aggregate (RCA) and recycled fine aggregate (RFA) on FRAC’s mechanical performance and failure mechanisms. Results show that optimal strength is achieved at 70% RCA and 25% RFA saturation. Reducing RFA saturation from 100% to 25% increases compressive strength by 28.8% and tensile strength by 34.6%. RFA saturation has a greater influence than sand ratio or superplasticizer dosage, second only to water–cement ratio. Analysis indicates that excessive saturation leads to pores and microcracks in the interfacial transition zone, weakening bonding. A multiple linear regression model based on recycled aggregate saturation accurately predicts FRAC properties, supporting optimized use of recycled materials and cleaner construction practices. Full article

In the era of growing demand for sustainable solutions in construction, increasing attention is being paid to the potential use of waste materials as components of building composites. This article presents the results of a study on the impact of ground polyurethane foam waste on the mechanical properties and durability of cement mortars. The waste, derived from industrial production processes, was used as a partial replacement for fine aggregates in various proportions. The analysis included bulk density, compressive and flexural strengths, water absorption, and resistance to freeze–thaw cycles. The results indicate that adding waste reduces the density of the mortar, which can be advantageous in applications requiring lightweight materials. The most favourable balance of strength retention, density reduction, and frost resistance was observed with a 1% addition, as the mortar maintained good mechanical performance and freeze–thaw durability while achieving reduced weight. Higher waste content (2–3%) led to significant deterioration of the mechanical properties due to increased porosity. All samples exhibited increased strength after 25 freeze–thaw cycles, possibly due to continued hydration under moist low-temperature conditions. The analysis of the microstructure of cement coatings with the addition of polyurethane foam enabled the explanation of the causes of the observed changes in physico-mechanical properties resulting from ageing factors. This study suggests that small amounts of waste can be effectively used to produce lightweight and environmentally friendly construction materials, supporting circular economy practices. Full article

The primary objective of this study is to bridge the gap between descriptive geometry and mechanistic design by establishing a dual-domain fractal framework to analyze the internal architecture of asphalt mixtures. This research quantitatively assesses the sensitivity of volumetric indicators—namely air voids (VV), voids in mineral aggregate (VMA), and voids filled with asphalt (VFA)—by employing the coarse aggregate fractal dimension (D_c), the fine aggregate fractal dimension (D_f), and the coarse-to-fine ratio (k) through Grey Relational Analysis (GRA). The findings demonstrate that whereas D_f and k substantially influence macro-volumetric parameters, the mesoscopic void fractal dimension (D_V) remains structurally unchanged, indicating that gradation predominantly dictates void volume rather than geometric intricacy. Sensitivity rankings create a prevailing hierarchy: Process Control (Compaction) > Skeleton Regulation (D_c) > Phase Filling (Pb) > Gradation Adjustment (k, D_f). D_c is recognized as the principal regulator of VMA, while binder content (Pb) governs VFA. A “Robust Design” methodology is suggested, emphasizing D_c to stabilize the mineral framework and reduce sensitivity to construction variations. A comparative investigation reveals that the optimized gradation (OG) achieves a more stable volumetric condition and enhanced mechanical performance relative to conventional empirical gradations. Specifically, the OG group demonstrated a substantial 112% enhancement in dynamic stability (2617 times/mm compared to 1230 times/mm) and a 75% increase in average film thickness (AFT), while ensuring consistent moisture and low-temperature resistance. In conclusion, this study transforms asphalt mixture design from empirical trial-and-error to a precision-engineered methodology, providing a robust instrument for optimizing the long-term durability of pavements in extreme cold and arid environments. Full article

The archaeological terraces of Petra (southern Jordan) have long been recognized for their role in agriculture and flood mitigation. Despite the dominance of fine-grained sediments behind many terrace walls, these systems exhibit high infiltration capacity and remarkable resistance to erosion. This study investigates the hydrological behavior of terrace-trapped sediments through detailed soil texture, aggregate stability, salinity, and chemical analyses across eight representative sites in and around Petra. Grain-size distributions derived from dry and wet sieving, supplemented by laser diffraction, reveal that dry sieving substantially overestimates sand content due to aggregation of fine particles into unstable peds. Wet analyses demonstrate that many terrace soils are clay- or sandy-clay-dominated yet remain highly permeable. Chemical indicators (nitrate, phosphate, potassium, pH, and salinity) further suggest that terracing enhances downward water movement and salt leaching irrespective of clay content. The nature of the terrace settings and their sediment structure (especially the coarse-grained framework) exerts a stronger control on hydrological functioning than texture alone. The results have direct implications for understanding ancient land management in Petra and for informing sustainable terracing practices in modern arid and semi-arid landscapes, as they are effective both in harvesting water and reducing sediment mobilization. Full article

Background: Ambient yoghurt, also known as room-temperature yoghurt, has gained increasing attention due to its convenience in distribution and consumption without needing cold storage. To ensure extended shelf life, ambient yoghurt normally undergoes an additional heat treatment during manufacturing, the post-fermentation sterilisation process (typically at 65–85 °C), which may induce the formation of fine particle aggregates and result in undesirable textural attributes, particularly graininess. Assessing textural attributes of such products remains a challenge. Methods: By mimicking the oral behaviour of ambient yoghurt, this study uses rheological as well as tribological techniques for objective assessment of the textural sensations of slipperiness and graininess. Various experimental conditions, including the amount of saliva incorporation, sliding speed, and ball-contact and plate-contact lubrication, were examined, and results were analysed against perceived texture by panellists. Main findings: The results indicate that viscosity changes are closely associated with perceived slipperiness under the tested conditions. The friction coefficient obtained from a plate-contact tribometer shows a positive correlation with the sensation of graininess (Pearson’s r was 0.74, p < 0.05, N = 8). It was also observed that a 20% saliva incorporation showed the closest agreement with sensory perception, although this observation should be interpreted cautiously due to the limited sample size. Implications: Results obtained from this work indicate the feasibility of using rheology and tribology techniques for texture prediction in ambient yoghurt. The findings are exploratory in nature, and further studies with larger sample sets are required to validate the proposed approach. The methodology presented here may serve as a reference framework for investigating texture perception in other dairy systems. Full article

The progressive depletion of natural aggregate resources and the increasing emphasis on sustainable construction practices have intensified interest in incorporating recycled concrete aggregate (RCA) into cement-based materials. This study provides a comprehensive evaluation of the influence of partially replacing natural fine aggregate with fine RCA on the physical, mechanical, and durability properties, as well as the microstructure, of cement mortars. Mortar mixtures containing 25%, 50%, 75%, and 100% RCA were tested and compared with a reference mix MC. The experimental program included measurements of bulk density, compressive and flexural strength, water absorption, and freeze–thaw resistance. Additionally, microstructural observations were performed to assess the effect of RCA on the internal structure of matured mortars. The results demonstrated that the intrinsic characteristics of RCA—particularly its higher water absorption and lower density—significantly affected the pore structure and mechanical behavior of the cement mortars. Mortars with RCA exhibited enhanced early-age compressive and flexural strength, especially at substitution levels of 50–100%, attributed to the activation of residual cement paste adhering to the recycled particles. However, increased porosity and water absorption in RCA-based mixes led to a higher sensitivity to freeze–thaw cycles compared with the reference mix. Overall, the findings indicate that incorporating fine RCA up to 50% enables the production of mortars with performance comparable to conventional mixtures under non-freezing conditions, while, under freeze–thaw exposure, comparable performance is achieved at replacement levels up to 25%, contributing to improved resource efficiency and reduced environmental impact. This study confirms the viability of fine RCA in cement mortars, emphasizing the importance of controlling pore structure development to maintain long-term durability. Additionally, it demonstrates that the use of recycled concrete aggregates provides a sustainable alternative to natural sand in mortar production. Full article

Segmentation of tomato fruits under complex lighting conditions remains technically challenging, especially in low illumination or overexposure, where RGB-only methods often suffer from blurred boundaries and missed small or occluded instances, and simple multimodal fusion cannot fully exploit complementary cues. To address these gaps, we propose YOLO-MSRF, a lightweight RGB–NIR multimodal segmentation and refinement framework for robust tomato perception in facility agriculture. Firstly, we propose a dual-branch multimodal backbone, introduce Cross-Modality Difference Complement Fusion (C-MDCF) for difference-based complementary RGB–NIR fusion, and design C2f-DCB to reduce computation while strengthening feature extraction. Furthermore, we develop a cross-scale attention fusion network and introduce the proposed MS-CPAM to jointly model multi-scale channel and position cues, strengthening fine-grained detail representation and spatial context aggregation for small and occluded tomatoes. Finally, we design the Multi-Scale Fusion and Semantic Refinement Network, MSF-SRNet, which combines the Scale-Concatenate Fusion Module (Scale-Concat) fusion with SDI-based cross-layer detail injection to progressively align and refine multi-scale features, improving representation quality and segmentation accuracy. Extensive experiments show that YOLO-MSRF achieves substantial gains under weak and low-light conditions, where RGB-only models are most prone to boundary degradation and missed instances, and it still delivers consistent improvements on the mixed four-light validation set, increasing $mA{P}_{0.5}$ by 2.3 points, $mA{P}_{0.5–0.95}$ by 2.4 points, and mIoU by 3.60 points while maintaining real-time inference at 105.07 FPS. The proposed system further supports counting, size estimation, and maturity analysis of harvestable tomatoes, and can be integrated with depth sensing and yield estimation to enable real-time yield prediction in practical greenhouse operations. Full article

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches. Full article

Concrete waste generated from the demolition of structures constitutes a significant source of waste worldwide. Recycled concrete aggregates (RCA) obtained from this waste exhibit disadvantages such as high porosity and low mechanical strength; therefore, they are not used in pavement structures without improvement. This study investigates the feasibility of using RCA improved with waste-based stabilizers as highway subbase material. RCA was used as fine aggregate and blended with basalt aggregate (BA) at different replacement ratios. The mixtures were subjected to California Bearing Ratio (CBR) tests to determine the optimum RCA content. Subsequently, unconfined compressive strength (UCS) tests were conducted using calcium carbide slag (CCS) as an activator and sewage sludge ash (SSA) as pozzolanic material at various proportions. The experimental results indicated that the mixture containing 35% RCA exhibited the most favorable performance, while higher RCA contents resulted in significant reduction in CBR values. The highest UCS value was obtained in the mixture containing 30% waste additive by weight of RCA with a CCS:SSA ratio of 3:7. For this mixture, CBR reached 315%, and displacement measured in the cyclic plate loading test under a load of 35 kN was 2.5 mm. This mixture provides sustainable and mechanically suitable alternatives for highway subbase applications. Full article

Cells are continuously exposed to physiological and environmental stressors that disrupt homeostasis, triggering adaptive mechanisms such as the integrated stress response (ISR). A central feature of ISR is the selective translation of activating transcription factor 4 (ATF4), which orchestrates gene programs essential for metabolic adaptation and survival. Stress-induced acute ATF4 expression occurs in diverse mammalian cell types and is typically protective; however, chronic activation contributes to pathologies including cancer and neurodegeneration. Canonical ISR (c-ISR) is initiated by phosphorylation of eIF2α in response to stressors such as endoplasmic reticulum or mitochondrial dysfunction, hypoxia, nutrient deprivation, and infections. This modification suppresses global protein synthesis while promoting ATF4 translation through upstream open reading frames (uORFs) in its 5′UTR. Recently, an alternative pathway, split ISR (s-ISR), enabling ATF4 translation independently of eIF2α phosphorylation, was identified in mice, suggesting ISR adaptability, though its relevance in humans remains unclear. Under normal conditions, cap-dependent translation predominates, mediated by the eIF4F complex and requiring the activity of eIF2B at its initial steps. During translational stress, eIF2α phosphorylation inhibits eIF2B activity, resulting in the formation of stalled initiation complexes, which can aggregate into stress granules (SGs). SGs sequester mRNAs and translation initiation factors, further repressing global translation, while ATF4 mRNA largely escapes sequestration, enabling selective translation. This partitioning highlights a finely tuned regulatory mechanism balancing ATF4 expression during stress. Recent advances reveal that, beyond cis-regulatory uORFs, trans-acting factors such as translation initiation factors and associated RNA-binding proteins critically influence ATF4 translation. Understanding these mechanisms provides insight into ISR plasticity and its implications for development, aging, and disease. Full article

With the depletion of natural sand and gravel resources and increasing emphasis on environmental protection, natural aggregates suitable for concrete production are becoming increasingly scarce. Steel slag, a by-product of steelmaking, is produced in substantial quantities yet remains underutilized due to its low recycling rate. Owing to the high strength and excellent compatibility of steel slag particles with cementitious materials, they demonstrate significant potential as a replacement for natural river sand in fine aggregate applications. However, the volumetric instability of steel slag has long been a major impediment to its widespread adoption in cement-based composites. This study examines the stability performance of cement mortar containing steel slag aggregate, with the objective of clarifying the mechanisms responsible for dimensional instability resulting from steel slag incorporation. When the replacement level exceeds 40%, the dimensional stability of the mortar deteriorates markedly. The initial contents of free CaO (f-CaO) and free MgO (f-MgO) in the steel slag were determined to be 1.58% and 1.14%, respectively. Following 50 h of hydrothermal treatment, 69.6% of f-CaO and 44.3% of f-MgO had hydrated, causing internal volumetric expansion and subsequent particle fracturing. Under elevated temperature conditions, over-burned lime demonstrated 220% volumetric expansion and completed its reaction within 40 min, consequently impairing early-age stability. In contrast, periclase (dead-burned MgO) exhibited 34% expansion and attained a reaction degree of merely 13.3%, suggesting a more substantial impact on long-term stability. For each mixture, linear expansion measurements were performed on n = 5 independent specimens, and results are reported as mean ± standard deviation. Full article

This study developed a design framework for porous mixtures using a 100% sustainable non-bituminous epoxy–polyurethane binder system. Conventional design protocols for porous asphalt mixtures exhibit limitations in accurately controlling void content and mixture composition. This study proposed a novel design framework for porous mixtures containing 100% sustainable binder based on statistical analysis and theoretical calculations. The relationships among target air voids, binder content, and aggregate gradation were systematically analyzed, and calculation formulas for coarse aggregate, fine aggregate, and mineral filler contents were derived. A mix design framework was further established by applying the void-filling theory, where the combined volume of binder, fine aggregate, and filler equals the void volume of the coarse aggregate skeleton, thereby ensuring precise control of the target void ratio. Additionally, mixing procedures were investigated with emphasis on feeding sequence, compaction method, and mixing temperature. Results indicated that the optimized feeding sequence significantly improved binder distribution; specimens compacted using the Marshall double-sided compaction method achieved a density of 89.60%. Rheological analysis revealed that at 30 °C, the viscosities of sustainable binder and polyurethane filler were 1280 mPa·s and 6825 mPa·s, respectively, suggesting optimal mixture uniformity. The proposed methodology and process parameters provide essential technical guidance for engineering applications of porous mixtures containing 100% sustainable binder. Full article

Spent garnet (SG) wastes are generated in significant quantities by several industrial activities, including abrasive waterjet cutting (AWJ), abrasive blasting, and filtration and powdered media applications. These wastes represent a promising secondary raw material for the production of sustainable construction materials, particularly green mortars and concretes, through their partial replacement of natural sand in cementitious systems. Such applications are relevant to both hydraulically setting inorganic binders (cement-based materials) and alkali-activated cementitious materials (AACMs). The valorization of SG wastes offers multiple benefits, notably a dual environmental advantage: reducing the consumption of natural aggregates and diverting industrial waste from disposal by integrating it into a new life cycle as a value-added by-product. Additional potential advantages include reduced production costs and possible improvements in the overall performance of mortars and concretes. Despite these benefits, the use of SG as an aggregate replacement remains insufficiently explored, with existing studies providing only preliminary and fragmented evidence of its feasibility. This paper presents an overview of a comprehensive four-year research program investigating SG wastes derived from single-cycle AWJ processes and their incorporation into conventional mortars as partial fine aggregate replacement in cement-based construction composites. The validation stage of the performance assessment expands the range of SG sources by including new sampling from the original suppliers, enabling verification of the repeatability and reproducibility of earlier findings. A broad set of physical, mechanical, and durability properties—particularly resistance to freeze–thaw cycles—is evaluated to achieve a robust and comprehensive material characterization. These results are further correlated with chemical and microstructural analyses, providing critical insights to support the technological transfer of SG-based construction materials to industrial applications with reduced carbon footprint. Full article

Sub-meter-resolution Polarimetric Synthetic Aperture Radar (PolSAR) imagery enables precise building footprint extraction but introduces complex scattering correlated with fine spatial structures. This change renders both traditional methods, which rely on simplified scattering models, and existing deep learning approaches, which sacrifice spatial detail through multi-looking, inadequate for high-precision extraction tasks. To address this, we propose an Orthogonal Dual-Resolution Network (ODRNet) for end-to-end, precise segmentation directly from single-look complex (SLC) data. Unlike complex-valued neural networks that suffer from high computational cost and optimization difficulties, our approach decomposes complex-valued data into its orthogonal real and imaginary components, which are then concurrently fed into a Dual-Resolution Branch (DRB) with Bilateral Information Fusion (BIF) to effectively balance the trade-off between semantic and spatial details. Crucially, we introduce an auxiliary Polarization Orientation Angle (POA) regression task to enforce physical consistency between the orthogonal branches. To tackle the challenge of diverse building scales, we designed a Multi-scale Aggregation Pyramid Pooling Module (MAPPM) to enhance contextual awareness and a Pixel-attention Fusion (PAF) module to adaptively fuse dual-branch features. Furthermore, we have constructed a VHR PolSAR building footprint segmentation dataset to support related research. Experimental results demonstrate that ODRNet achieves 64.3% IoU and 78.27% F1-score on our dataset, and 73.61% IoU with 84.8% F1-score on a large-scale SLC scene, confirming the method’s significant potential and effectiveness in high-precision building extraction directly from SLC. Full article

Mountain treelines are among the most climate-sensitive ecosystems on Earth, yet their fine-scale structural and species level dynamics remain poorly resolved in the Himalayas. In particular, the absence of three-dimensional, crown level measurements have hindered the detection of structural thresholds and species turnover that often precede treeline shifts. To bridge this gap, we introduce UAV LiDAR—applied for the first time in the Hindu Kush Himalayas—to quantify canopy structure and tree species distributions across a steep treeline ecotone in the Manang Valley of central Nepal. High-density UAV-LiDAR data acquired over elevations of 3504–4119 m was used to quantify elevation-dependent changes in canopy stature and cover from a canopy height model derived from the 3D point cloud, while individual tree segmentation and species classification were performed directly on the 3D, height-normalized point cloud at the crown level. Individual trees were delineated using a watershed-based segmentation algorithm while tree species were classified using a random forest model trained on LiDAR-derived structural and intensity metrics, supported by field-validated reference data. Results reveal a sharply defined treeline characterized by an abrupt collapse in canopy height and cover within a narrow ~60–80 m vertical interval. Treeline “threshold” was quantified as a breakpoint elevation from a piecewise model of tree cover versus elevation, and the elevation span over which modeled cover and height distributions rapidly declined from forest values to near-zero. Segmented regression identified a distinct structural breakpoint near 3995 m elevation. Crown-level species predictions aggregated by elevation quantified an ordered turnover in dominance, with Pinus wallichiana most frequent at lower elevations, Abies spectabilis peaking mid-slope, and Betula utilis concentrated near the upper treeline. Species classification achieved high overall accuracy (>85%), although performance varied among taxa, with broadleaf Betula more difficult to discriminate than conifers. These findings underscore UAV LiDAR’s value for resolving sharp ecological thresholds, identifying elevation-driven simplification in forest structure, and bridging observation gaps in remote, rugged mountain ecosystems. Full article