Search Results (353)

Urban expansion and spatial restructuring significantly influence the urban thermal environment. This study investigates the central urban area of Jinan, developing a multi-dimensional spatial structure index system that integrates terrain, 2D/3D morphology, and layout based on multi-source data. Land surface temperature (LST) was derived from remote sensing imagery. Using road networks and triangulated irregular networks (TINs) generated from a digital elevation model (DEM), hybrid analysis units were created. Pearson correlation and bivariate global/local spatial autocorrelation analyses were applied to examine the mechanisms and spatial heterogeneity of how urban spatial structure affects LST. The results showed that (1) LST was strongly associated with urban spatial structure. Among the 12 significantly correlated indicators, building density showed the strongest positive correlation with LST (r = 0.5883), while DEM mean had the strongest negative correlation (r = −0.7444), indicating that compact built-up areas intensified heating, whereas terrain most strongly moderated surface temperature. (2) LST and indicator correlations varied with elevation. LST showed a negative correlation with the standard deviation of DEM, suggesting that greater terrain variability enhances cooling effects. This spatial variation in the dominant drivers of the thermal environment reflects a clear divergence of influencing factors across different elevational zones. The thermal environment exhibits a pronounced north–south split: cooling effects prevail in the south due to terrain, while warming effects dominate in the north due to building forms. (3) Bivariate spatial autocorrelation revealed clear spatial heterogeneity. High–high clustering of LST and spatial structure indicators in the northern plain denoted heat-aggregated zones. Low–low clustering in the topographically complex, sparsely built south formed cold-source zones, and transitional areas showed mixed high–low and low–high clustering. (4) Based on these findings, a zonal governance framework was advocated, prioritizing terrain assessment followed by spatial structure optimization. This promoted a shift from uniform to precise, zone-based thermal environment management, laying a scientific foundation for sustainable spatial planning. Full article

This study proposes a reproducible and accessible methodological framework for non-planar path generation to enable scaffold biofabrication on irregular anatomical surfaces replicating the native morphology of human tissue. By integrating a simulation-based trajectory optimization system with a robotic arm, lattice paths are generated using an intersection-based method with parallel planes. This method is processed by intersecting the anatomical object with orthogonal planes, allowing for the creation of paths that conform to complex geometries. The proposed approach relies on widely available and commonly used tools, such as MATLAB, avoiding the need for highly specialized software. Thus, a MATLAB-based kinematic model computes optimal end-effector trajectories, while a coaxial nozzle facilitates the simultaneous extrusion of an alginate-based biomaterial. The proposed method ensures smooth trajectory execution, achieving positional standard deviation within the reproducibility threshold of the robotic arm for an optimal path discretization density. Unlike conventional planar methods, the optimized approach achieves positional accuracy within the robotic arm’s reproducibility threshold while demonstrating superior geometric conformity on complex anatomical patches. The approach successfully fabricates scaffolds with controlled deposition on anatomical patches, demonstrating improved geometric conformity over traditional planar methods. This method provides a pathway for patient-specific scaffold fabrication, supporting advances in tissue engineering and regenerative medicine. Full article

This study investigates the machinability of Continuous Fiber-Reinforced Thermoplastic Composite (CFRTP) produced via Material Extrusion (MEX) additive manufacturing, focusing on drilling as a critical post-processing step in hybrid manufacturing. CFRTP components, fabricated from 3K carbon fibers and a PLA matrix, were subjected to systematic drilling tests under varying cutting speeds (50–110 m/min) and feed rates (0.06–0.24 mm/rev). Thrust force (Fz) and torque (Mz) were recorded using a high-precision dynamometer to evaluate the influence of cutting parameters on mechanical loads and damage mechanisms. Results indicate that increasing the feed rate significantly increases Fz and Mz, promoting fiber pull-out, delamination, and edge deformation, particularly at hole entry and exit regions. Conversely, higher cutting speeds reduce Fz and Mz due to thermal softening of the PLA matrix, enabling more controlled fiber–matrix interaction. Microscopic analyses revealed that damage severity correlates strongly with mechanical load levels. While high feed rates caused pronounced surface irregularities and matrix smearing, low feed rates combined with high cutting speeds yielded smoother hole morphology and preserved fiber–matrix integrity. The study concludes that optimal drilling conditions for CFRTP materials involve low feed rates and high cutting speeds, minimizing mechanical loads and suppressing damage formation. These findings provide a scientific basis for precision finishing strategies in hybrid manufacturing, enhancing dimensional accuracy and structural reliability of CFRTP components for advanced engineering applications. Full article

Acrylate-based self-polishing copolymer antifouling paint particles (SPC-APPs) are persistent micropollutants that act as carriers for biocidal heavy metals, posing significant ecological hazards to aquatic ecosystems. Despite their toxicity, the occurrence, characterization, and metal-leaching risks of SPC-APPs in estuarine environments remain largely understudied. This study investigated the contamination characteristics of SPC-APPs in surface sediments from the Yangtze River Estuary, a hotspot of shipping activity. A multi-technique analytical protocol was employed, combining density separation with scanning electron microscopy–energy-dispersive spectroscopy (SEM-EDS), inductively coupled plasma mass spectrometry (ICP-MS), and pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) to characterize the morphology, quantify particle abundance, and assess the correlation between SPC-APPs and sedimentary heavy metals. SPC-APPs were ubiquitously detected across all sampling sites, with abundances ranging from (0.82 ± 0.15) × 10³ to (3.65 ± 0.42) × 10³ particles g⁻¹ dry sediment. A distinct distribution property (South Branch > North Branch > offshore shoal) was identified, primarily driven by shipping density and hydrodynamic sorting. Morphologically, particles exhibited irregular, abraded surfaces, with EDS confirming Cu (1.76~5.63 wt%) and Zn (0.27~3.65 wt%) as major metallic components. Py-GC/MS analysis identified specific mass fragments (m/z 41, 69, 87) as diagnostic markers. Strong positive correlations were observed between SPC-APP abundance and sediment Cu (r = 0.82, p < 0.01) and Zn (r = 0.76, p < 0.01) concentrations, indicating that these particles are a primary source of metal contamination. Ecological risk assessment based on sediment quality benchmarks showed that Cu in the South Branch reached 82~91% of the probable effect concentration (PEC), highlighting potential risks to benthic organisms. This study provides critical baseline data on the distribution and speciation of SPC-APPs, underscoring their role as vectors for toxic metals and the need for targeted pollution control in high-shipping-intensity estuarine regions. Full article

The ability to precisely control the morphology of polylactide (PLA) microparticles is crucial for their biomedical applications, yet it is a challenge due to the interdependent nature of key parameters such as size, porosity, and surface topology. This study presents a systematic approach to fabricating PLA microparticles with tunable architecture via emulsion-solvent evaporation by investigating the interplay of polymer molecular weight (44–442 kDa), solution concentration (0.5–20% w/v), and porogen type (PEG, alkanes, lithium salts). We achieved precise size control from 5 to 500 μm, dictated by solution viscosity and the polymer’s crystallization tendency, with poly(L-lactide) yielding irregular particles and poly(D,L-lactide) forming perfect spheres. Furthermore, porogen selection was critical for porosity: alkanes enabled tailored pore networks, with longer chains (e.g., decane) producing larger pores via enhanced phase separation, whereas the double-emulsion method with Li₂CO₃ proved superior for macroporosity due to its slow leaching kinetics. This work provides a foundational guideline for the rational design of PLA microparticles with customized properties for targeted applications in drug delivery and tissue engineering. Full article

During the assembly process of the bolter miner cutting drum, the varying installation postures of the cutting picks result in unique and non-repetitive irregular gaps between the tooth seat bottom surface and the cylindrical rotating surface. Such gaps are constrained by dual-surface geometry and lack batch statistical regularity, making traditional methods such as shim filling, selective assembly, or on-site welding inadequate for achieving high-precision fitting and reliable process implementation. To address this challenge, this paper proposes an automatic design method for compensation bodies based on computer-aided design, realizing a shift from experience dependence to algorithm-driven design. This method transforms the complex dual-surface gap filling problem into a serialized geometric modeling process: first, smooth extrapolation of the tooth seat bottom surface is achieved through a point sequence prediction model based on minimum mean square error; second, surface projection is simplified to boundary curve projection, enabling precise mapping onto the cylindrical surface and generating trimming surfaces; finally, a ruled surface is constructed to integrate the extended surface with the trimming surfaces, automatically generating a compensation body fully adapted to the gap morphology. Case verification demonstrates that this method can automatically and accurately generate compensation bodies that meet dual-surface fitting requirements, significantly improving geometric adaptability and weldability. This research not only resolves a critical technical bottleneck in the assembly of bolter miner cutting drums but also provides a universal and scalable computational framework for the intelligent compensation design of non-repetitive dual-surface gaps in complex equipment. Full article

During fluororubber production, strong material agitation and agglomeration induce severe dynamic fluctuations, irregular surface morphology, and pronounced variations in apparent material level. Under such operating conditions, conventional single-modality monitoring approaches—such as point-based height sensors or manual visual inspection—often fail to reliably capture the true process state. This information deficiency leads to inaccurate valve opening adjustment and degrades material level control performance. To address this issue, valve opening prediction is formulated as a data-driven, control-oriented regression task for material level regulation, and an end-to-end multimodal temporal regression framework, termed MECFN (Multi-Modal Enhanced Cross-Fusion Network), is proposed. The model performs deep fusion of visual image sequences and height sensor signals. A customized Multi-Feature Extraction (MFE) module is designed to enhance visual feature representation under complex surface conditions, while two independent Transformer encoders are employed to capture long-range temporal dependencies within each modality. Furthermore, a context-aware cross-attention mechanism is introduced to enable effective interaction and adaptive fusion between heterogeneous modalities. Experimental validation on a real-world industrial fluororubber production dataset demonstrates that MECFN consistently outperforms traditional machine learning approaches and single-modality deep learning models in valve opening prediction. Quantitative results show that MECFN achieves a mean absolute error of 2.36, a root mean squared error of 3.73, and an ${\mathrm{R}}^2$ of 0.92. These results indicate that the proposed framework provides a robust and practical data-driven solution for supporting valve control and achieving stable material level regulation in industrial production environments. Full article

Recent advances in polymeric composites emphasize the incorporation of natural and mineral fillers to enhance sustainability while maintaining mechanical performance. Studies have shown that lignocellulosic residues and nanostructured clays can improve stiffness and thermal stability, although interfacial compatibility remains a key challenge. This study investigates the mechanical behavior of epoxy composites reinforced with eucalyptus powder and montmorillonite clay, aiming to develop sustainable materials with reduced environmental impact. Formulations containing 5%, 10%, and 20% by volume of each particulate, as well as hybrid combinations, were produced and tested for impact, flexural, and compressive strength. Higher particulate contents were not explored, as fractions above 20% considerably increased viscosity, hindering proper mixing and specimen fabrication. Scanning electron microscopy (SEM) revealed irregular morphologies and heterogeneous dispersion of both fillers. The reduction in impact strength observed across all formulations was mainly attributed to poor interfacial adhesion and void formation, as no chemical or surface treatments were applied to enhance compatibility between the particulates and the epoxy matrix. Conversely, compressive strength improved at low filler contents (5–10%), suggesting a more efficient load transfer under compressive stress. Composites with up to 10% particulate presented a viable balance between mechanical performance and sustainability, showing potential for non-structural applications such as panels, coatings, and eco-friendly construction components. Overall, the results highlight the feasibility of using natural and mineral particulates as sustainable reinforcements, albeit with performance constraints at higher loadings. Full article

In this study, polylactic acid (PLA) was blended with poly(ε-caprolactone) (PCL) and reinforced with nanosilica (SiO₂) to tailor surface characteristics and improve adhesion in biopolymer-based printed packaging applications. The surface microstructure and topography were analyzed using FTIR-ATR, SEM, and surface profilometry. Surface wettability and surface free energy (SFE), along with the adhesion properties of printed ink layers on polymer blends, were assessed, and the optical properties of the substrates and prints were evaluated. SEM revealed that PLA/PCL blends exhibited phase-separated morphologies with PCL droplet domains, whereas incorporation of 3 wt% SiO₂ resulted in finer dispersion and reduced surface irregularities. Surface roughness (Ra) increased from 1.92 µm for PLA/SiO₂ 100/3 to 4.45 µm for PLA/PCL/SiO2 50/50/0, while water contact angle decreased from 70.9° for neat PLA to 43.4° for PLA/SiO₂ 100/3 surface, reflecting enhanced hydrophilicity. SFE components ranged from 26 to 40.7 mJ/m² (dispersive) and 3.2 to 21.5 mJ/m² (polar). Adhesion parameters (interfacial tension ranging from 0.01 to 5.54 mJ/m², work of adhesion from 76.9 to 97.3 mJ/m², and wetting coefficient from 3.04 to 11.1 mJ/m²) indicated favorable ink compatibility for most blends, and optical density of the printed layers (1.85–2.35) confirmed potential for good printability. These findings demonstrate that PLA/PCL/SiO₂ blends allow controlled tuning of surface morphology, wettability, and adhesion, providing a promising approach for biodegradable and print-ready packaging substrates. Full article

Recycled powder (RP), a by-product with a particle size smaller than 150 μm, is generated during the processing of construction and demolition waste (CDW) for recycled aggregate production. RP mainly consists of recycled concrete powder and recycled brick powder. Previous studies have demonstrated that RP can serve as a supplementary cementitious material (SCM) in concrete production. Due to the heterogeneity of parent materials with different ages, service environments, and compositions, the physicochemical properties and reactivity of RP vary significantly, which largely accounts for the inconsistent results reported in the literature. This paper presents a critical review of the application of RP as an SCM in construction. The preparation technologies, chemical and physical properties, microstructural characteristics, and activation methods of RP are systematically examined. Owing to its irregular and rough surface morphology, RP tends to reduce workability and increase water demand when incorporated as an SCM. Nevertheless, when the replacement level and median particle size are limited (typically below 30% and 20 μm, respectively), RP can contribute through micro-filling, nucleation, and limited pozzolanic effects, thereby mitigating adverse impacts on mechanical and durability properties. The mechanisms and effectiveness of mechanical grinding, thermal activation, chemical activation, and CO₂ treatment are comparatively evaluated. Moreover, the incorporation of RP in cement-based materials offers significant economic and environmental benefits. Full article

The sustainable management and valorisation of agricultural and agro-industrial residues are essential to reduce environmental impacts, enhance resource efficiency, and support circular economy strategies. In Mediterranean regions, large quantities of residual biomass are annually produced from olive and citrus supply chains, representing promising feedstocks for biochar production. In this study, biochar was obtained at 600 °C in a fixed-bed reactor under a N₂ atmosphere from four representative feedstocks: olive pruning (OPr), citrus pruning (CPr), olive pomace (OPo), and citrus peel (CPe). The resulting biochar was characterized in terms of physico-chemical, energetic, and structural properties, including proximate and ultimate analyses, fuel properties, cation exchange capacity (CEC), pH, elemental ratios (O/C, H/C, N/C), thermal stability, bulk density, metal content, and surface morphology (SEM), in order to assess parameters relevant to environmental potential applications. The results highlighted clear feedstock-dependent differences. OPoB and CPeB exhibited the highest thermal stability (0.56–0.66), indicating a strong potential for long-term carbon sequestration. CPeB showed the highest CEC (47.2 cmol kg⁻¹). From an application-oriented perspective, this high CEC suggests that, when applied to soil at typical amendment rates (2–5 wt%), CPeB could potentially increase soil CEC by approximately 10–30%, thereby improving nutrient retention and cation availability. Energy yields were highest for citrus-derived biochar (42.0–47.5%), while OPoB exhibited the lowest solid yield due to its higher volatile content. SEM analysis revealed marked structural differences, with OPrB retaining an ordered lignocellulosic porous structure, whereas OPoB and CPeB displayed highly irregular morphologies, favorable for surface reactivity. Overall, this study demonstrates that olive and citrus residues are suitable feedstocks for producing biochar with differentiated properties, and that a rapid screening methodology can support feedstock selection and biochar design for targeted energy, soil amendment, and carbon management applications. Full article

Amine-functionalized solid adsorbents are widely recognized as promising candidates for direct air capture of CO₂; however, their practical deployment remains constrained by humidity-dependent adsorption behavior and poor packed-bed operability arising from irregular particle morphology and fines generation. Rather than focusing solely on maximizing intrinsic adsorption capacity, this study addresses these process-level limitations through an integrated design strategy combining particle morphology control with surface chemistry optimization. Uniform spherical magnesium silicate particles with a mean diameter of approximately 15 μm were synthesized via a water-in-oil emulsion route to suppress fines formation and reduce hydrodynamic resistance. Controlled acid pretreatment was subsequently applied to adjust surface hydroxyl accessibility and enable efficient amine grafting without altering bulk composition. The optimized spherical magnesium silicate amine adsorbents exhibited pronounced humidity-enhanced carbon dioxide capture, achieving capacities of 1.7 to 1.8 millimoles/g at 50% relative humidity, representing an approximately fourfold increase compared with dry conditions. This enhancement is attributed to a humidity-induced mechanistic transition from carbamate formation under dry conditions to water-assisted bicarbonate formation under humid conditions. Complete regeneration was achieved at 100 °C, with stable adsorption desorption behavior maintained over ten consecutive cycles, demonstrating short-term reversibility. These findings highlight morphology controlled scalability. Future work should prioritize durability beyond 100 cycles, mechanical robustness, and techno-economic viability at scale. Full article

Surface defects in hydraulic concrete structures exhibit extreme topological heterogeneity. and are frequently obscured by unstructured environmental noise. Conventional detection models, constrained by fixed-grid convolutions, often fail to effectively capture these irregular geometries or suppress background artifacts. To address these challenges, this study proposes the Adaptive Local–Global Synergistic Perception Network (ALGSP-Net). First, to overcome geometric constraints, the Defect-aware Receptive Field Aggregation and Adaptive Dynamic Receptive Field modules are introduced. Instead of rigid sampling, this design adaptively modulates the receptive field to align with defect morphologies, ensuring the precise encapsulation of slender cracks and interlaced spalling. Second, a dual-stream gating fusion strategy is employed to mitigate semantic ambiguity. This mechanism leverages global context to calibrate local feature responses, effectively filtering background interference while enhancing cross-scale alignment. Experimental results on the self-constructed SDD-HCS dataset demonstrate that the method achieves an average Precision of 77.46% and an mAP50 of 72.78% across six defect categories. Comparative analysis confirms that ALGSP-Net outperforms state-of-the-art benchmarks in both accuracy and robustness, providing a reliable solution for the intelligent maintenance of hydraulic infrastructure. Full article

Background: Durable bonding to zirconia remains difficult because its chemically inert surface resists acid etching. Additive manufacturing (AM) enables controlled surface morphology, which may enhance micromechanical retention without additional treatments. Methods: Zirconia specimens with three AM-derived surface designs—(1) concave–convex hemispherical patterns, (2) concave hemispherical patterns, and (3) as-printed surfaces—were fabricated using a slurry-based 3D printing system and sintered at 1500 °C. Zirconia specimens fabricated by subtractive manufacturing using CAD/CAM systems, polished with 15 µm diamond lapping film and with or without subsequent alumina sandblasting, served as controls. Surface morphology was analyzed by FE-SEM, and shear bond strength (SBS) was tested after cementation with a resin-based luting agent. Results: SEM revealed regular layered textures and designed hemispherical structures (~300 µm) in AM specimens, along with step-like irregularities (~40 µm) at layer boundaries. The concave–convex AM group showed significantly higher SBS than both sandblasted and polished subtractive-manufactured zirconia (p < 0.05). Vertically printed specimens demonstrated greater bonding strength than those printed parallel to the bonding surface, indicating that build orientation affects resin infiltration and interlocking. Conclusion: AM-derived zirconia surfaces can provide superior and reproducible micromechanical retention compared with conventional treatments. Further optimization of printing parameters and evaluation of long-term durability are needed for clinical application. Full article

Defect detection on Printed Circuit Boards (PCBs) constitutes a pivotal component of the quality control system in electronics manufacturing. However, owing to the intricate circuitry structures on PCB surfaces and the characteristics of defects—specifically their minute scale, irregular morphology, and susceptibility to background texture interference—existing generic deep learning models frequently fail to achieve an optimal equilibrium between detection accuracy and inference speed. To address these challenges, this study proposes MDEB-YOLO, a lightweight real-time detection network tailored for PCB micro-defects. First, to enhance the model’s perceptual capability regarding subtle geometric variations along conductive line edges, we designed the Efficient Multi-scale Deformable Attention (EMDA) module within the backbone network. By integrating parallel cross-spatial channel learning with deformable offset networks, this module achieves adaptive extraction of irregular concave–convex defect features while effectively suppressing background noise. Second, to mitigate feature loss of micro-defects during multi-scale transformations, a Bidirectional Residual Multi-scale Feature Pyramid Network (BRM-FPN) is proposed. Utilizing bidirectional weighted paths and residual attention mechanisms, this network facilitates the efficient fusion of multi-view features, significantly enhancing the representation of small targets. Finally, the detection head is reconstructed based on grouped convolution strategies to design the Lightweight Grouped Convolution Head (LGC-Head), which substantially reduces parameter volume and computational complexity while maintaining feature discriminability. The validation results on the PKU-Market-PCB dataset demonstrate that MDEB-YOLO achieves a mean Average Precision (mAP) of 95.9%, an inference speed of 80.6 FPS, and a parameter count of merely 7.11 M. Compared to baseline models, the mAP is improved by 1.5%, while inference speed and parameter efficiency are optimized by 26.5% and 24.5%, respectively; notably, detection accuracy for challenging mouse bite and spur defects increased by 3.7% and 4.0%, respectively. The experimental results confirm that the proposed method outperforms state-of-the-art approaches in both detection accuracy and real-time performance, possessing significant value for industrial applications. Full article