Search Results (557)

Accurately predicting machining-induced deformation is crucial for high-precision CNC turning, particularly when working with dissimilar metallic alloys. This study presents a novel, data-driven framework that integrates empirical deformation analysis, multivariate regression, and principal component analysis (PCA) to predict axial deformation as a function of intrinsic material properties, including Brinell hardness, thermal conductivity, and Young’s modulus. The approach begins with second-order polynomial modeling of experimentally observed force–deformation behavior, from which three physically interpretable coefficients, nonlinear (a), load-sensitive (b), and intercept (c), are extracted. Each coefficient is then modeled using log-linear power-law regression, revealing strong statistical relationships with material properties. Specifically, the nonlinear coefficient correlates predominantly with thermal conductivity, while both the linear and offset terms are governed mainly by hardness, with average R² values exceeding 0.999 across all materials. To improve physical insight and reduce dimensionality, three non-dimensional ratios (H/E, k/E, H/k) are also introduced, enhancing correlation and interpretability. PCA further confirms that over 93% of the total variance in deformation behavior can be captured using just two principal components, with clear separation of materials based on thermomechanical signature and deformation coefficients. This is the first comprehensive study to unify empirical modeling, property-driven regression, and PCA for deformation prediction in CNC-machined alloys. The resulting framework offers a scalable, interpretable, and physically grounded alternative to black-box models, providing rapid screening of new materials, reduced experimental demand, and support for smart manufacturing applications, such as digital twins and material-informed process optimization. Full article

In selective laser melting (SLM), real-time visual inspection of powder spreading quality is essential for maintaining dimensional accuracy and mechanical performance. However, reflections from metallic chamber walls introduce non-uniform illumination and reduce local contrast, hindering reliable defect detection. To overcome this problem, a chamber-reflection-aware image enhancement method is proposed, integrating a physical reflection model with a dual-channel deep network. A Gaussian-based curved-surface reflection model is first developed to describe the spatial distribution of reflective interference. The enhancement network then processes the input through two complementary channels: a Retinex-based branch to extract illumination-invariant reflectance components and a principal components analysis (PCA)-based branch to preserve structural information. Furthermore, a noise-aware loss function is designed to suppress the mixed Gaussian–Poisson noise that is inherent in SLM imaging. Experiments conducted on real SLM monitoring data demonstrate that the proposed method significantly improves contrast and defect visibility, outperforming existing enhancement algorithms in peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). The approach provides a physically interpretable and robust preprocessing framework for online SLM quality monitoring. Full article

The dynamics of gas–liquid two-phase flow at fracture junctions are crucial for optimizing fluid transport in the complex fracture networks of coal seams, particularly for coalbed methane (CBM) extraction and gas hazard management. This study presents a comprehensive numerical investigation of transient air–water flow in a two-dimensional, symmetric, cross-shaped fracture junction. Using the Volume of Fluid (VOF) model coupled with the SST k-ω turbulence model, the simulations accurately capture phase interface evolution, accounting for surface tension and a 50° contact angle. The effects of inlet velocity (0.2 to 5.0 m/s) on flow patterns, pressure distribution, and energy dissipation are systematically analyzed. Three distinct phenomenological flow regimes are identified based on interface morphology and force balance: an inertia-dominated high-speed impinging flow (Re > 4000), a moderate-speed transitional flow characterized by a dynamic balance between inertial and viscous forces (∼1000 < Re < ∼4000), and a viscous-gravity dominated low-speed creeping filling flow (Re < ∼1000). Flow partitioning at the junction—defined as the quantitative split of the total inflow between the main (straight-through) flow path and the deflected (lateral) paths—exhibits a strong dependence on the Reynolds number. The main flow ratio increases dramatically from approximately 30% at Re ∼ 500 to over 95% at Re ∼ 12,000, while the deflected flow ratio correspondingly decreases. Furthermore, the pressure loss (head loss, ΔH) across the junction increases non-linearly, following a quadratic scaling relationship with the inlet velocity (ΔH ∝ V₀^1.95), indicating that energy dissipation is predominantly governed by inertial effects. These findings provide fundamental, quantitative insights into two-phase flow behavior at fracture intersections and offer data-driven guidance for optimizing injection strategies in CBM engineering. Full article

Enhancing the carbon sequestration (CS) capacity of urban green spaces is crucial for mitigating global warming, environmental degradation, and urbanisation-induced issues. This study focuses on the urban community unit to establish a system of determining factors for the CS capacity of green space, considering the built-up spatial pattern and green space morphology. An interpretable machine learning approach (Random Forest + Shapley Additive exPlanations) is employed to systematically analyse the non-linear relationship of built-up spatial pattern and green space morphology factors. Results demonstrate significant urban zonal heterogeneity in green space CS, whereas southern suburban area communities exhibited higher capacity. In terms of green space morphology factors, higher fractional vegetation cover (FVC) and cohesion were positively correlated with green space CS capacity. Leaf area index (LAI), canopy density (CD), and the evergreen-broadleaf forest ratio additionally further enhanced the positive effect of two-dimensional green space factors on CS. For built-up spatial pattern factors, communities with a high green space ratio and low development intensity exhibited higher CS capacity. And the optimal ranges of FVC, LAI and CD for effective facilitation of community green space CS were identified as 0.6–0.75, 4.85–5.5 and 0.68–0.7, respectively. Moreover, cohesion, LAI and CD bolstered the CS capacity in communities with a high building density and plot ratio. This study provides a rational basis for planning and layout of green space patterns to enhance CS efficiency at the urban community scale. Full article

Against the backdrop of intensifying global climate change and human activities, the increasing frequency and evolution of major wildfire events pose severe challenges to global disaster prevention and mitigation systems. Systematically understanding their disaster characteristics, spatiotemporal patterns, and societal response efficacy is an urgent scientific requirement for formulating effective coping strategies. This study constructed a comprehensive database covering 137 major global wildfire events from 2018 to 2024, with data sourced from GFED, EM-DAT, and official national reports. Utilizing a synthesis of methods including descriptive statistics, spatiotemporal clustering analysis, K-means pattern recognition, and non-parametric tests, a multi-dimensional quantitative analysis was conducted on disaster characteristics, evolutionary trends, casualty patterns, and policy effectiveness. Despite potential reporting biases and heterogeneous data standards across countries, the analysis reveals the following: (1) All key wildfire metrics (e.g., burned area, casualties, evacuation scale) exhibited extreme right-skewed distributions, indicating that a minority of catastrophic events dominate the overall risk profile; (2) Global wildfire hotspots demonstrated dynamic expansion, spreading from traditional regions in North America and Australia to emerging areas such as Mediterranean Europe, Chile, and the Russian Far East, forming three significant spatiotemporal clusters; (3) Four distinct casualty patterns were identified: “High-Lethality”, “Large-Scale Evacuation”, “Routine-Control”, and “Ecological-Destruction”, revealing the differentiated formation mechanisms under various disaster scenarios; (4) A substantial gap of nearly 65 times in emergency evacuation efficiency—defined as the ratio of evacuated individuals to total casualties—was observed between developed and developing countries, highlighting a significant “development gap” in emergency management capabilities. This study finds evidence of increasing extremization, expansion, and polarization in global wildfire risk within the 2018–2024 event sample. The conclusions emphasize that future risk management must shift from addressing “normal” events to prioritizing preparedness for “catastrophic” scenarios and adopt refined strategies based on casualty patterns. Simultaneously, the international community needs to focus on bridging the emergency response capability gap between nations to collectively build a more resilient global wildfire governance system. Full article

The global trend in gluten-free snack innovation involves using naturally gluten-free grains as a nutrient-rich foundation, enriching formulations with multifunctional plant and microbial proteins, and optimizing ingredient interactions to balance nutritional demands with structural integrity. The study demonstrates that blending proso millet flour with yeast-derived and almond (50:50 ratio) proteins effectively produces a protein- and fiber-rich gluten-free dough suitable for 3D printing, without the need for synthetic additives. This approach aligns with the growing demand for clean-label, sustainable protein sources that enable the creation of healthy, stable, and appealing ready-to-eat snacks. The enriched dough demonstrated superior rheological behavior, characterized by a dominant elastic modulus (G′ > G″), enabling smooth extrusion and stable shape retention. Nutritional analysis revealed an increase in protein (28.16 vs. 13.26 g/100 g DB) and dietary fiber (12.17 vs. 6.22 g/100 g DB) compared to the control. The amino acid profile improved significantly, with 48% more essential amino acids and a 63% increase in non-essential amino acids. Dimensional accuracy improved, and post-processing deformation was reduced, confirming enhanced structural integrity. Texture analysis showed no significant increase in hardness, maintaining a desirable texture profile despite higher protein content. Sensory evaluation confirmed greater acceptance of the enriched snack, especially in terms of flavor, aroma, and smell, while preliminary cost assessment indicated that, despite higher ingredient costs, the enriched formulation remains economically feasible. Additional optimization of protein concentration and processing conditions could enhance the overall functionality even further. Full article

Intrusive phase-detection probes remain a standard tool for local characterization of gas–liquid bubbly flows, but their accuracy is strongly affected by probe geometry and bubble–probe interaction kinematics. This work presents a Monte Carlo-based framework to evaluate four-sensor intrusive probes in bubbly flow, relaxing the classical assumptions of spherical bubbles and purely axial trajectories. Bubbles are represented as spheres or ellipsoids, a broad range of non-dimensional probe geometries are explored, and local quantities such as interfacial area concentration, bubble and flux velocities, and chord lengths are recovered from synthetic four-sensor signals. The purpose of the framework is threefold: (i) it treats four-sensor probes in a unified way for interfacial area, velocity, and chord length estimation; (ii) it includes ellipsoidal bubbles and statistically distributed incidence angles; and (iii) it yields compact correction laws and design maps expressed in terms of the spacing-to-diameter ratio $a_p/D$, the dimensionless probe radius $r_p/D$, and the missing ratio $m_r$ (defined as the fraction of bubbles that cross the probe footprint without being detected), which can be applied to different intrusive four-sensor probes. The numerical results show that, within a recommended geometric range $0.5\le a_p/D\le 2$ and $r_p/D\le 0.25$ and for missing ratios $m_r\le 0.7$, the axial velocity $V_z$ estimates the bubble centroid velocity and its projection with typical errors within $\pm 10\%$, while a chord length correction $C{L}_{corr}\left(m_r\right)$ recovers the underlying chord length distribution with a residual bias of only a few percent. The proposed interfacial area correction, written solely in terms of $m_r$, remains accurate in polydisperse bubbly flows. Outside the recommended $(a_p/D,r_p/D)$ range, large probe radius or extreme tip spacing lead to velocity and chord length errors that can exceed 20–30%. Overall, the framework provides quantitative guidelines for designing and using four-sensor intrusive probes in bubbly flows and for interpreting their measurements through geometry-aware correction factors. Full article

Coral reefs are integral components of tropical coastal marine ecosystems that have considerable capacity to mitigate extreme flows and marine floods caused by storms and tsunamis. However, limited studies on coral reef efficacy in reducing such flows, coupled with variable roughness coefficient characteristics, hinder their broader utilization in sustainable engineering applications for societal benefit. In this study, we conducted comprehensive experimental investigations to examine flow–coral interactions and the flow energy reduction capabilities of coral reefs. Three-dimensional-printed coral reefs were used to simulate actual coral reefs, providing a scalable and environmentally responsible approach for studying nature-based coastal protection systems. Flow characteristics within the coral reef were investigated through flow depth and velocity measurements taken at the front of, over, and behind the reef. Analysis was performed considering nondimensional parameters, i.e., the Froude number (Fr), the depth effect (DE; ratio of flow depth to coral height), and the size effect (SE; ratio of coral length to coral height), to assess the flow energy reduction under different coral combinations and flow conditions. Spatial variations in flow depth over the reef showed that fast and shallow flows exhibited a reduction gradient toward the back of the reef. The findings revealed a substantial reduction in flow depth and velocity, reaching up to 27.5% and 25%, respectively, at the back boundary of the coral. Two-layered velocity analyses showed that the velocity over the top of corals could be six times higher than that through the coral reef structure for deep flows. Manning’s roughness coefficient varied considerably from 0.03 to 0.26. Overall, this study contributes to sustainable coastal engineering by demonstrating how bio-inspired coral reef structures can be applied to reduce flow energy and enhance coastal resilience in an environmentally adaptive manner. Full article

Additive and coating technologies, such as high-velocity oxy-fuel (HVOF) thermal spraying and direct metal laser sintering (DMLS), often require extensive post-processing to meet dimensional and surface quality requirements, which remains challenging for nickel-based superalloys such as Inconel 718. This study presents the design and topology optimisation of a cutting tool with a linear cutting edge, capable of operating in turn-milling or turning modes, offering a viable alternative to conventional grinding. A non-optimised tool served as a baseline for comparison with a topology-optimised variant improving cutting-force distribution and stiffness-to-mass ratio. Finite element analyses and experimental turn-milling trials were performed on DMLS and HVOF Inconel 718 using carbide and CBN inserts. The optimised tool achieved significantly reduced roughness values: for DMLS, Ra decreased from 0.514 ± 0.069 µm to 0.351 ± 0.047 µm, and for HVOF from 0.606 ± 0.069 µm to 0.407 ± 0.069 µm. Rz was similarly improved, decreasing from 4.234 ± 0.343 µm to 3.340 ± 0.439 µm (DMLS) and from 5.349 ± 0.552 µm to 4.521 ± 0.650 µm (HVOF). The lowest measured Ra, 0.146 ± 0.030 µm, was obtained using CBN inserts at the highest tested cutting speed. All improvements were statistically significant (p < 0.005). No measurable tool wear was observed due to the small engagement and the use of a fresh cutting edge for each pass. The resulting surface quality was comparable to grinding and clearly superior to conventional turning. These findings demonstrate that combining topology optimisation with a linear-edge tool provides a practical and efficient finishing approach for additively manufactured and thermally sprayed Inconel 718 components. Full article

Deep alterations in tumor cell gene profiles resulting in the loss of their specific functions are frequently the cause of resistance to traditional cancer treatments. Therefore, reprogramming the expression pattern of cancer cells toward a differentiated phenotype represents a promising therapeutic strategy. In this study, we investigated whether resveratrol (RSV) and its natural analogs—3,4′,5-trimethoxystilbene (3-MET-OX) and isorhapontigenin (ISOR-H-PG)—can modulate the SOX2/SOX17 balance and promote re-differentiation in anaplastic thyroid cancer (ATC) cells. Two human ATC cell lines (SW1736 and 8505c) and non-tumoral thyroid cells (Nthy-ori 3-1) were cultured in two-dimensional (2D) or three-dimensional (3D) systems and treated with polyphenols at sub-cytotoxic doses. In 2D cultures, cell viability and cell cycle analyses confirmed a cytostatic effect characterized by G1 arrest. In 3D cultures, polyphenol treatment caused morphological disruption of ATC spheroids and significantly modulated the gene expression profile. RSV and 3-MET-OX reduced stemness markers (SOX2, NANOG), increased the thyroid lineage transcription factor (SOX17), and enhanced differentiation genes (TTF-1, TPO, NIS). Overall, these results support our hypothesis that modulation of the SOX2/SOX17 ratio by polyphenols provides a mechanistic basis for re-differentiation, thereby improving therapeutic responsiveness in ATC. Full article

To characterize the key odorants and elucidate the flavor profiles of compound fermented beverages after fermentation, single-compound fermented beverages (GW, AW) and a compound fermented beverage (CW) were prepared using Italian Riesling grapes and SirPrize apples as raw materials. The flavor and metabolite profiles were systematically analyzed by integrating flavoromics (comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry, GC × GC–TOF MS) and metabolomics (LC–MS/MS). The results demonstrated that CW exhibited the most favorable acid/reducing sugars (2.18), imparting a drier taste and superior stability. Compounds with relative odor activity values (rOAV) greater than 1—including 3-methyl-1-butyl acetate, ethyl hexanoate, ethyl butanoate, and ethyl octanoate—collectively contributed prominent fruity, floral, and sweet aromas to all three wine types. Ethyl decanoate provided an additional distinctive traditional fruity note specifically to AW, while 1-octen-3-ol contributed a mushroom-like aroma to both GW and CW. Moreover, 3-methylbutanal, 4-ethyl-2-methoxyphenol, and ethyl 3-methylbutanoate added additional significant aroma contributions to CW, imparting floral, clove-like, and fruity notes, respectively. Notably, ethyl hexanoate (fruity aroma) exhibited a remarkably high rOAV of 27.43 in CW, significantly surpassing its levels in the single-substrate fermentations. Lipid metabolism and the phenylpropanoid pathway were significantly activated in CW, facilitating the coordinated synthesis of esters and phenolic compounds. Sensory attribute network analysis further confirmed that CW possessed more pronounced “sweet”, “fruity”, and “floral” characteristics. Correlation analysis revealed significant relationships between volatile organic compounds (VOCs) and total soluble solids (TS), titratable acidity (TA), the TA/TS ratio, and metabolite levels, underscoring the close connections among physicochemical properties, precursor/intermediate metabolites, and flavor formation. Comprehensive analysis of non-volatile metabolites and flavor-associated VOCs revealed variety-specific characteristics and compounding effects, providing valuable insights for enhancing the quality of compound fermented beverages. Full article

Positioning technology based on 5G networks has been deeply integrated into everyday life. Despite this, severe non-line-of-sight (NLOS) conditions in wireless signal environments can cause signal obstructions, negatively impacting the precision and dependability of positioning services. This paper introduces an innovative algorithm called Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Non-Line-of-Sight Angle of Arrival (STAR-RIS NLOS AOA) to address these challenges. The algorithm initially develops a system model named 5G STAR-RIS localization (GSL). By integrating STAR-RIS into the system, the model effectively overcomes the challenges of positioning in NLOS scenarios. The inclusion of STAR-RIS not only boosts the system’s adaptability but also meets the positioning requirements for users on both sides of the reflective surface simultaneously. The algorithm then utilizes the Root-MUSIC algorithm for estimating user coordinates. An optimization problem is formulated based on these estimations, with the goal of reducing the gap between estimated and real coordinates. To address this optimization, the Inertia Weight Whale Optimization Algorithm is employed, providing high-precision estimations of users’ three-dimensional positions. Simulations reveal that the proposed Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Non-Line-of-Sight Angle of Arrival (SRNA) algorithm substantially outperforms conventional algorithms in positioning performance across different signal-to-noise ratio contexts. Specifically, in challenging NLOS situations, the SRNA algorithm can cut positioning errors by 50% to 62%, demonstrating its outstanding capability and efficiency in addressing the difficulties presented by NLOS conditions within 5G-based positioning systems. Full article

A single-plunger two-dimensional electro-hydraulic pump is an integrated unit in which a two-dimensional plunger pump is embedded inside the rotor of a permanent magnet synchronous motor, significantly improving the power density and power-to-weight ratio of electro-hydraulic pumps. The pursuit of a higher power-to-weight ratio has made high-speed operation and high-pressure output persistent research priorities. However, during the iterative design process of electro-hydraulic pumps, cavitation has been identified as a common issue, leading to difficulties in oil suction and even severe backflow. Based on the structure and motion characteristics of the single-plunger two-dimensional electro-hydraulic pump, a CFD numerical model was established to analyze the influence of different working conditions on the cavitation characteristics inside the pump. The study shows that cavitation mainly occurs in the plunger chamber, the distribution groove, and the triangular damping groove. The location and intensity of cavitation are directly reflected by the gas volume fraction. The simulation analysis of variable operating conditions has verified that suction pressure and rotational speed have a significant impact on cavitation—an increase in suction pressure can effectively suppress cavitation, while an increase in rotational speed will exacerbate cavitation development. Specifically, the non-cavitation working boundary of this type of pump was determined through theoretical derivation, and the coupling relationship between critical suction pressure and critical speed was clarified. This work provides an important theoretical basis for the optimization design of the new integrated electro-hydraulic pump. Full article

Bridge columns, as core load-bearing and force-transferring components in bridge structures, are highly susceptible to partial damage and even global failure when subjected to close-in explosions. Therefore, analyzing the damage characteristics of reinforced concrete (RC) bridge columns under such blast scenarios and developing corresponding damage assessment methods are significant. In this study, a high-fidelity three-dimensional numerical model of an RC bridge column was developed in ANSYS/LS-DYNA and validated against field blast experimental measurements. Using the verified model, the typical failure processes and damage mechanisms of the column were systematically investigated. According to the extent of cross-sectional damage, the failure modes of the column were classified into three types: non-spalling, spalling, and breaching. Additionally, the influence of initial axial load was considered, and the regions for different failure modes were analyzed. Finally, on the basis of the analysis of failure modes and residual capacity, a material loss-based damage index P was proposed, and the relationship curves between residual capacity-based damage indices D and P under different damage modes were established. Using the control variable method, the relationship between these two indices under the influence of a single parameter was further explored, and empirical formulas were derived to express the correlations among longitudinal reinforcement ratio, damage index D, and damage index P under both non-spalling and spalling damage modes. Full article

Unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) is a transformative paradigm that delivers ubiquitous communication and computing services for next-generation wireless networks. By incorporating a reconfigurable pinching antenna (PA) system, this paper proposes a novel framework to enhance energy efficiency in UAV-aided uplink communication, effectively addressing mobility-related challenges such as line-of-sight (LoS) propagation, Doppler effects, and stringent energy constraints. The framework jointly optimizes UAV trajectories, task offloading ratios, transmit powers, and PA positions to minimize total energy consumption while ensuring reliable data rates, collision avoidance, and comprehensive coverage of ground target points. A mixed-integer non-linear program is formulated, which is efficiently solved using a block coordinate descent (BCD) algorithm combined with successive convex approximation (SCA) and one-dimensional grid search. The simulation results demonstrate that the proposed approach reduces energy consumption by 20–45% compared to baseline methods while maintaining robust communication performance and near-perfect coverage across diverse system configurations. Full article