Tetra-hybrid nanofluids, formed by dispersing four distinct nanoparticles in a base fluid, offer enhanced thermal conductivity and momentum transport compared to mono-, hybrid-, and tri-hybrid nanofluids, making them attractive for advanced thermal management systems. This study numerically investigates the magnetohydrodynamic (MHD) flow and heat transfer of an Al₂O₃-Cu-SiO₂-TiO₂/water tetra-hybrid nanofluid over a permeable stretching sheet, incorporating Boger (viscoelastic) fluid characteristics, Darcy–Forchheimer porous drag, thermal radiation, velocity and thermal slip, heat source/sink, and viscous dissipation effects. The governing partial differential equations are transformed into a system of nonlinear ordinary differential equations using appropriate similarity variables and solved numerically via the bvp4c method in MATLAB. The present numerical scheme is validated against previously published results for limiting cases, showing close agreement. Results indicate that increasing the magnetic parameter and velocity slip parameter jointly reduce heat transfer efficiency, while the Boger fluid relaxation parameter enhances the velocity profile and the retardation parameter suppresses it. A deep neural network (DNN) surrogate model is additionally employed to predict the skin friction coefficient and Nusselt number across the parameter space, showing smooth, physically consistent trends and confirming the reliability of the numerical solution. These findings provide insight into controlling momentum and heat transport in tetra-hybrid nanofluid systems, with potential applications in cooling technologies, biomedical devices, and thermal management in industrial processes. It is noticed that, the TiO₂–SiO₂–Cu–Al₂O₃/water quadra-hybrid nanofluid provides a 23.63% enhancement in the Nusselt number relative to the base-fluid case, demonstrating its superior thermal transport capability. The Nusselt number enhances 5.6% improvement with relaxation parameter in observed.
Timely identification and efficient remediation of subsurface road voids are imperative for ensuring traffic safety and extending the service life of road infrastructure. Addressing the limitations of traditional Ground Penetrating Radar (GPR) void interpretation—specifically low efficiency, susceptibility to complex backgrounds and noise, and challenges stemming from small target sizes and multi-scale variations—this paper proposes an advanced GPR void detection model based on an improved YOLOv11, designated as SMW-YOLO. The model integrates Space-to-Depth Convolution (SPDConv) within the backbone network to preserve fine-grained structural details, and employs a global attention feature extraction algorithm integrated with an enhanced Multi-Scale Dilated Attention (MSDA) mechanism to effectively capture multi-scale void features. Furthermore, an attention-guided bounding box loss function is constructed using Wise-IoU to improve regression accuracy. Experimental results demonstrate that the SMW-YOLO model achieves a precision of 94.7%, a recall of 88%, an mAP50 of 96.1%, an mAP50-95 of 47.6%, and an F1-score of 91%, significantly outperforming the baseline model and various popular detection algorithms. The model also exhibits robust performance in cross-domain testing, offering a high-efficiency and high-precision technical solution for GPR-based road void detection.
This study evaluated bottom ash combined with activated slag (BA-AS) as a sustainable alternative to bottom ash-cement (BA-CE) for building brick production. Composites were prepared at BA: binder ratios of 90:10 to 40:60 with a liquid-to-solid ratio of 0.15, thermally cured at 80°C to determine the optimum mix, and then cured at room temperature for 7, 14, and 28 days. Mechanical and durability performance (UCS, water absorption, porosity, wet-dry cycles, and efflorescence) were assessed alongside characterisation by XRF, XRD, FTIR, SEM, and PSD. The 60BA:40AS mix achieved the highest UCS of 9.74 MPa after thermal curing and 12.1 MPa at 28 days, meeting ASTM C270 standards for load-bearing masonry, whereas BA-CE reached only 4.55 MPa, suitable only for non-load-bearing applications. BA-AS bricks exhibited lower porosity (26%) and water absorption (18%) than BA-CE (31% and 22%, respectively), though BA-AS showed moderate efflorescence due to its alkali content. Wet-dry cycling resulted in a net UCS gain of 1.5% in BA-AS, attributed to densification from activated slag bonding. XRD, SEM, and FTIR confirmed amorphous aluminosilicate gel formation in BA-AS versus crystalline C-S-H and calcite phases in BA-CE. TCLP testing confirmed all heavy metals remained below regulatory limits in both systems, with BA-AS demonstrating stronger chemical stabilisation.
Water scarcity remains a major challenge in arid and semi-arid regions. Sorption-based atmospheric water harvesting (AWH) systems usually suffer from high energy consumption, and some emerging adsorbents still involve uncertainty in toxicity and chemical stability. To address these issues, a novel AWH system by silica-gel-based two-stage desiccant wheel is investigated through theoretical simulations, experiments, and a scenario assessment under varied diurnal ambient conditions. Theoretical analysis shows that, for the proposed two-stage system in parallel under the same external ambient, water production rate is increased by 10.9%, and exergy efficiency is improved by 7.3% compared with the conventional single-stage system. Experimental results indicate that the system achieves a water production rate of 4.9 g·h⁻¹ and an exergy efficiency of 47.2% under a controlled condensation boundary of 17°C. Based on the experimentally calibrated model, scenario analyses are conducted for typical arid and semi-arid regions, including Dunhuang, Wuwei, Hami, and Almaty. The scenario comparisons show that, by utilizing environmental thermal energy during condensation through condenser-side heat rejection under diurnal ambient variation, the daily-average water-production rate is increased by 2.91 g·h⁻¹, and the exergy efficiency is enhanced by 105% compared with the reference case without such utilization. Overall, the results indicate that the new AWH system coordinating a two-stage silica-gel-based desiccant wheel with favorable day–night ambient may effectively improve its main performance of water-harvesting in regions with pronounced diurnal climatic changes.
Mitigating anthropogenic CO₂ emissions remains a critical challenge for achieving net-zero climate targets. Here, we report a synergistic photocatalytic–alkaline hybrid process that integrates TiO₂-assisted photochemical activation with alkaline absorption to achieve enhanced CO₂ capture efficiency. The hybrid system was systematically optimized using response surface methodology (RSM) with a D-optimal design to evaluate the effects and interactions of pH, temperature, solution volume, UV intensity, and photocatalyst loading. The results reveal that UV-activated TiO₂ significantly accelerates CO₂ dissolution and conversion kinetics, yielding a removal efficiency of over 71%, which exceeds that of conventional alkaline absorption by more than 15%. Mechanistic analysis indicates that the synergistic enhancement arises from the photoinduced generation of reactive oxygen species (•OH, •O₂⁻) that facilitate the transformation of dissolved CO₂ into bicarbonate and carbonate species, thereby bridging chemical absorption and photochemical conversion pathways. The developed quadratic regression model exhibited strong predictive reliability (R² = 0.96, desirability = 0.98) and accurately captured nonlinear parameter interactions. Optimal performance was achieved at pH 9, 40°C, UV intensity 12 W, and TiO₂ loading 1000 mg L⁻¹. This study establishes a scalable, energy-efficient, and environmentally benign CO₂ capture route that merges alkaline absorption with photocatalytic activation, offering a viable platform for next-generation carbon mitigation and utilization technologies.
Welding flux slag (WFS) is a waste product from the submerged arc welding industry, typically disposed of in landfills. The reuse of WFS aligns with circular economy strategies and contributes to reducing landfill volumes. This work evaluates the feasibility of incorporating WFS as a partial Portland cement replacement at levels of 6% and 35%. The mechanical properties, durability against acid attack and carbonation, eco-efficiency, and cost implications of WFS-incorporated cement were assessed following European standards. Results indicate that higher WFS contents led to increased porosity and reduced compressive strength, mainly due to lower C-S-H formation and changes in matrix densification. Even so, mortars with 6% WFS complied with the requirements for CEM II/A-S 42.5N, while those with 35% WFS still met the minimum strength class for CEM II/B-S 32.5N. Additionally, WFS-containing cement demonstrated improved resistance to sulfuric acid attack and promising long-term eco-efficiency performance. Thermogravimetric and isothermal calorimetry analyses provided insights into hydration kinetics and phase composition changes. Although WFS incorporation reduced early-age mechanical efficiency, the cement intensity values of all mixtures became practically equivalent at 91 days. Notably, the mixture containing 35% WFS achieved clinker consumption per unit of strength nearly identical to the reference mortar, despite replacing more than one-third of the clinker content. The findings demonstrate the technical feasibility of incorporating WFS as a partial cement replacement material, highlighting its strong potential for clinker reduction, industrial waste valorization, and landfill diversion with limited long-term mechanical performance loss. Further studies are recommended to better distinguish possible physicochemical contributions from filler effects.
Fatigue cracks frequently occur in aeroengine combustion chambers under elevated temperature and pressure, while conventional laser welding faces challenges in repairing thin-walled components with controlled weld morphology. While using conventional laser welding technology to repair thin-walled parts, it is difficult to simultaneously achieve adequate weld width and sufficient penetration depth. In this study, beam-swing laser welding (BSLW) is combined with a hybrid back-propagation neural network and particle swarm optimization (BPNN–PSO) framework to optimize welding parameters according to the target weld morphology. A BPNN model was developed using 17 orthogonal experiments and evaluated through 5-fold cross-validation, achieving R2>0.94. The optimized parameters yielded predicted front and back weld widths of 2.196 and 1.004 mm, respectively, which agreed closely with experimental measurements. The optimized process was subsequently applied to HAYNES 230 repair welding and experimentally evaluated. The repaired specimens achieved an average tensile strength of around 761 MPa, approximately 90.9% of the reference tensile strength of HAYNES 230 at room temperature. Compared with the base metal, the reduced section shrinkage indicates the lower plasticity. The Vickers hardness of approximately 220 HV remained comparable to that of the base metal, while the radial deformation error of the repaired combustion chamber was only 0.18%. Metallographic analysis revealed continuous reticulated eutectic carbides in the weld region, which contributed to reduced joint strength and ductility. Overall, the proposed BSLW–BPNN–PSO framework provides an experimentally validated approach for welding parameter optimization, reducing cost, and repair performance evaluation of thin-walled HAYNES 230 aeroengine components.
This study investigates the Soret and Dufour phenomena in stagnation-point flow of a third-grade fluid past a stretching cylinder under thermal radiation, considering the influence of the Cattaneo-Christov heat flux. In this regard, the relevant governing partial differential equations (PDEs) are reduced to ordinary differential equations (ODEs) using similarity transformations and then numerically integrated with the bvp4c solver in MATLAB. Graphs and tables are depicted to examine the impact of certain parameters on the engineering quantities, velocity, temperature, and concentration profiles. Furthermore, Response Surface Methodology (RSM) is used to determine the effect of the Soret number, Dufour number, and variable thermal conductivity on the Nusselt number. Analysis of variance (ANOVA) provides a coefficient of determination of 98.21%, establishing the model's accuracy. In addition, artificial neural networks (ANNs) are used as a surrogate modeling approach for thermal and energy-related applications, demonstrating notable advantages in computational efficiency and predictive accuracy with MSE (mean square error) values on the order of 10-9-10-12 and a coefficient of determination (R2) close to 1. The ANN is trained using numerical data, and comparisons between actual and ANN-predicted results for the Dufour parameter and variable thermal conductivity against the temperature profile show excellent agreement. K-fold cross-validation and normality tests show the ANN's robustness, strong generalization, computational stability, reduced computational load, and statistical stability. The outcomes indicate that increasing the thermal relaxation time parameter enhances the Nusselt number while reducing the Sherwood number. Expanding the concentration relaxation time parameter reduces the Nusselt number while enhancing the Sherwood number. A higher variable viscosity parameter decreases the skin friction, whereas both the Nusselt number and the Sherwood number increase. This work has potential applications in cooling systems, chemical reactors, power plants, automotive applications, and material processing.
The demand for sustainable, high-performance construction materials has accelerated the development of technologies that improve concrete durability while reducing environmental impact. This study synthesized plant-mediated zinc oxide nanoparticles (ZnO NPs) from four plant species and evaluated their potential as sustainable nanobionic additives for enhancing the mechanical performance and microstructure of concrete. ZnO NPs were synthesized using leaf extracts of Artocarpus heterophyllus Lam., Lantana camara L., Azadirachta indica A. Juss., and Ficus benghalensis L., characterized by UV–Vis spectroscopy, FTIR, and GC–MS, and incorporated into concrete at 0.5% (w/w) of cement. UV–Vis confirmed nanoparticle formation with absorption peaks between 303.1 and 320.0 nm. FTIR identified functional groups involved in nanoparticle synthesis and stabilization, while GC–MS revealed bioactive compounds including fatty acids, terpenoids, alcohols, phytol, and squalene. The nanobionic concrete exhibited a slump value of 75 mm, indicating satisfactory workability. Mechanical testing demonstrated significant improvements over the control. The highest compressive strength was achieved with Ficus benghalensis-mediated ZnO NPs (13.95 N/mm²), followed by Lantana camara (12.90 N/mm²), Artocarpus heterophyllus (12.67 N/mm²), and Azadirachta indica (8.90 N/mm²). The highest split tensile strength was also recorded for Ficus benghalensis-mediated concrete (7.65 N/mm²). XRD, SEM, and EDX analyses confirmed enhanced crystallinity, improved hydration, effective ZnO incorporation, reduced porosity, fewer microcracks, and a denser cement matrix. These findings demonstrate that plant-mediated ZnO nanoparticles are promising nanobionic additives for producing sustainable concrete with enhanced mechanical properties and microstructural stability.
This study presents a Physics-Informed Temporal Attention Network (PITAN) for forecasting river temperature (T) and dissolved oxygen (DO) in the Padma River surrounding the Rooppur Nuclear Power Plant (RNPP). PITAN combines a hybrid CNN–BiLSTM backbone, multi-head scaled dot-product temporal self-attention, and physics-informed regularisation to capture nonlinear interactions among hydrological, atmospheric, and physicochemical variables while discouraging physically infeasible forecasts. Multi-source environmental records from the Bangladesh Water Development Board were quality-controlled and temporally harmonised, yielding 82 monthly observations from July 2016 to September 2023. One-step-ahead forecasts were generated using a seven-step historical window and evaluated using rolling-origin temporal validation with multiple random initialisations. PITAN achieved pooled RMSEs of 2.33 °C for temperature and 1.78 mg L⁻¹ for DO, compared with 2.88 °C and 2.02 mg L⁻¹, respectively, for persistence forecasts. Component-wise ablation showed that physics-informed output anchoring was the principal contributor to temperature forecasting skill, with its removal reducing R² from 0.484 to −0.036 (p < 0.001), whereas the contributions of the convolutional, recurrent, attention, and penalty components were not statistically distinguishable at the available sample size. Predictive uncertainty was characterised using Monte Carlo dropout and benchmarked against split-conformal intervals, while SHAP analysis highlighted the annual cycle, antecedent dissolved oxygen, and river discharge as the predictors most strongly associated with model output. Overall, the findings indicate that physically anchored deep learning can improve forecasting of coupled thermal and oxygen dynamics in a data-scarce monsoonal river while providing uncertainty estimates, interpretable predictors, and explicit evidence regarding which architectural components are supported by the available data.
Pitting corrosion-fatigue remains a dominant degradation mechanism in offshore and marine steel structures. Conventional approaches for modelling pitting corrosion-fatigue often fail to capture the stochastic, localised progression of pit-induced fatigue under complex environmental and loading conditions. This study focuses on recent advancements in data-driven methodologies, particularly machine learning (ML) and hybrid models. ML frameworks, ranging from neural networks to ensemble learning, show considerable promise in modelling non-linear, high-dimensional relationships among corrosion, fatigue, and environmental parameters. Hybrid models, which integrate domain knowledge through physics-based features or mechanistic coupling, further enhance predictive accuracy and robustness while offering improved interpretability. This study highlights both opportunities and persistent challenges in the field, including the scarcity of standardised, high-fidelity datasets; difficulties in generalising models across different steel grades and exposure conditions; and the limited interpretability of certain ML algorithms. This study emphasises the development of intelligent, adaptable models to support improved structural health monitoring and asset management in offshore and marine environments.
Accurate indoor location underpins a wide range of services, from asset tracking in hospitals and campuses to navigation in airports, shopping centers, and large office buildings. Although the Global Positioning System (GPS) is highly effective outdoors, satellite signals are severely attenuated by building materials and are often unavailable in enclosed spaces. Indoor Positioning Systems (IPS) address this limitation by enabling the localization and tracking of people, devices, and assets within buildings. Antenna design is a key determinant of positioning performance in indoor channels, where multipath, electromagnetic interference, and obstruction by metallic structures can bias angle- and time-based measurements. Although IPS technologies are widely studied, a dedicated review of antenna designs for indoor positioning systems remains underexplored.This review presents an overview of antenna designs for IPS, looking specifically at the unique positioning requirements of indoor systems where obstacles such as metal structure multipath effects will have an impact on antenna performance and position accuracy. We investigate key measurement techniques such as -Angle of Arrival (AoA), Time of Arrival (ToA), and Received Signal Strength (RSS)–and explain their significance for localization in the indoor environment. We also explore various antenna technologies such as Wi-Fi, Bluetooth, Ultra-Wideband (UWB), and millimeter-wave (mmWave). Furthermore, we discuss various antenna types and analyze their respective advantages and limitations for indoor positioning applications. Finally, we summarize emerging directions and open challenges toward robust, infrastructure-efficient indoor positioning.
Inferior vena cava (IVC) filters are prone to mechanical complications, including fracture, migration, tilt, and wall perforation; however, the underlying physiological mechanisms remain obscure. This paper employs computational fluid dynamics to investigate time-dependent hemodynamics of IVC filters under realistic clinical conditions. A patient-specific IVC geometry was subjected to physiologically pulsatile flow to evaluate flow-field dynamics, hemodynamic indices, and drag forces across varying clot burdens, heart rates, and device tilt angles. Results show that anatomical curvature inherently drives asymmetric, phase-dependent flow—an effect amplified by trapped clots and exercise-induced pulsatility. Filter tilting expands thrombogenic surfaces and spikes peak wall shear stress, raising the oscillatory shear index by up to 88%. While higher heart rates generally reduce extreme low-shear zones under mild occlusion, severe clot burdens generate new downstream stagnation regions characterized by elevated relative residence time, creating conditions favorable for secondary thrombosis. Force analysis reveals that clot burden governs force misalignment; large clots cause the resultant drag vector to deviate by approximately 60∘ from the filter axis, generating a substantial lateral moment that provides a mechanistic basis for progressive tilting and migration. Exercise heart rates further amplify pressure drop fluctuations, suggesting that patient activity level should be incorporated into post-implantation risk stratification. These findings demonstrate that the interplay between patient-specific geometry, pulsatile flow, and clot growth governs both local flow disturbances and mechanical stability, offering a pathway toward computational tools for individualized risk assessment and improved long-term management of IVC filters.
The demand for conductor materials that combine high electrical conductivity and sufficient mechanical strength is increasing in light of electrification. Combining highly conductive copper sheaths and mechanically stronger cores offers a promising approach for components where the skin effect redistributes the current-carrying region. Therefore, this study investigates the production of Cu-ETP/CuZn37 composite rods through direct and indirect hot extrusion. The influence of process setup on the material distribution in the extruded rods and the interface integrity is evaluated. Metallographic analyses, including the investigation of Cu/Zn concentration near the interface, are performed to reveal radial material distribution and interface characteristics. Finite element simulations are evaluated concerning the axial material distribution and verified through metallurgical and electrical resistance measurements. The billets produced by partial melting treatment exhibited a continuous interface with a narrow transition zone of approximately 40 µm. After hot extrusion, no visible voids or delamination were observed in any of the applied process setups. Microhardness measurements showed a continuous transition across the interface, providing complementary evidence of interface integrity. The results confirmed that direct extrusion led to pronounced axial variations in relative core diameter, while indirect extrusion produced a more homogeneous axial material distribution after the initial transient region. The simulation model is capable of capturing material flow and the qualitative differences between direct and indirect extrusion. Overall, the results highlight that hot extrusion is suitable for producing Cu-ETP/CuZn37 composite rods with a defined sheath-core design and sound interface integrity, while indirect extrusion is more favorable for achieving axial homogeneity.
Landslides are primarily triggered by earthquakes and extreme rainfall events, although anthropogenic factors such as vegetation change, deforestation, and unplanned land development can amplify their occurrence and severity. Landslides are catastrophic disasters that lead to fatalities, infrastructure damage, and economic disruption. Sri Lanka recently experienced the impact of Cyclone Ditwah in November 2025; the disaster resulted in 643 confirmed deaths, with 183 individuals missing. More than 6000 houses were completely destroyed, and nearly 114,000 sustained damage. However, it was observed that population density and land use and land cover (LULC) changes have some relationship to this damage. With the advancement of remote sensing technology, multispectral images were analyzed to assess how LULC affected natural disasters, alongside 2024 census data. The results of this study demonstrate that built-up and crop land areas have increased, while vegetation cover has decreased from 2017 to 2024. Furthermore, the findings indicate that landslide occurrence is associated with anthropogenic activities, such as urbanization and crop land development near the slope, which may contributed to reduced slope stability. The landslide-prone areas are located near hilly slopes with gradients greater than 30°. The Ududumbara–Minipe and Rideegama–Lunugala case studies demonstrate that landslides occurred more frequently on destabilized gentle slopes than on undisturbed steep slopes in these specific case-study areas, a pattern likely reflecting local anthropogenic disturbance rather than a general slope-stability principle. These findings highlight the importance of measuring and tailoring land-use policies related to urbanization, crop land, and settlement development in mountain regions to mitigate landslide risk.
Premature transverse cracking in a jointed plain concrete pavement located in a tropical environment was investigated using 18,844 vehicle-weighing records, compressive-strength tests on 20 extracted cores, Falling Weight Deflectometer measurements, surface thermography, and sequential three-dimensional finite element analyses. The objective was to quantify how prescribed through-thickness temperature differences modify tensile stress and PCA fatigue damage when repetitions from the measured traffic spectrum are distributed according to weighing time. Because only surface temperature was measured, the bottom-face temperature was inferred from Severi (2002). A value of 17.5°C was adopted as the upper bound of a deterministic series comprising 0, 4, 8, 12, 16, and 17.5°C, rather than as a simultaneously measured site gradient or an observed annual frequency. At 17.5°C, the maximum positive global S22 stresses were 1.589 MPa for ESRS, 2.046 MPa for ESRD, 2.097 MPa for ETD, and 2.266 MPa for ETT, corresponding to amplifications of approximately 60%, 54%, 10%, and 89% relative to ΔT = 0°C. None of the results reached the estimated tensile-strength range of 2.9–4.6 MPa. The conventional assessment of the 220 mm slab yielded 0.0133% fatigue damage and 2.543% erosion damage. In the exploratory hourly assessment, grouping the records by date, season, and hour and combining them with a representative thermal profile from Severi produced cumulative fatigue damage of 120.509%, comprising 53.480% for ESRS and 67.030% for ESRD. This result is not a deterministic fatigue-life prediction because no synchronized local history of traffic and through-thickness temperature was available. The study demonstrates how thermal action may amplify PCA fatigue demand without proposing a thermal thickness factor or attributing exclusive causality to curling.
Regenerative pumps are employed in applications requiring high pressure rises at low flow rates. However, their performance prediction is complicated by the complex, three-dimensional and highly recirculating internal flow. Over the past decades, numerous analytical models have been proposed to describe their operating principles, relying on different physical interpretations and levels of empirical adjustment.This review presents a critical assessment of closed-form analytical performance models developed for regenerative pumps, with a particular focus on their physical assumptions, governing equations, and predictive capabilities. The two main theoretical frameworks are examined in detail: the Turbulent-Exchange Theory (TET), which attributes pressure generation to turbulent shear-driven momentum transfer, and the Momentum-Exchange Theory (MET), which explicitly accounts for the recirculating flow between the impeller and the side channel. The historical development of these approaches is reviewed, from early formulations to more advanced models incorporating geometric effects, loss mechanisms, and internal leakages.Key model parameters, including slip and incidence coefficients, effective inlet and outlet radii of the recirculating flow, recirculation-loss coefficients, and leakage models, are analyzed and compared. Model predictions are systematically confronted with experimental results obtained from a well-documented reference pump, enabling a consistent evaluation of accuracy and limitations. The review demonstrates that MET provides a more physically representative description of the internal flow mechanisms in regenerative pumps than TET, while highlighting that the overall predictive accuracy remains strongly dependent on the empirical modeling of losses. Perspectives for the development of more robust and predictive analytical models are finally discussed.
Traditional prediction methods for predicting ship resistance, such as computational fluid dynamics (CFD), are computationally costly and inefficient, severely limiting ship form optimization efficiency.To address this bottleneck, this paper proposes a novel physics-informed dynamic regularization extreme gradient boosting (PIDR-XGBoost) model, which is further coupled with the Harris hawk optimization (HHO) algorithm to realize high-precision resistance prediction and efficient optimization. Firstly, high-reliability sample data were generated via CFD. Subsequently, a calm water resistance prediction model was constructed based on the PIDR-XGBoost method. This model innovatively embeds a dual-exponential dynamic regularization module, which adjusts the regularization strength in real time according to the local sample density and iterative fitting residuals, fundamentally solving the over-constraint/under-constraint dilemma. Meanwhile, a hydrodynamic mechanism-based physical deviation penalty term is introduced into the loss function, thereby addressing the deficiency of physical interpretability and the tendency to deviate from actual hydrodynamic laws inherent in conventional machine learning models. PIDR-XGBoost model reduces the root mean square error (RMSE) by 36.920% and the mean absolute error (MAE) by 58.473%, while increasing the determination coefficient (R²) by 2.793% compared with the traditional XGBoost model, providing high-precision and physically interpretable prediction support for subsequent ship form optimization. Finally, the CFD validation of the optimized ship form demonstrates that the total calm water resistance is reduced by 4.358%, with the error between the PIDR-XGBoost predicted value and the CFD validation value as low as 0.404%. The proposed integrated framework of PIDR-XGBoost and HHO breaks through the limitations of high computational cost, and can be directly applied to hull optimization and ship energy consumption assessment, exhibiting significant engineering application value.
Oils and soaps have traditionally been employed to enhance the performance of lime- and gypsum-based plasters, but the influence of these additives on the structure and physicochemical behavior of gypsum-based materials remains insufficiently understood. Here the effects of adding Ca-soap/oil (soap being prepared with 1or 5 wt% olive oil and limewater) to low or high T (calcined at either 180 or 330 °C) gypsum plasters are investigated with the goal of obtaining an optimized material with enhanced weathering resistance for outdoor finishing applications. Samples were subjected to various tests to evaluate the effect of the Ca-soap/oil on their hydration behavior, hydric properties, textural and structural features, and mechanical strength. Although the incorporation of Ca-soap/oil led to moderate reductions in mechanical strength (5 wt% oil reduced strength from 6.05 MPa to 4.69 and 4.27 MPa to 3.40 MPA in low and high T gypsum, respectively), the modified plasters exhibited improved plasticity, and lower surface hydrophilicity (5 wt% oil increased contact angles from 13.0 ± 1.6° to 71.1.0 ± 6.4° and 13.0 ± 3.0° to 91.8 ± 10.2° in low and high T gypsum, respectively). Calcination T influenced open porosity, being ∼40 and 49% for low and high T gypsum, respectively, additives primarily affecting the pore size distribution. 5 wt% oil addition reduced material loss upon water spraying by 36 and 15% in low and high T gypsum, respectively. Overall, the laboratory study provided encouraging results, but further long-term testing is required to fully assess the potential of Ca-soap/oil as additives for gypsum plaster, particularly in environments involving salt-laden substrates or freeze-thaw conditions.
The extracellular matrix, particularly collagen, undergoes dynamic remodeling during cutaneous wound healing, yet its spatiotemporal organization in living tissues remains poorly understood. Here we used multispectral photoacoustic imaging to non-invasively visualize collagen dynamics in a murine fibrotic wound healing model. By spectral unmixing of collagen-specific absorption signatures from the hemoglobin background, we resolved collagen content and distribution with a resolution of 7.8 μm and an imaging depth up to 2.4 mm. We showed that untreated wounds exhibit progressive collagen accumulation with disorganized architecture, whereas silicone gel-treated wounds display attenuated deposition and more aligned fiber organization, as validated by quantitative histology. These findings establish photoacoustic imaging as a powerful tool for dissecting extracellular matrix dynamics in vivo, with implications for understanding scar pathogenesis and monitoring therapeutic interventions.
