Search Results (622)

This present study proposes different machine learning-based predictors for the assessment of the residual compressive strength of Self-Compacting Concrete (SCC) subjected to high temperatures. The investigation is based on several literature algorithmic approaches based on Artificial Neural Networks with distinct training algorithms (Bayesian Regularization, Levenberg–Marquardt, Scaled Conjugate Gradient, and Resilient Backpropagation), Support Vector Regression, and Random Forest methods. A training database of 150 experimental data points is derived from a careful literature review, incorporating temperature (20–800 °C), geometric ratio (height/diameter), and corresponding compressive strength values. A statistical analysis revealed complex non-linear relationships between variables, with strong negative correlation between temperature and strength and heteroscedastic data distribution, justifying the selection of advanced machine learning techniques. Feature engineering improved model performance through the incorporation of quadratic terms, interaction variables, and cyclic transformations. The Resilient Backpropagation algorithm demonstrated superior performance with the lowest prediction errors, followed by Bayesian Regularization. Support Vector Regression achieved competitive accuracy despite its simpler architecture. Experimental validation using specimens tested up to 800 °C showed a good reliability of the developed systems, with prediction errors ranging from 0.33% to 23.35% across different temperature ranges. Full article

Prefabricated construction elements are widely used in both large- and small-scale projects, serving structural and infrastructural purposes. One notable application is in power transmission poles, which ensure the safe and efficient delivery of electricity. Despite their importance, limited research exists on the structural and modal behavior of reinforced concrete power poles. This study presents a comprehensive modal analysis of such poles, focusing on how factors like modulus of elasticity, height, and lower/upper inner and outer diameters influence dynamic performance. A total of 3240 finite element models were created, with reinforced concrete poles partially embedded in the ground. Modal analyses were performed to evaluate natural frequencies, mode shapes, and modal mass participation ratios. Results showed that increasing the modulus of elasticity raised frequency values, while greater pole height decreased them. Enlarging the lower inner and upper outer radii also led to higher frequencies. Regression analysis yielded high accuracy, with R² values exceeding 90% and an average error rate of about 6%. The study provides empirical formulas that allow for quick frequency estimations without the need for detailed finite element modeling, as long as the material and geometric properties remain consistent. The approach can be extended to other prefabricated structural elements. Full article

Aerosol uniformity in the mixing chamber is one of the key factors in evaluating performance of aerosol samplers and accuracy of aerosol monitors which could output the direct reading of particle size or concentration. For obtaining high uniformity and a stable test aerosol sample during evaluation, a portable mixing chamber, where the sample and clean air were dual-vortex turbulent mixed, was designed. By using computational fluid dynamics (CFD), particle motion within the mixing chamber was illustrated or explained. By adjusting critical structure parameters of chamber such as height and diameter, the flow field structure was optimized to improve particle mixing characteristics. Accordingly, a novel portable aerosol mixing chamber with length and inner diameter of 0.7 m and 60 mm was developed. Through a combination of simulations and experiments, the operating conditions, including working flow rate, ratio of carrier/dilution clean air, and mixture duration, were studied. Finally, by using the optimized parameters, a mixing chamber with high spatial uniformity where variation is less than 4% was obtained for aerosol particles ranging from 0.3 μm to 10 μm. Based on this chamber, a standardized testing platform was established to verify the sampling efficiency of aerosol samplers with high flow rate (28.3 L·min⁻¹). The obtained results were consistent with the reference values in the sampler’s manual, confirming the reliability of the evaluation system. The testing platform developed in this study can provide test aerosol particles ranging from sub-micrometers to micrometers and has significant engineering applications, such as atmospheric pollution monitoring and occupational health assessment. Full article

Ectomycorrhizal symbiosis is a cornerstone of ecosystem health, facilitating nutrient uptake, stress tolerance, and biodiversity maintenance in trees. Optimizing Pinus armandii–Tuber indicum mycorrhizal synthesis enhances the ecological stability of coniferous forests while supporting high-value truffle cultivation. This study conducted a pot experiment to compare the effects of three root nutrient regulations—Aolu 318S (containing N-P₂O₅-K₂O in a ratio of 15-9-11 (w/w%)), Aolu 328S (11-11-18), and Youguduo (19-19-19)—on the mycorrhizal synthesis of P. armandii–T. indicum. The results showed that root nutrient supplementation significantly improved the seedling crown, plant height, ground diameter, biomass dry weight, and mycorrhizal infection rate of both the control and mycorrhizal seedlings, with the slow-release fertilizers Aolu 318S and 328S outperforming the quick-release fertilizer Youguduo. The suitable substrate composition in this experiment was as follows: pH 6.53–6.86, organic matter content 43.25–43.49 g/kg, alkali-hydrolyzable nitrogen 89.25–90.3 mg/kg, available phosphorus 83.69–87.32 mg/kg, available potassium 361.5–364.65 mg/kg, exchangeable magnesium 1.17–1.57 mg/kg, and available iron 33.06–37.3 mg/kg. It is recommended to mix the Aolu 318S and 328S solid fertilizers evenly into the substrate, with a recommended dosage of 2 g per plant. These results shed light on the pivotal role of a precise N-P-K ratio regulation in fostering sustainable ectomycorrhizal symbiosis, offering a novel paradigm for integrating nutrient management with mycorrhizal biotechnology to enhance forest restoration efficiency in arid ecosystems. Full article

Soil salinization, affecting approximately 954 million hectares globally, severely impairs plant growth and agricultural productivity. Apocynum venetum L., a perennial herbaceous plant with ecological and economic value, demonstrates remarkable tolerance to saline and alkali soils. This study investigated the effects of saline (NaCl) and alkali (Na₂CO₃ and NaHCO₃) stress on the growth, anatomical adaptations, and metabolite accumulation of A. venetum (Apocynum venetum L.). Results showed that alkali stress (100 mM Na₂CO₃ and 50 mM NaHCO₃) inhibited growth more than saline stress (NaCl 240 mM), reducing plant height by 29.36%. Anatomical adaptations included a 40.32% increase in the root cortex-to-diameter ratio (100 mM Na₂CO₃ and 50 mM NaHCO₃), a 101.52% enlargement of xylem vessel diameter (NaCl 240 mM), and a 68.69% thickening of phloem fiber walls in the stem (NaCl 240 mM), enhancing water absorption, salt exclusion, and structural support. Additionally, leaf palisade tissue densification (44.68% increase at NaCl 160 mM), along with epidermal and wax layer adjustments, balanced photosynthesis and water efficiency. Metabolic responses varied with stress conditions. Root soluble sugar content increased 49.28% at NaCl 160 mM. Flavonoid accumulation in roots increased 53.58% at Na₂CO₃ 100 mM and NaHCO₃ 50 mM, enhancing antioxidant defense. However, chlorophyll content and photosynthetic efficiency declined with increasing stress intensity. This study emphasizes the coordinated adaptations of A. venetum, providing valuable insights for the development of salt-tolerant crops. Full article

Extrusion-based 3D bioprinting is prevalent in tissue engineering, but enhancing precision is critical as demands for functionality and accuracy escalate. Process parameters (nozzle diameter d, layer height h, printing speed v₁, extrusion speed v₂) significantly influence hydrogel deposition and structure formation. This study optimizes these parameters using an orthogonal experimental design and grey relational analysis. Hydrogel filament formability and the die swell ratio served as optimization objectives. A response mathematical model linking parameters to grey relational grade was established via support vector regression (SVR). Particle Swarm Optimization (PSO) then determined the optimal parameter combination: d = 0.6 mm, h = 0.3 mm, v₁ = 8 mm/s, and v₂ = 8 mm/s. Comparative experiments showed the optimized parameters predicted by the model with a mean error of 5.15% for printing precision, which outperformed random sets. This data-driven approach reduces uncertainties inherent in conventional simulation methods, enhancing predictive accuracy. The methodology establishes a novel framework for optimizing precision in extrusion-based 3D bioprinting. Full article

To address the persistent challenges of substantial land occupation, intricate construction sequencing, and extended project timelines inherent to conventional substation accident oil sumps, this research introduces a novel integrally prefabricated circular cross-section oil containment structure. The study establishes a finite element representation of this prefabricated system to systematically examine structural deformation mechanisms and failure patterns under combined hydrostatic and geostatic loading scenarios. Through parametric analysis of the oil tank structure, the influences of longitudinal reinforcement diameter, thickness–diameter ratio, height–diameter ratio, and concrete-strength grade on the mechanical characteristics of the structure are explored. Utilizing the response surface methodology for the parametric optimization in finite element analysis, a comprehensive optimization of critical geometric design variables is conducted. These results indicate that longitudinal reinforcement diameter and concrete-strength grade exert negligible influence on concrete stress except for stress increase under internal pressure, with higher concrete grades. The thickness-to-diameter ratio dominantly regulates structural responses: response surface optimization achieved 12% stress reduction and 14% displacement mitigation at 220 mm wall thickness under internal pressure, despite a 4% stress increase under external loading. Height-dependent effects require specific optimization, with 18% stress reduction beyond 3000 mm under external pressure but 20% stress increase at 3400 mm under top loads. Geometric refinements enable 34–50% displacement reduction in critical zones, providing validated references for prefabricated oil tanks. Full article

The tensile strength of loess, a key mechanical parameter for crack-related failures, has not received much attention in previous research, with the literature demonstrating a lack of systematic studies. Therefore, in this study, the variations in the tensile strength, crack distribution, crack number, and internal stress of an undisturbed loess were studied in detail by combining the unconfined penetration test (UPT) and a discrete element method (DEM)-based simulation. The tensile strengths of undisturbed loess samples with different height–diameter ratios (1, 1.5, and 2) were investigated by using the UPT with loading plates of different diameters (12.86 mm, 15.56 mm, and 19.02 mm). DEM simulation was then conducted based on the experimental results. The results showed that (1) the tensile strength of undisturbed loess decreased with increased height–diameter ratio, while it increased with an increase in the diameters of the loading plates; (2) the DEM simulation allowed us to study the tensile characteristics of the undisturbed loess, and the simulated tensile strengths obtained via DEM simulation agreed with those determined via the UPT; (3) the distribution of internal stress and crack number in the DEM model were significantly influenced by the height–diameter ratio and loading plate diameter; (4) the number of cracks in the DEM model increased with an increase in the diameter of the loading plate, while the number of cracks first increased and then decreased with an increase in the height–diameter ratio. This study helps us to understand the variation in the tensile strengths of undisturbed loess samples from both macroscopic and microscopic perspectives. It is expected to serve as a reference for design, construction, and maintenance in engineering projects hinging upon the Loess Plateau region in China. Full article

To address the technical problems of broad droplet size spectrum, insufficient atomization uniformity, and spray drift in plant protection unmanned aerial vehicle (UAV) applications, this study developed a novel two-stage aerial electrostatic spraying device based on the coupled mechanisms of hydraulic atomization and electrostatic induction, and, through the integration of three-dimensional numerical simulation and additive manufacturing technology, a new two-stage inductive charging device was designed on the basis of the traditional hydrodynamic nozzle structure, and a synergistic optimization study of the charging effect and atomization characteristics was carried out systematically. With the help of a charge ratio detection system and Malvern laser particle sizer, spray pressure (0.25–0.35 MPa), charging voltage (0–16 kV), and spray height (100–1000 mm) were selected as the key parameters, and the interaction mechanism of each parameter on the droplet charge ratio (C/m) and the particle size distribution (Dv50) was analyzed through the Box–Behnken response surface experimental design. The experimental data showed that when the charge voltage was increased to 12 kV, the droplet charge-to-mass ratio reached a peak value of 1.62 mC/kg (p < 0.01), which was 83.6% higher than that of the base condition; the concentration of the particle size distribution of the charged droplets was significantly improved; charged droplets exhibited a 23.6% reduction in Dv50 (p < 0.05) within the 0–200 mm core atomization zone below the nozzle, with the coefficient of variation of volume median diameter decreasing from 28.4% to 16.7%. This study confirms that the two-stage induction structure can effectively break through the charge saturation threshold of traditional electrostatic spraying, which provides a theoretical basis and technical support for the optimal design of electrostatic spraying systems for plant protection UAVs. This technology holds broad application prospects in agricultural settings such as orchards and farmlands. It can significantly enhance the targeted deposition efficiency of pesticides, reducing drift losses and chemical usage, thereby enabling agricultural enterprises to achieve practical economic benefits, including reduced operational costs, improved pest control efficacy, and minimized environmental pollution, while generating environmental benefits. Full article

Defects arising from the 3D printing process of continuous fiber-reinforced thermoplastic composites primarily hinder their overall performance. These defects particularly include twisting, folding, and breakage of the fiber bundle, which are induced by printing trajectory errors. This study presents a follow-up theory assumption to address such issues, elucidates the formation mechanism of printing trajectory errors, and examines the impact of key geometric parameters—trace curvature, nozzle diameter, and fiber bundle diameter—on these errors. An error model for printing trajectory is established, accompanied by the proposal of a trajectory error compensation method premised on maximum printable curvature. The presented case study uses CCFRF/PA as an exemplar; here, the printing layer height is 0.1~0.3 mm, the fiber bundle radius is 0.2 mm, and the printing speed is 600 mm/min. The maximum printing curvature, gauged by the printing trajectory of a clothoid, is found to be 0.416 mm⁻¹. Experimental results demonstrate that the error model provides accurate predictions of the printed trajectory error, particularly when the printed trajectory forms an obtuse angle. The average prediction deviations for line profile, deviation kurtosis, and deviation area ratio are 36.029%, 47.238%, and 2.045%, respectively. The error compensation effectively mitigates the defects of fiber bundle folding and twisting, while maintaining the printing trajectory error within minimal range. These results indicate that the proposed method substantially enhances the internal defects of 3D printed components and may potentially be applied to other continuous fiber printing types. Full article

In order to explore the axial compression performance of external steel rib ring restraint waste-steel-fiber-reinforced concrete-filled steel tubes (ERWCFSTs), 18 short-column axial compression tests were conducted. The effects of the number of rib rings, rib ring spacing, rib ring setting position, and waste steel fiber (WSF) content on the axial compression performance of the columns were analyzed. The results show that the concrete-filled steel tube (CFST) short columns with rib rings were strengthened, the specimens were mainly characterized by drum-shaped failure, and the buckling was concentrated between the rib rings. Without rib ring specimens, the steel tube is unable to resist the rapid increase in lateral expansion, leading to buckling initiation near the bottom of the specimens. The columns with rib rings exhibited a minimum increase of 32.5% and a maximum increase of 53.17% in load-bearing capacity compared to those without rib rings, with an average improvement of 37.78%. The columns achieved the best ductility when the rib ring spacing was 50 mm. When the rib ring spacing remained constant, columns with a number of rib rings no less than the height-to-diameter ratio (H/D) demonstrated more uniform stress distribution and optimal confinement effects. For a fixed number of rib rings, specimens with rib ring spacing between H/8 and H/4 showed significant improvements in both load-bearing capacity and ductility. The confinement effect was better when the rib rings were positioned in the middle of the column height rather than near the ends. The incorporation of WSF resulted in a minimum increase of 2.86% and a maximum increase of 10.49% in column load-bearing capacity, indicating limited enhancement. However, WSF improved the ductility performance of the columns by at least 10%. Combined with theoretical analysis and experimental data, a formula for calculating the bearing capacity of ERWCFSTs was established. Full article

High-amylose wheat (HAW), developed through non-genetic modification, addresses the growing demand for clean-label and nutritionally enhanced food products. This study systematically investigated the effects of heat-moisture treatment (HMT; 20% and 25% moisture levels) on the physicochemical properties and cookie-making performance of HAW flour (HAWF) and soft wheat flour (SWF). HMT promoted moisture-induced agglomeration, leading to increased particle size, reduced damaged starch content, and enhanced water and sucrose solvent retention capacities. Although the amylose content remained largely unchanged, pasting behavior was differentially affected, with increased viscosities in SWF and slight decreases in HAWF. Thermal analyses demonstrated elevated gelatinization temperatures, indicating improved thermal stability, while X-ray diffraction revealed alterations in starch crystallinity. Furthermore, HMT weakened gluten strength and modified dough rheology, effects more pronounced in HAWF. Cookies prepared from HMT-treated flours exhibited larger diameters, greater spread ratios, and reduced heights. In vitro digestibility assays showed a marked reduction in rapidly digestible starch and increases in slowly digestible and resistant starch fractions, particularly in HAWF cookies. Collectively, these findings establish HMT as an effective strategy for modulating flour functionality and enhancing cookie quality, while concurrently improving the nutritional profile through the alteration of starch digestibility characteristics. Full article

The Miombo woodlands are declining in both area and value, primarily due to over-harvesting of commonly preferred species. These forests, however, still contain several other species that are potentially of commercial importance. This study aimed to address the need for improved volume and biomass estimates for the sustainable management and utilization of two of the most abundant timber species in Mozambique’s Miombo woodlands: Brachystegia spiciformis (common name: Messassa) and Julbernardia globiflora (common name: red Messassa). Non-linear models were developed to estimate the merchantable wood volume under bark, heartwood volume, and biomass. The volume and biomass models for wood and heartwood volume, which included both diameter at breast height (DBH) and tree height as predictor variables, outperformed single-predictor models. However, the performance of some ratio models using DBH as the only predictor variable surpassed that of models using two predictor variables. The developed models are recommended for adoption by forest companies to increase economic and environmental benefits as they can refine harvest planning by improving the selection of trees for harvesting. Proper tree selection enhances the rate of recovery of high-quality timber from heartwood while observing sustainable forest management practices in Miombo and increasing the proportion of carbon removed from forests, which is subsequently stored in wood products outside the forest. Full article

The relationship between plants and their environment has always been a core issue in ecological research. This study about how plant architecture and leaf traits respond to environmental changes helps to more deeply understand the adaptive mechanisms of plants in diverse environments. Although there have been related studies, a systematic analysis on a China-wide scale is still lacking. To address this gap, we conducted a meta-analysis of 115 studies across China examining plant architectural and leaf trait responses to environmental changes. The dataset includes 849 observations across 11 ecological variables, such as the mean annual precipitation, mean annual temperature, soil type, and elevation, and evaluates their effects on seven key plant traits. The results indicated that variations in the plant height, diameter at breast height (DBH), and root-to-shoot ratio are primarily influenced by the soil type and mean annual precipitation. In contrast, the soil type and mean annual sunshine duration mainly affected the specific leaf area (SLA), leaf area, leaf thickness, and leaf dry matter content. Moreover, while the magnitude of trait responses varies across precipitation, temperature, elevation, and soil property gradients, the impacts of environmental change are particularly pronounced under more extreme conditions. This study provides robust scientific evidence for understanding the effects of environmental change on plant growth across China and offers valuable insights into ecological conservation and the sustainable use of plant resources. Full article

Accurate prediction of individual tree mortality in Pinus yunnanensis Franch. is essential for sustainable forest management and ecological monitoring in southwest China. The aim of this study is to develop a tree mortality prediction model for Pinus yunnanensis based on resurvey data from the Cangshan area in Dali, Yunnan Province, using a stacked ensemble learning algorithm. After an initial evaluation of model performance, the classification thresholds were optimized using the Minimum Classification Error method, the Maximum Sensitivity and Specificity method, the Kappa coefficient method, and the Precision-Recall (PR) curve method to enhance classification results. The findings show that, compared to traditional statistical methods and individual machine learning models, the stacked ensemble learning model (Stacked-RSX) outperforms others in tree mortality classification tasks, which achieved an accuracy of 0.8947, recall of 0.9431, true negative rate of 0.9490, misclassification rate of 0.2289, and an area under the curve of 0.953. Through an exhaustive search for the best classification thresholds, the PR curve method demonstrated good adaptability across all models. All optimal thresholds, relative to the default threshold, significantly improved overall classification performance. Furthermore, feature importance analysis revealed that tree height, diameter at breast height (DBH), Hegyi competition index, and the ratio of DBH to stand basal area are key variables influencing mortality risk. These results indicate that the stacking ensemble learning algorithm effectively analyzes the complex relationships among different factors, significantly improving the prediction accuracy of tree mortality, and providing scientific insights for the management and health monitoring of Pinus yunnanensis forests. Full article