Search Results (482)

In the process of social development, there is a contradiction between economic development and the ecological environment. Western countries were the first to experience the inverted U-shaped development model of “destruction first and compensation later”, and China is also facing similar problems. To reveal the formation mechanism and dynamic evolution of the inflection point of ecological environment changes in China, this paper combines the entropy weight method, the analytic hierarchy process, and quadratic curve fitting to construct the “Ecological Pressure Index—GDPP Model” and studies the inflection point of ecological pressure during China’s economic development from 2000 to 2022. The study shows that the key inflection point of China’s ecological environment pressure is between 2016 and 2017, which is mainly affected by multiple factors such as the economy, domestic and international situations, and policy adjustments. For example, the implementation of the “Supply-side Structural Reform” and the environmental protection supervision system has significantly reduced the pollution pressure. At the same time, the “inflection point” is applied to dynamically adjust the PSR model, revealing the stage transition of China’s environmental governance focus. For instance, from 2000 to 2016, end-of-pipe pollution treatment was dominant (for example, the weights of pollution emission indicators X₅–X₈ were relatively high), while after 2016, the focus of governance shifted to the restoration of ecological space (for example, the weight of nature reserves X₂₂ was 2.759%). The theoretical contribution of this paper lies in proposing the concept of “Policy-driven EKC”, emphasizing the core role of policy intervention in the formation of the inflection point of the ecological environment. In addition, the dynamic adjustment of the PSR model using the “inflection point” better interprets China’s self-transformation in the development process and provides other developing countries with a Chinese solution of “institutional innovation first” and the “Policy-driven EKC—Chinese PSR Model” for reference in balancing economic growth and ecological protection. Full article

A lack of public vehicular traffic data for a city limits our understanding of the traffic occurring in the street networks of that city; however, there are free tools to extract street network graphs from digital maps and to assess the static properties associated with those graphs. This study proposes a two-stage modeling method to describe dynamic traffic data with static street network features. A quadratic polynomial is used to fit the average travel speed (ATS) pattern observed in the city center. Then, the relationship between the polynomial parameters and street network variables is analyzed through multiple linear regression. Descriptive geometric and topological measurements of downtown areas are obtained with the OSMnx tool (from OpenStreetMap), and with these data, independent variables are defined. The speed of vehicles, assessed every 15 min (from 6:00 a.m. to 10:00 p.m.) on the downtown street networks of twelve major cities, is obtained with the distance_matrix service of GoogleMaps, and with these data, the ATS (the dependent variable) is calculated. The ATS (presenting a U-shape) is modeled with a polynomial equation of order two, so there are three parameters for each city; in turn, each parameter is modeled with a multiple linear regression equation with the independent variables. For training purposes, the ATS equation parameters of ten cities are calculated, and the parameters, in turn, are explained with the proposed method. For validation purposes, the parameters of two cities not considered in the training process are calculated with the multiple linear regression equations. The ATS equation parameters of the twelve cities are correctly modeled so that each city’s ATS can be adequately described. It was concluded that the method selects the independent variables that are suitable to explain the ATS equation parameters. In addition, with the Akaike information criterion, the variable selection case presenting the best trade-off between accuracy and complexity is identified. Full article

The degree of tree curvature exerts a significant influence on the utilization of forestry resources. This study proposes an enhanced quantitative structural modeling (QSM) method, founded upon terrestrial laser scanning (TLS) point cloud data, for the precise extraction of 3D curvature characteristics of tree trunks. The conventional approach operates under the assumption that the tree trunk constitutes an upright rotating body, thereby disregarding the tree trunk’s true curvature morphology. The proposed method is founded on the classical QSM algorithm and introduces two zoom factors that can dynamically adjust the fitting parameters. This improvement leads to enhanced accuracy in the representation of tree trunk curvature and reduced computational complexity. The study utilized 146 sample trees from 13 plots in Jixi, Anhui Province, which were collected and pre-processed by TLS. The study combines point cloud segmentation, manual labeling of actual curvature and dual-factor experiments, and uses quadratic polynomials and simulated annealing algorithms to determine the optimal model factors. The validation results demonstrate that the enhanced method exhibits a greater degree of concordance between the predicted and actual curvature values within the validation set. In the regression equation, the coefficient of the two-factor method for fitting a straight line is 0.95, which is substantially higher than the 0.75 of the one-factor method. Furthermore, the two-factor model has an R² of 0.21, indicating that the two-factor optimization method generates a significantly smaller error compared to the one-factor model (with an R² of 0.12). In addition, this study discusses the possible reasons for the error in the results, as well as the shortcomings and outlook. The experimental results demonstrate the augmented method’s capacity to accurately reconstruct the 3D curvature of tree trunks in most cases. This study provides an efficient and accurate method for conducting fine-grained forest resource measurements and tree bending studies. Full article

Background/Objectives: Atrophy of the paraspinal and psoas major muscles is closely linked to sagittal malalignment in adult spinal deformity (ASD). However, most studies overlook the balance between these muscle groups. This study investigates the relationship between trunk muscle balance and sagittal alignment in ASD patients. Methods: A single-institution database was reviewed for patients with sagittal malalignment (PT > 20° and PI–LL > 10°). Standard sagittal parameters were measured based on standing X-rays. The cross-section area (CSA) of trunk posterior muscles (CSA^P: erector spinae and multifidus) and anterior muscles (CSA^A: psoas) at L4 were measured based on a T2-weighted MRI. Patients with prior lateral fusions were excluded. Muscle balance was evaluated by the CSA ratio of trunk posterior to anterior muscles (CSA^P/A). The relationship between sagittal alignment parameters and CSA^P, CSA^A, as well as CSA^P/A were analyzed using linear and quadratic regressions. Akaike information criteria (AIC) compared model fit. Subgroup analyses examined the relationship between sagittal alignment changes and different CSA^P/A levels. Results: A total of 112 patients met inclusion and exclusion criteria. CSA^P correlated linearly with SS (r² = 0.057, p = 0.011), PT (r² = 0.043, p = 0.028), and T4–L1PA mismatch (r² = 0.044, p = 0.027). CSA^A showed no significant linear or quadratic relationships with sagittal spinal alignment parameters. In contrast, CSA^P/A was quadratically associated with LL (r² = 0.056, p = 0.044), SS (r² = 0.134, p < 0.001), PI (r² = 0.096, p = 0.004), L1PA (r² = 0.114, p = 0.001), and T4–L1PA mismatch (r² = 0.094, p = 0.005). Quadratic models of CSA^P/A consistently had higher r² and lower AIC values compared to the linear models of CSA^P for most sagittal alignment parameters, especially in SS, PI, L1PA, and T4–L1PA mismatch (AIC difference ≥4). Higher CSA^P/A is correlated to larger PI (and consequently, larger LL, SS, and L1PA). Conclusions: Trunk posterior–anterior muscle balance (CSA^P/A) demonstrates a stronger relationship with sagittal alignment than individual muscle metrics. Quantitative MRI-based definitions of sarcopenia may need to be adjusted for PI. Full article

The response surface methodology (RSM) is utilized to construct a corrosion prediction model for steel pipelines for underground gas storage (UGS). Four key corrosion-influencing factors—the CO₂ partial pressure, Cl⁻ concentration, temperature, and flow rate—are identified by investigating the operating parameters of 14 UGS extracted pipelines (Nos. S1–S14) in China. Based on the operating parameters, 29 sets of high-temperature and high-pressure autoclave corrosion tests are designed and carried out. A quadratic regression equation model for corrosion rate prediction is fitted using the data from the corrosion test results. The p-values of the model’s four influencing factors are <0.01, indicating that the influencing factors are significant and reasonable. The F-value of the model is greater than the critical value, and the noise probability p-value is <0.01, indicating that the model has good fitness. The determination coefficient R² of the model is 0.9753, which is close to 1. Therefore, the observed value and the response value of the model are obviously correlated: i.e., the model has a high degree of truth. The model is used to predict the corrosion rate of 14 UGS pipelines: S3 and S14 are severely corroded, while the others are moderately corroded. Full article

To effectively utilize Chongqing’s solid waste red clay for scour protection of local cross-river bridge foundations, this study modified Chongqing red clay using curing agent and cement, focusing on the effects of curing agent dosage, cement content, and water-to-solid ratio on the flowability, anti-dispersion performance, and scour resistance of solidified soil. Microstructural characteristics were observed via SEM, with formula fitting performed for two key parameters. Results indicate that an increased curing agent dosage significantly reduces flowability and suspended solids content of solidified soil while negligibly affecting critical shear stress; elevated cement content markedly enhances critical shear stress, slightly improves short-term flowability with reverse effects over time, and minimally impacts anti-dispersion performance; reduced water-to-solid ratio mitigates free water-induced cohesion weakening, lowering suspended solids content and flowability while increasing critical shear stress. Microstructural analysis reveals that generated C–S–H gels and ettringite (AFt) effectively fill pores, enhance matrix integrity, and improve scour resistance. A suspended solids content–flowability relationship model (R² = 0.977) established through quadratic polynomial regression demonstrates excellent predictive performance. The optimal mix proportion (0.3% curing agent, 10% cement, 0.5 water-to-solid ratio) meets specifications and construction requirements, serving as the optimal solidified soil formulation for scour protection. Full article

This paper explores the application of a mathematical trend model to analyze product sales performance. A logistic trend model was utilized to analyze product sales performance, employing monthly sales data collected over three years. The model assessed impacts across various phases of the product life cycle. Significant sales trends were identified and modeled from historical data, demonstrating how sales dynamics mirror broader economic phenomena and consumer behaviors. In addition to logistic trends, linear and quadratic trends were also evaluated. To assess the significance of the sales trends for three products, the Mann–Kendall test was applied. The results indicate a statistically significant positive trend in the sales of product A. For evaluating the quality of data fit in model comparison, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were deemed appropriate. The analysis revealed that the logistic model effectively delineates different sales phases—from introduction to maturity—and highlights opportunities for optimizing strategic sales planning and customer satisfaction in alignment with market demands. The study’s findings are crucial for businesses seeking to enhance product lifecycle management and boost sales forecasting precision. Full article

The identification of gully-type debris flow remains a challenging task due to the irregularity of terrain, which causes significant fluctuations in local curvature and hinders accurate feature extraction using traditional methods. To address this issue, this study proposes a novel identification approach based on point cloud local curvature extrema. The methodology involves collecting image data of debris flow and landslide areas using DJI Matrice 300 RTK (M300RTK), planning control points and flight routes, and generating three-dimensional point cloud data through image matching and point cloud reconstruction techniques. A quadratic surface fitting method was employed to calculate the curvature of each point in the point cloud, while a topological k-neighborhood algorithm was introduced to establish spatial relationships and extract extreme curvature features. These features were subsequently used as inputs to a convolutional neural network (CNN) for landslide identification. Experimental results demonstrated that the CNN architecture used in this method achieved rapid convergence, with the loss value decreasing to 0.0032 (cross-entropy loss) during training, verifying the model’s effectiveness. The introduction of early stopping and learning rate decay strategies effectively prevented overfitting. Receiver-operating characteristic (ROC) curve analysis revealed that the proposed method achieved an area under the ROC curve (AUC) of 0.92, significantly outperforming comparative methods (0.78–0.85). Full article

Upcycling horticulture residues offers a sustainable solution to reduce environmental impact, maximize resource utilization, mitigate climate change, and contribute to the circular economy. We synthesized and characterized 14 natural deep eutectic solvents (NADESs) and applied them to upcycle horticulture residues, offering an innovative valorization approach. Using an initial many-factors-at-a-time (MFAT) screening followed by a rotatable central composite response surface methodology (RCCRSM) for optimization, quadratic models fitted the response data for all the synthesized NADESs given: TPC (R² = 0.984, p < 0.0001), TFC (R² = 0.9999, p < 0.0001), AA-CUPRAC (R² = 0.918, p < 0.0001), FRAP (R² = 1.000, p < 0001), and DPPH (R² = 0.9992, p < 0.0001). An ultrasound temperature of 45 °C, extraction time of 5 min, solvent volume of 25 mL, and solvent concentration of 90% (v/v) were considered the optimal conditions for achieving maximum desirability (0.9936) for TPC yield. For TFC and CUPRAC, the optimal conditions were 30 °C, 5 min, 25 mL, and 90% (v/v), with maximum desirability values of 0.9003 and 1.00, respectively. The maximum desirability for FRAP (0.9605) was achieved under conditions of 45 °C, 25 min, 25 mL, and 50%, while DPPH had a maximum desirability of 0.9313, with 50 °C, 15 min, 15 mL, and 70% (v/v) as the optimized conditions. Full article

This research aims to estimate long-term aerosol radiative effects by combining radiation and Aerosol Optical Depth (AOD) observations in Barcelona, Spain. Aerosol Radiative Forcing and Aerosol Forcing Efficiency (ARF and AFE) were estimated by combining shortwave radiation measurements from a SolRad-Net CM-21 pyranometer (level 1.5) and AERONET AOD (level 2), using the direct method. The shortwave AFE was derived from the slope between net solar radiation and AOD at 440, 675, 879, and 1020 nm, and the ARF was computed by multiplying the AFE by AOD at six solar zenith angles (20°, 30°, 40°, 50°, 60°, and 70°). Clear-sky conditions were selected from all-skies days by a quadratic fitting. The aerosol was classified to investigate the forcing contributions from each aerosol type. The aerosol classification was based on Pace and Toledano’s thresholds from AOD vs. Ångström Exponent (AE). The GRASP inversions were performed by combined AOD, radiation, Degree of Linear Polarization (DoLP) by zenith angles from the polarized sun–sky–lunar photometer and the elastic signal from the UPC-ACTRIS lidar system. The long-term AFE and ARF are both negative, with an increasing tendency (in absolute value) of +24% (AFE) and +40% (ARF) in 14 years. The yearly AFE varied from −331 to −10 Wm⁻²τ⁻¹, and the ARF varied from −64 to −2 Wm⁻², associated with an AOD (440 nm) from 0.016 to 0.690. The three types of aerosols on clear-sky days are mixed aerosols (61%), desert dust (10%), and urban/industrial-biomass burning aerosols (29%). Combined with Gobbi’s method, this classification clustered the aerosols into four groups by AE analysis (two coarse- and two fine-mode aerosols). Then, the contribution of the aerosol types to the ARF showed that the desert dust forcing had the largest cooling effect in Barcelona (−61.5 to −37.4 Wm⁻²), followed by urban/industrial-biomass burning aerosols (−40.4 to −20.4 Wm⁻²) and mixed aerosols (−31.8 and −24.0 Wm⁻²). Regarding the comparison among Generalized Retrieval of Atmosphere and Surface Properties (GRASP) inversions, AERONET inversions, and direct method estimations, the AFE and ARF had some differences owing to their definitions in the algorithms. The DoLP, used as GRASP input, decreased the ARF overestimation for high AOD. Full article

Alternative models for the respiration rate (RR) of ready-to-eat strawberries were evaluated as a function of O₂ and CO₂ concentration and temperature. The effect of the gaseous atmosphere and temperature on RR was determined in a total factorial experiment where 45 treatments were applied by combining factors: oxygen (0–21%) and carbon dioxide (0–15%) concentration at three levels and temperature (4–26 °C) at five levels. Both phenomenological (Michaelis–Menten, Langmuir) and non-phenomenological (Generalized linear and Quadratic) approaches were used to fit RR data. The temperature effect was modeled by Arrhenius, exponential, and power models. Model selection was performed based on R2-adjusted, RMSE, and IAC indicators. Models with R² greater than 0.80, lower RMSE, and AIC were selected. The quadratic model and Michaelis–Menten Uncompetitive-with power model for temperature dependence were the best predictors of the experimental data. An integrated mathematical model based on strawberry respiration activity considering the influence of oxygen, carbon dioxide, and temperature was obtained, allowing its use for MAP modeling. Full article

Background/Objectives: Clinical studies have shown a marked reduction in tumor control in prostate cancer treated with radically hypofractionated high-dose-rate brachytherapy (HDR-BT). The purpose of this study was to analyze the dose–response of prostate cancer treated with HDR-BT, specifically aiming at investigating the potential failure of the linear–quadratic (LQ) model to describe the response at large doses-per-fraction. Methods: We collated a dataset of dose–response to HDR-BT (3239 patients). The analysis was conducted separately for low and intermediate risk, resulting in 21 schedules (1633 patients) and 23 schedules (1606 patients), respectively. Data were fitted to tumor control probability models based on the LQ model, the linear–quadratic–linear (LQL), and a modification of the LQ model to include the effect of reoxygenation during treatment. Results: The LQ cannot fit the data unless the $\alpha /\beta$ is allowed to be high (∼[20, >100] Gy, 95% confidence interval). If the $\alpha /\beta$ is constrained to be low (≤8 Gy), the LQ model cannot reproduce the clinical results, and the LQL model, which includes a moderation of radiation damage with increasing dose, significantly improves the fitting. On the other hand, the reoxygenation model does not match the results obtained with the LQL. The clinically observed reduction in tumor control in prostate cancer treated with radical HDR-BT is better described by the LQL model. Using the best-fitting parameters, the BED for a 20 Gy × 1 treatment (128 ${\mathrm{Gy}}_{\alpha /\beta }$) is far less than that of a conventional 2 Gy × 37 fractionation (196 ${\mathrm{Gy}}_{\alpha /\beta }$). Conclusions: Our analysis showed that the substantial loss of tumor control observed in extremely hypofractionated HDR-BT trials can only be explained by the LQ model if the $\alpha /\beta$ is very large (≥100 Gy), in clear disagreement with the limits set in the analysis of external radiotherapy data. It seems more reasonable that there is a moderation of the LQ-predicted effect with increasing dose per fraction. These results may assist in the design of radical HDR-BT treatments. Full article

Maritime transportation is responsible for most global trade and is generally considered more environmentally efficient compared to other modes of transport, particularly for long-distance trade. With increasingly stringent emission regulations, however, accurately quantifying emissions and identifying their key determinants has become essential for effective environmental management. This study introduced a structured and comparative statistical modeling framework for ship-based emission modeling using gross tonnage (GT) as the primary predictor variable, due to its strong correlation with emission levels. Emissions for hydrocarbon (HC), carbon monoxide (CO), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and volatile organic compounds (VOC) were estimated using a bottom-up approach based on emission factors and formulas defined by the U.S. Environmental Protection Agency (EPA), using data from 38,304 vessel movements through the Bosphorus in 2021. These EPA-estimated values served as dependent variables in the modeling process. The modeling framework followed a three-step strategy: (1) outlier detection using Rosner’s test to reduce the influence of outliers on model accuracy, (2) curve fitting with 12 regression models representing four curve types—polynomial (e.g., linear, quadratic), concave/convex (e.g., exponential, logarithmic), sigmoidal (e.g., logistic, Gompertz, Weibull), and spline-based (e.g., cubic spline, natural spline)—to capture diverse functional relationships between GT and emissions, and (3) model comparison using difference performance metrics to ensure a comprehensive assessment of predictive accuracy, consistency, and bias. The findings revealed that nonlinear models outperformed polynomial models, with spline-based models—particularly natural spline and cubic spline—providing superior accuracy for HC, PM₁₀, SO₂, and VOC, and the Weibull model showing strong predictive performance for CO and NO_x. These results underscore the necessity of using pollutant-specific and flexible modeling strategies to capture the intricacies of maritime emission dynamics. By demonstrating the advantages of flexible functional forms over standard regression techniques, this study highlights the need for tailored modeling strategies to better capture the complex relationships in maritime emission data and offers a scalable and transferable framework that can be extended to other vessel types, emission datasets, or maritime regions. Full article

Despite ongoing research efforts aimed at understanding the structural response of steel fiber reinforced concrete (SFRC), there is very limited research on the failure characteristics and theoretical compression-bending capacity of unreinforced steel fiber reinforced concrete (SFRC without rebars, USFRC). In this study, the cube compression tests, notched beam tests, and full-scale segment compression-bending tests are carried out to investigate the flexural performance of USFRC. The crack width–bending moment curves, load–deflection curves, and ultimate load of USFRC segments are obtained. Additionally, the theoretical compression-bending capacity of USFRC segments according to Model Code 2010 is investigated and the calculation methods applicable to different fiber contents, segment sizes, and mix proportions are obtained, which can provide a basis for predicting the performance of USFRC segments in related engineering applications, and some conclusions can be drawn. The results show that steel fibers can slightly improve the compressive strength of concrete, and the improvement capacity varies with different mix proportions and fiber contents. The addition of steel fibers can also improve the compressive failure mode of concrete. The relationships among the crack width, bending moment, and eccentricity can be expressed by a multivariate linear regression equation, and the relationship between the bending moment and deflection can be fitted by a quadratic equation. Both fitting effects are good. Based on the Model Code 2010 calculation model, a calculation method for the compression-bending capacity of USFRC is proposed, and the calculation method of residual tensile strength of steel fiber is modified. The new method can predict the compression-bending capacity of USFRC more accurately. Full article

Mangrove ecosystems rank among the most productive on Earth. Conducting research on the biomass prediction model of mangroves, as well as achieving simple and efficient estimations of the biomass of mangrove plant organs and the overall biomass, is of utmost significance for evaluating the productivity of the mangrove ecosystem and offering guidance for the future planning, restoration, and management of mangroves. This study examines the biomass distribution characteristics of Kandelia obovata at the northern edge of its range in China and develops models for estimating the biomass of its various components and individual trees. The findings provide valuable references for accurately assessing the biomass of Kandelia obovata plantations in Zhejiang Province. We measured the biomass of different components (branches, leaves, roots) using the harvest method and employed independent variables, including basal diameter (D), tree height (H), diameter squared (D²), the product of diameter squared and height (D²H), and the product of basal diameter and height (DH). Dependent variables included the leaf, branch, root, and total biomass. We developed linear, quadratic, and power function regression equations, selecting the optimal models based on the coefficient of determination (R²), significance of regression, root mean square error (RMSE), and Akaike Information Criterion (AIC). The total biomass ranged from 0.100 to 0.925 Mg ha⁻¹, while the carbon stocks ranged from 0.038 to 0.377 Mg C ha⁻¹. Results indicated that branch biomass accounted for the highest proportion (47.44%~68.35%), while leaf biomass (8.61%~27.83%) and root biomass (23.04%~25.64%) were relatively lower. Similarly, branch carbon storage constituted the highest proportion (52.68%~77.79%), with leaf (8.70%~29.36%) and root carbon storage (13.51%~20.55%) being lower. The optimal model exhibited R² values ranging from 0.594 to 0.921 and significant F-tests (p < 0.001). Single variables D, D², and combined variables D²H and DH provided the best fits. Basal diameter (D) and tree height (H) effectively predict the biomass of Kandelia obovata across different ages, with combined variables DH and D²H enhancing model accuracy. The biomass estimation model for total biomass is: W_Total = 0.0584(DH)^1.3918 (R² = 0.908, F = 2459.87, RMSE = 0.448). This model serves as a reliable tool for estimating the biomass of Kandelia obovata mangroves at the northern edge of its distribution in China. Full article