Search Results (941)

Background: Extreme drought and flood events continuously threaten the stability of forest and crop production. Long-term hydrological records derived from tree-ring anatomical proxies provide critical evidence for revealing historical drought hazard differentiation. Methods: Cell wall thickness chronologies of Betula platyphylla (northern forest) and Picea koraiensis (southern agro-pastoral zone) were developed to reconstruct nearly 300-year annual runoff sequences. Pearson correlation, quadratic regression, wavelet transform and superposed epoch analysis (SEA) were applied to quantify hydrological evolution, periodic signals, large-scale climate forcing and statistical coupling between dry/wet extremes and historical yield reduction records. Results: The northern watershed showed stronger interannual runoff oscillation. Both regions entered persistent low-flow phases post-1950. Pacific Decadal Oscillation (PDO) acted as the dominant driver, while solar radiation exerted weak secondary regulation. Severe drought events corresponded closely to historical forest and grain yield losses, with far higher agricultural vulnerability in the southern agro-pastoral ecotone. Conclusions: This study reconstructed the long-term historical runoff of the Greater Khingan Range from the thickness of the cell wall, analyzed the different impacts of PDO on it, and clarified the differentiated effects of drought and flood on agricultural and forestry production losses and the interrelated impact of land use on hydrology and the value of agricultural output. Full article

Background/Objectives: Letrozole combined with intrauterine insemination (IUI) is a common first-line treatment for unexplained and anovulatory infertility; however, the optimal dominant follicle size at human chorionic gonadotrophin (hCG) triggering remains uncertain. We evaluated associations of follicle size with biochemical pregnancy, clinical pregnancy, and live birth. Methods: This retrospective cohort included 596 letrozole–IUI cycles from 454 women aged <40 years treated between May 2024 and August 2025. Cycles were grouped by dominant follicle diameter at trigger (17.0–18.9, 19.0–21.0, and >21.0 mm). Mixed-effects logistic regression accounted for repeated cycles. Results: Biochemical pregnancy, clinical pregnancy, and live-birth rates differed across groups and were highest at 19.0–21.0 mm; live-birth rates were 3.4%, 15.1%, and 8.6%, respectively (p = 0.008). Compared with 17.0–18.9 mm, the 19.0–21.0 mm group had higher adjusted odds of clinical pregnancy (aOR 5.58, 95% CI 1.31–23.84; p = 0.020) and live birth (aOR 15.15, 95% CI 1.74–131.95; p = 0.014), although both estimates were imprecise. Adjusted live-birth probabilities were 3.5%, 16.1%, and 9.1%, respectively. The direction of the live-birth association was preserved in the single-cycle sensitivity analysis (aOR 3.85, 95% CI 1.22–12.13; p = 0.021). Continuous linear and quadratic follicle-diameter models were not significant. Conclusions: The 19.0–21.0 mm category was associated with higher reproductive outcome rates, including live birth; however, the observational design, sparse events, wide confidence intervals, and null continuous analyses preclude defining an optimal trigger threshold. These findings are hypothesis-generating and require prospective multicenter confirmation. Full article

In regression tasks, point estimates are insufficient—interval uncertainty must be quantified. The two main criteria for evaluating prediction intervals—Prediction Interval Coverage Probability (PICP) and Normalized Average Width (PINAW)—are conflicting, forming a Pareto front. This paper presents GA-PC, a novel genetic algorithm that modifies NSGA-II by introducing auxiliary calibration variables with a quadratic penalty. Unlike heuristic approaches, the introduction of these variables is theoretically justified via Noether’s second theorem and Bianchi identities: they correspond to gauge degrees of freedom, and the penalty acts as gauge fixing, improving convergence without altering the Pareto set. On ZDT2, GA-PC yields results that match the best reported values. On real financial data, the method provides full PICP coverage (0.000–1.000) and a wider PINAW range than NSGA-II and MOEA/D. The algorithm is scalable, exhibits transferability of hyperparameters, and is applicable to any number of criteria. Code available in the Supplementary File. Full article

Finite-dimensional closed-system gate synthesis is a geometric optimal-control problem on a compact Lie group, but global solutions require careful treatment of logarithm branches, determinant-one constraints, drift, anisotropic penalties, and amplitude limits. This paper assembles and extends a self-contained family of exactly solved benchmarks under explicit hypotheses. For a positive right-invariant quadratic metric, smooth stationary curves obey the Euler–Arnold equation and possess Lax invariants. Under fully actuated isotropic control on $\mathrm{SU}\left(n\right)$, the fixed-time minimum action is the squared Hilbert–Schmidt distance divided by twice the gate time and is generated by a minimum-norm skew-Hermitian logarithm. A finite eigenphase-unwrapping rule computes that logarithm, while the affine spectral-width cut wall and a branch-gap criterion distinguish stable selection from set-valued behavior. Drift is removed isometrically whenever the metric is invariant under the drift adjoint action. Weighted action and box-constrained time on maximal tori and arbitrary closed commuting subtori reduce to explicit period-lattice problems; the latter gives distinct strict determinant-one and projective period lattices for an ideal fSim sector. Numerical validation uses 1506 Haar-random targets through dimension 128, a complete cost-bounded dynamic-program cross-check for every target, 300 direct Cartesian branch enumerations, 120 random-conjugation tests with analytically prescribed spectra, 240 independently cross-checked random weighted-torus instances, a separate $\mathrm{SU}\left(256\right)$ stress target, large-cluster selector regressions, repeated timing trials, GRAPE–L-BFGS, a first-order sequential Krotov-type update with exact discrete propagation and Fréchet derivatives, randomized and drift-adapted CRAB–Powell bases, and cut-locus tests that exercise the implemented Schur/logarithm solver. When sparse actuation, decoherence, leakage, or uncertainty invalidates the ideal hypotheses, the exact results are positioned as ideal-model reference values, branch-aware seeds, and regression tests rather than as certificates for the enlarged objective. Full article

The rheology of high-temperature and high-pressure oil-based drilling fluids is critical for managed pressure drilling in deep and ultra-deep wells. This study investigates the rheological behavior of high-temperature and high-pressure oil-based drilling fluid from the Tarim Basin under conditions of 60–160 °C and 60–140 MPa. Three oil-based drilling fluids with densities of 1.8, 2.0, and 2.2 g/cm³ were tested under 60 temperature–pressure–density conditions, and six rotational speeds were selected for each condition, resulting in 360 rheological data points for model evaluation and parameter prediction. Rheological models, including Bingham, Power law, Casson, and Herschel–Bulkley, were established and evaluated to identify the optimal model. An improved high-temperature and high-pressure rheological parameter prediction model was proposed. Unlike previous correlations mainly developed for Bingham rheological parameters, the proposed model directly predicts the three Herschel–Bulkley parameters and introduces a quadratic pressure term to describe the nonlinear pressure dependence under high-temperature and high-pressure conditions. The results indicate that the Herschel–Bulkley model best characterizes the fluid’s rheological properties, with an average relative error of 2.64% in shear stress prediction and superior regression accuracy compared to Landmark WellPlan software, achieving an average error of 2.049%. Density significantly affects yield stress and consistency coefficient, while temperature and pressure have minimal impact on the flow index. Rheological parameters exhibit opposite trends under varying densities. The improved Herschel–Bulkley model enables precise rheological parameter predictions within the tested range, with average prediction accuracies of 81.40% for yield stress, 97.90% for flow index, and 87.05% for consistency coefficient, meeting engineering requirements. These findings provide theoretical support for managed pressure drilling in deep and ultra-deep wells. Full article

Leaf nitrogen content (LNC) is an important physiological indicator for evaluating citrus nutritional status, photosynthetic capacity, and fertilization demand. However, conventional LNC determination mainly relies on field sampling and laboratory chemical analysis, which are destructive, time-consuming, labor-intensive, and limited in spatial continuity, making them unsuitable for large-scale real-time nitrogen monitoring in complex orchard environments. To achieve rapid and non-destructive estimation of citrus LNC, this study developed a UAV multispectral inversion framework integrating object-based canopy extraction and machine learning models. Field experiments were conducted in a citrus orchard in western Hubei Province, China. Multi-temporal UAV multispectral images were collected from April to October 2025, and ground measurements of citrus LNC were collected simultaneously. First, minimum distance classification (MDC), maximum likelihood classification (MLC), and object-based image analysis (OBIA) were used for land-cover classification of citrus orchard images, and their canopy extraction performance under complex orchard backgrounds was compared. Subsequently, multiple vegetation indices were calculated from the extracted citrus canopy spectra, and sensitive spectral features were selected through correlation analysis. Finally, seven models, including simple linear regression, quadratic regression, partial least squares regression (PLS), back propagation neural network (BP), extreme learning machine (ELM), particle swarm optimization-extreme learning machine (PSO-ELM), and particle swarm optimization-back propagation neural network (PSO-BP), were constructed to systematically evaluate the inversion performance of citrus LNC across the entire growth period. The results showed that: (1) OBIA achieved higher classification accuracy and temporal stability in citrus orchard land-cover classification, with overall accuracy ranging from 68.86% to 85.65% and Kappa coefficients ranging from 0.56 to 0.72, outperforming MDC and MLC. This indicates that OBIA can effectively reduce the interference of bare soil, grass, shadows, and other non-target objects on canopy spectral extraction. (2) The correlations between vegetation indices and LNC varied markedly among different growth stages, suggesting that the spectral response of citrus LNC has strong phenological dependence and that a single vegetation index is insufficient to stably characterize LNC variation across the whole growth period. (3) At the whole-growth-period scale, multi-index fusion models outperformed single-index models, among which EVI, TVI, and MTVI showed relatively strong cross-stage sensitivity. (4) Optimized machine learning models generally outperformed traditional regression models and unoptimized machine learning models. Among them, PSO-BP achieved the best performance, with a validation R² of 0.68 and an RMSE of 1.54 g kg⁻¹, representing an increase in R² of 23.64% compared with the PLS model and 25.93% over the baseline BP model in terms of R². Overall, this study demonstrates that OBIA-based canopy spectral quality improvement combined with PSO-optimized machine learning can effectively improve the stability and reliability of UAV multispectral estimation of citrus LNC under complex orchard backgrounds. The proposed framework provides technical support for citrus nitrogen diagnosis, precision fertilization, and intelligent orchard management. Full article

Natural inland saline–alkaline wetlands offer opportunities for evaluating microbial responses to long-term salinity stress. This study examined surface soils from non-saline, moderately saline, and hypersaline sites in the Luyang Lake wetland, measuring comprehensive edaphic variables (including SAR, ESP, carbonate/bicarbonate chemistry, moisture, DOC, and inorganic N) alongside bacterial and fungal communities via 16S rRNA and ITS sequencing. A coupled salinity–ion and nutrient gradient was identified, with hypersaline soils characterized by high Na⁺, Cl⁻, SAR, and ESP alongside depleted organic carbon and nitrogen. Bacterial α-diversity exhibited a significant unimodal response along the salinity gradient (quadratic regression: p < 0.001), peaking at moderate salinity. Fungal Shannon diversity declined with increasing salinity, but fungal Chao1 richness showed a U-shaped response, highlighting domain-specific and metric-dependent patterns along the gradient. Community assembly analyses revealed contrasting dynamics: deterministic processes were more prevalent in bacterial assembly in hypersaline soils, while fungal assembly remained predominantly stochastic. Co-occurrence networks showed sparser topological structure in high-salinity soils. These patterns are consistent with domain-specific microbial variation along the gradient to coupled edaphic stressors in inland saline–alkaline wetlands. Full article

The sectoral approach has traditionally served as the principal tool for analyzing regional specialization; however, it does not reveal the functional content of employment or the distribution of tasks within economic activities. This article assesses the degree of correspondence between the functional structure of employment and the sectoral structure of output across 17 harmonized regions of Kazakhstan, a resource-dependent post-Soviet economy, over the period 2019–2024. Regional functional profiles are constructed within the task-based framework and comprise four aggregate task categories: non-routine cognitive (NRC), routine cognitive (RC), routine manual (RM), and non-routine manual (NRM). The sectoral structure is represented by the distribution of nominal output across 47 sectors. Inter-regional differences are measured using Jensen–Shannon distance. The empirical analysis is conducted on a dyadic panel of 816 observations, controlling for region size, urbanization, gross regional product per capita, extractive specialization, geographic distance, and time effects. Primary statistical inference relies on the Freedman–Lane permutation procedure within multiple regression quadratic assignment procedure (MRQAP). The results indicate a positive but moderate association between the two structures. The standardized coefficient on functional distance is 0.300 (MRQAP p = 0.001). Including the task variable raises R² by 5.5 percentage points. The finding remains robust under most alternative distance metrics, different sets of control variables, and sequential exclusion of individual regions. However, the association weakens noticeably when the three largest cities—concentrating a substantial share of non-routine cognitive tasks—are excluded simultaneously. Within-region estimates of correspondence for 2019–2024 are considerably less precise. Pair fixed-effects models and correlations based on net change and cumulative path length do not reveal a systematic contemporaneous relationship between changes in functional and sectoral structures over the observed period. The six-year observation window does not allow the longer-term pattern of their coevolution or possible lagged adjustment to be determined. Overall, functional and sectoral structures are related but not interchangeable. The results provide statistically supported evidence of a positive inter-regional association, although its strength varies by distance metrics and regional subsamples. For regional policy, the findings underscore the value of combining sectoral specialization indicators with information on the characteristics of jobs and accumulated labor competencies. Full article

The increasing demand for sustainable building materials has motivated the search for alternative supplementary cementitious materials to reduce the use of Portland cement while maintaining concrete performance. The present study aims to investigate the synergistic effects of bagasse ash (BA) and rice husk ash (RHA) as partial cement replacements in ternary blended concrete. Previous studies used agricultural ashes individually or in binary form only, whereas in the present work, the synergistic effect of BA and RHA is systematically studied, and Response Surface Methodology (RSM) is used to develop predictive models and optimize the performance of concrete. The slump, compressive strength and splitting tensile strength were evaluated using a Central Composite Design (CCD) to determine the effect of the levels of replacement of BA and RHA. Quadratic regression models were built and evaluated using analysis of variance (ANOVA). All models were statistically significant (p < 0.05) and had high predictive accuracy (R² > 0.92). The results revealed that increases in BA and RHA contents reduced the workability because of their high specific surface areas and porous structures, while moderate combinations improved the compressive and splitting tensile strengths due to the synergistic filler effects, secondary pozzolanic reactions, and matrix densification. The multi-objective optimization based on the desirability function provided an optimal mixture of 5% BA and 15% RHA with an overall desirability of 92.8%, which provided the best compromise between workability and mechanical performance. Experimental validation of the optimized mixture showed good agreement of the model predictions with prediction errors of less than 5%, confirming the reliability and robustness of the developed RSM models. The results show that synergistic use of BA and RHA is a feasible and sustainable solution for producing high-performance ternary blended concrete and provides a reliable framework for the optimization of the mixture. Full article

Methods capable of inferring the nutritional requirements of forage cultivars from related genotypes may help generate preliminary fertilization recommendations when experimental information is limited. This study evaluated whether model identity analysis can be used to infer the nitrogen requirement of Mulato II hybrid grass from the response patterns of its progenitor species, Urochloa brizantha cv. Marandu and Urochloa decumbens cv. Basilisk. A greenhouse experiment using pots filled with an Oxisol was conducted using a completely randomized design in a 3 × 5 factorial arrangement, with three grasses and five nitrogen doses (0, 100, 200, 300, and 400 mg dm⁻³). Forage production, tiller density, and nutritive value were evaluated during establishment and regrowth phases. Linear and quadratic regressions were fitted, and significant models were compared using model identity tests. When model differences were detected, intercepts and parameters were further compared using F-tests. In both establishment and regrowth phases, Mulato II showed response patterns similar to Urochloa brizantha for most evaluated variables, with model identity observed except for tiller number and crude protein concentration. In contrast, Mulato II differed from Urochloa decumbens for all measured traits. Therefore, under the greenhouse conditions evaluated, the nitrogen requirement of Mulato II is more closely related to that of Urochloa brizantha, demonstrating the potential of model identity analysis as a tool for generating preliminary fertilization recommendations for forage cultivars from related genotypes before field validation. Full article

Aging of asphalt pavements is a complex, multi-scale degradative process driven by the synergistic effects of various environmental stressors. Traditional laboratory-accelerated aging protocols often employ static parameters that fail to accurately replicate dynamic, region-specific climatic conditions. To bridge the gap between laboratory simulations and actual field performance, this study investigates the aging behaviors of base binder and SBS-modified binder under multi-factor coupled environmental conditions. Field observations were conducted across six distinct climatic regions in Gansu Province, alongside an indoor second-order orthogonal regression composite design that evaluated the interactive effects of temperature, ultraviolet (UV) radiation, humidity, and aging time. Rheological evaluations revealed that for the base binder, the synergistic coupling of UV radiation, elevated temperatures, and high humidity significantly accelerates oxidative hardening and embrittlement far beyond the impact of any single factor. Conversely, SBS-modified binder demonstrated a non-linear, U-shaped rheological response governed by a competitive mechanism between UV/thermal-induced polymer scission and moisture/time-driven matrix oxidation. Fourier Transform Infrared (FT-IR) spectroscopy corroborated these macroscopic findings at the molecular level, tracking the simultaneous evolution of carbonyl and sulfoxide indices alongside the degradation of the polybutadiene segments in the modified binder. Ultimately, a quadratic polynomial regression model was established to precisely correlate natural field aging with equivalent indoor accelerated aging times based on specific regional climatic data. Full article

Microstructures with narrow openings and enlarged internal volumes are highly desirable for durable superlubricating surfaces, as they enable increased lubricant storage and improved lubrication stability. However, conventional laser texturing typically produces tapered features with wider openings due to the intrinsic Gaussian energy distribution of laser beams, limiting the achievable internal volume. To overcome this limitation, this study investigates an oblique dual-beam femtosecond laser ablation strategy for polyamide 6 (PA6) using a mechanism-informed optimization framework integrating COMSOL-based multiphysics simulations with response surface methodology (RSM). The effects of pulse energy, incident angle, and beam separation on micro-pit morphology are systematically analyzed. Pulse energy governs ablation intensity and feature scaling; increasing incident angle suppresses depth while promoting lateral expansion; and reduced beam separation enhances depth through local energy overlap. Quadratic regression models for pit depth, inner width, outer width, and ablated area are established via a central composite design and exhibit strong predictive capability. Multi-objective optimization identifies an effective processing window of 30–40 μJ pulse energy, 20–40° incident angle, and −12 to 12 μm beam separation, enabling uniform micro-pits with narrow openings and depths of 70–80 μm. Overall, this work provides a concise and predictive framework for oblique dual-beam laser texturing for predicting and guiding the future fabrication of narrow-opening, enlarged-interior microstructures. Full article

To simultaneously suppress shrinkage-related defects and refine the solidification microstructure of Mn70Ni25Cr5 alloy ingots, ProCAST simulation was combined with Box–Behnken response surface methodology to optimize pouring temperature, filling time, and mold temperature. Porosity in the ingot body and secondary dendrite arm spacing (SDAS) were selected as the response variables, and quadratic regression models were established for both responses. The optimized casting parameters were determined using analysis of variance, response surface analysis, and the desirability function approach. The porosity and SDAS models were both statistically significant, with non-significant lack-of-fit terms and R² values of 0.9906 and 0.9901, respectively. The optimal parameters were a pouring temperature of 1220.74 °C, a filling time of 6.34 s, and a mold temperature of 294.47 °C, corresponding to a predicted porosity of 0.426% and a predicted SDAS of 47.51 μm. A supplementary simulation and a validation casting experiment were then performed using practical process settings derived from the optimized solution. The supplementary simulation indicated that shrinkage-related defects were concentrated mainly in the riser, while metallographic examination revealed no large continuous shrinkage-porosity region in the examined ingot-body sections. The overall measured SDAS across the center, half-radius, and edge positions was 48.54 μm, differing from the response-surface prediction by approximately 2.2%. These results support the applicability of the combined ProCAST–RSM approach for simulation-assisted optimization of Mn70Ni25Cr5 alloy casting parameters within the investigated process range. Full article

The factors influencing the cutting efficiency of tunnel boring machine (TBM) cutters in jointed rock masses are very complex. To investigate TBM disc cutter cutting performance under variable cutter spacing and penetration depth, Particle Flow Code (PFC) 2D discrete element numerical simulation is carried out on a granite jointed rock mass. The numerical model adopts a disc cutter tip angle of 20° and tip width of 12 mm, joint spacing of 5 mm, joint inclination angle of 45°, and lateral confining pressure of 2.5 MPa; cutter spacing is set to 60, 80, 100, 120 mm, and penetration depth ranges from 2 mm to 10 mm as research variables. The force chain distribution, jointed rock mass failure modes, penetration load and cutting efficiency of disc cutters under different working conditions are systematically analyzed. An indicator for measuring the cutting efficiency called “crack propagation specific energy” is proposed. Based on the numerical simulation results, a complete quadratic multivariate nonlinear regression model is established to predict cutting efficiency. The results show that the optimal cutting performance occurs at a cutter spacing of 80 mm, where the shear failure proportion of contact bonds and cutting efficiency simultaneously reach the maximum, while incomplete penetration of joint failure surfaces and small cutting areas appear under 60 mm and 120 mm cutter spacing. With the increase in the disc cutter penetration depth, the shear failure proportion of contact bonds rises continuously, and the number of tensile failure microcracks gradually decreases. The research outcomes can provide a theoretical reference for TBM shield tunnel construction parameter optimization. Full article

Decoupling economic growth from carbon emissions is central to the sustainable development of rapidly urbanizing economies, and urban agglomerations are the pivotal spatial units for delivering this transition under China’s dual-carbon goals, yet systematic cross-agglomeration comparisons that could inform differentiated sustainability policy remain scarce. Using panel data for 107 prefecture-level cities in five agglomerations—the Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH), Pearl River Delta (PRD), Chengdu–Chongqing (CY), and the middle reaches of the Yangtze River (MRYR)—across five benchmark years spanning 2005–2023, we combined a two-way fixed-effects environmental Kuznets curve (EKC) model, the Tapio decoupling model, and cross-sectional quadrant analysis to examine the growth–emission relationship in shape, decoupling dynamics, and spatial structure. All five agglomerations traced an inverted-U trajectory, with turning-point per capita gross domestic product (GDP) rising in the order CY < PRD < BTH < MRYR < YRD. Once fixed effects and structural controls were added, most quadratic terms became insignificant and reversed sign after the secondary-industry share and carbon intensity entered; only the PRD and BTH retained a significant nonlinear form. The net income effect is therefore largely monotonic, with the inverted U carried by industrial upgrading and energy-efficiency gains. Tapio decoupling followed a non-monotonic “improve-then-regress” path, with expansive negative decoupling re-emerging across all agglomerations during 2020–2023. Spatially, high-value clustering persisted in the YRD, weakened in the BTH after 2020, and concentrated on single cores in Chengdu and Wuhan. We accordingly propose sustainability-oriented low-carbon pathways differentiated jointly by agglomeration and quadrant. By showing that decoupling is stage-dependent and reversible rather than an automatic by-product of income growth, our findings indicate that durable progress toward regional sustainability hinges on structural transformation and coordinated governance tailored to each agglomeration’s stage of development. Full article