Search Results (480)

Against China’s dual carbon peaking and carbon neutrality strategic goals, nationwide digital transformation brings both carbon abatement dividends and potential energy rebound risks, and its full low-carbon potential is constrained by local industrial foundations and regional economic development stages. Most existing studies merely treat industrial upgrading as an intermediate transmission channel, with little discussion of its moderating influence. Moreover, few threshold analyses take the comprehensive level of regional economic development as the core threshold variable to capture the boundary conditions of digital decarbonization effects. Based on balanced panel data covering 30 provincial-level regions of China from 2011 to 2023, this paper constructs a multi-dimensional digital economy index via the entropy weight method. Prior to formal regression, we conduct Pearson correlation analysis and mean-centered VIF multicollinearity diagnostics to avoid biased estimation. Two-way fixed-effects regression, moderation tests, Bootstrap-based regional heterogeneity comparison and Hansen’s single threshold model are adopted for empirical analysis. The results show that digital economy development significantly curbs carbon emission intensity; a one-standard-deviation increase in the digital economy composite index is associated with an approximately 9.7% decline in carbon emission intensity. The mean-centered interaction term DIG × UIS is significantly negative at the 1% level, proving that service-oriented industrial upgrading strengthens the carbon reduction effect of digitalization. The mitigation effect displays distinct spatial divergence: the estimated coefficient equals −2.638 for eastern provinces, −3.585 for central regions and −1.700 for western areas. Bootstrap inter-group coefficient tests confirm statistically significant gaps between east–west and central–western subgroups. Threshold regression identifies a single threshold of logarithmic per capita GDP at 11.94. After crossing this economic development threshold, the inhibitory coefficient of the digital economy rises markedly from −0.844 to −1.473. This study enriches the theoretical system of digital low-carbon transition by jointly uncovering the moderating role of industrial upgrading and the stage threshold constraint of economic development and offers differentiated digital low-carbon policy guidance for provincial governments. Full article

Background: Pericardial effusion is a common finding on pre-procedural computed tomography (CT) in patients undergoing transcatheter aortic valve implantation (TAVI). However, its clinical significance remains uncertain. Using artificial intelligence (AI)-based image analysis, we sought to determine whether pericardial effusion represents an incidental imaging finding, a marker of hemodynamic burden, or a prognostically relevant phenotype. Methods: This retrospective single-center study included 470 consecutive patients undergoing TAVI for severe aortic stenosis. Pericardial effusion volume was quantified using an AI-based CT segmentation workflow and categorized as no effusion (0 mL), trace effusion (>0–<5 mL), small effusion (5–<30 mL), or larger effusion (≥30 mL). Associations with baseline clinical and echocardiographic characteristics were assessed. Long-term mortality was evaluated using Kaplan–Meier analysis, Cox regression models, and restricted cubic spline analyses. Results: Detectable pericardial effusion was present in 276 patients (58.7%), although larger effusions were uncommon (5.1%). Increasing pericardial effusion volume was associated with a higher prevalence of atrial fibrillation (p = 0.001), higher systolic pulmonary artery pressure (p = 0.004), and lower TAPSE/sPAP ratios (p = 0.007). In contrast, left ventricular ejection fraction and transvalvular gradients did not differ across effusion categories. During long-term follow-up, no significant differences in mortality were observed between pericardial effusion groups (log-rank p = 0.46). Pericardial effusion volume was not associated with mortality when analyzed as a continuous variable (HR 1.00, 95% CI 0.996–1.01; p = 0.842), after logarithmic transformation (adjusted HR 0.91, 95% CI 0.76–1.08; p = 0.293), or in restricted cubic spline analyses. Conclusions: AI-derived pericardial effusion volume identifies a hemodynamic phenotype characterized by atrial fibrillation, pulmonary hypertension, and impaired right ventricular–pulmonary arterial coupling. Despite these associations, pericardial effusion volume does not independently predict long-term mortality after TAVI, suggesting that it reflects cardiovascular congestion and remodeling rather than a prognostically relevant phenotype. Full article

Hospital buildings (HBs) are among the most energy-intensive public buildings, yet their life-cycle carbon characteristics, emission drivers, and long-term mitigation potential remain insufficiently quantified. This study establishes a comprehensive life-cycle carbon assessment framework for HBs based on life cycle assessment (LCA), using a Grade-A tertiary hospital in Shenzhen, China, as a case study. The framework quantifies carbon emissions across the materialization, operation, and demolition stages, and estimates operational emissions from public hospital buildings at the city scale. Logarithmic Mean Divisia Index (LMDI) decomposition and Long-range Energy Alternatives Planning (LEAP) modeling were subsequently applied to identify historical drivers and evaluate future mitigation pathways. The results show that the case hospital generated approximately 0.57 Mt CO₂e of gross life-cycle carbon emissions over a 50-year service life, with the operational stage dominating approximately 88% of net emissions. Electricity consumption accounted for 94% of operational energy-related emissions, while HVAC systems and the Diagnostic departments were identified as major carbon hotspots. At the city scale, the gross operational emissions of 73 public hospitals in Shenzhen were estimated at approximately 0.74 Mt CO₂e in 2020 within the defined accounting boundary. For the broader citywide hospital sector, LMDI analysis revealed that annual operational emissions increased from approximately 0.25 Mt CO₂e in 2006 to 0.91 Mt CO₂e in 2020, primarily driven by healthcare service demand and hospital infrastructure expansion, whereas the declining operational carbon emission coefficient provided a partial offset. LEAP scenario analysis further demonstrated that net operational emissions peaked in 2050 under BS and in 2030 under SI and SII. Under SIII, emissions declined continuously from the 2020 base-year level to approximately 0.29 Mt CO₂e in 2060, representing a reduction of approximately 68%. These findings highlight the necessity of coordinate building energy optimization, healthcare infrastructure development, and energy system decarbonization for low-carbon transformation of hospital buildings. Full article

To address ambiguous maneuver boundaries, background interference, and uneven multi-sensor quality in long-duration flight parameter recordings, this paper proposes an adaptive flight maneuver boundary localization algorithm that integrates spectral entropy-weighted multi-channel spectrogram fusion with attitude-constrained structural correction. Multi-channel Short-Time Fourier Transform (STFT) spectrograms are first constructed from flight parameter time series. Spectral entropy (SE) is introduced to quantify the uncertainty of each channel’s time–frequency energy distribution and is combined with the maneuver activation ratio (MAR) and the linear contrast ratio (LCR) to form objective credibility weights, thereby suppressing channels dominated by aerodynamic turbulence and high frequency structural vibration. Normal overload soft gating and logarithmic noise floor subtraction are then applied to obtain an enhanced fused spectrogram, from which candidate intervals are extracted by low band energy thresholding. Finally, roll and pitch angle steady-state priors refine the event structure through local boundary refinement, cross-segment expansion/chain merging, and semantic post-processing, recovering continuous maneuvers fragmented by instantaneous energy valleys. On the held-out test sorties (SE_018–SE_020; 61 annotated intervals), the proposed algorithm achieves Precision, Recall, and F1-scores of 0.967. On the full primary corpus of 20 sorties (461 intervals), used for ablation and sensitivity analyses, the corresponding figures are Precision 0.934, Recall 0.959, and F1 0.946, with start and end boundary mean absolute errors of 1.484 s and 1.471 s. Under the same IoU protocol, consistent superiority is observed against learning-based baselines, and an independent external set of 10 sorties yields F1 = 0.938. The results indicate that entropy-constrained multi-sensor time–frequency fusion mainly improves maneuver/background separability, whereas attitude-constrained structural correction restores the integrity of long continuous maneuvers. Full article

The standard models of stock-price dynamics and option valuation rest on stochastic processes postulated at the outset; here, we lay down an entropic-inference framework that derives these processes rather than assuming them, by making explicit the information each one encodes. A symmetry comes first: markets reward returns rather than price levels, which selects the logarithm of price as the dynamical variable. The price then evolves through two channels, a continuous one carrying the constraints of continuity and directionality, and a jump channel carrying the arrival rate and the first two moments of the jump size. Because these constraints act on disjoint parts of the microstate, the channels factorize as a theorem, and the dynamics is the Merton jump-diffusion, with Geometric Brownian Motion as its no-jump limit; the log-price density obeys a Kolmogorov–Feller equation, of which the Fokker–Planck equation is the no-jump limit. The same principle, now imposing no-arbitrage through the mean log-return, selects the Esscher transform from among the many martingale measures an incomplete market admits, here derived rather than borrowed; the premium then satisfies Merton’s partial integro-differential equation, and the risk-neutral mixture of lognormals generates the implied-volatility smile, the Black–Scholes results returning when jumps vanish. What changes from one model to the next is never the inference but the information supplied to it. Full article

The Rayleigh–Logarithmic distribution provides a useful framework for modelling lifetime data by combining continuous lifetime variability with a logarithmic compounding mechanism. This study introduces a three-parameter Alpha Power Rayleigh–Logarithmic (APRL) distribution by applying the Alpha Power transformation to the classical Rayleigh–Logarithmic model. The additional transformation parameter allows the distributional shape, skewness, tail behaviour, and rate of increase in the hazard function to be adjusted while retaining the underlying structure of the baseline model. Several mathematical and reliability properties of the APRL distribution are derived, including the probability density and cumulative distribution functions, survival and hazard rate functions, quantile function, moments, order statistics, and mean residual life function. Model parameters are estimated by maximum likelihood using a multiple-start numerical optimization procedure, and the finite-sample performance of the estimators is investigated through Monte Carlo simulations under different parameter configurations and sample sizes. The simulation results show that estimation accuracy generally improves with increasing sample size, as reflected by decreasing bias, MSE, and RMSE, although estimation of the transformation parameter may exhibit greater variability for more extreme parameter settings. The practical performance of the APRL distribution is examined using the Aircraft Windshield Failure Times and Breaking Stress of Carbon Fibres datasets. Model comparisons based on information criteria, bootstrap-based goodness-of-fit assessment, and graphical diagnostics show that the APRL distribution provides competitive fits relative to several established lifetime distributions. In addition, mean time to failure and mean residual life analyses illustrate the practical interpretation of the reliability measures derived for the proposed model. Overall, the results support the APRL distribution as a useful alternative for the statistical analysis of lifetime and reliability data. Full article

Accurate estimation of grassland biomass is fundamental for designing sustainable grazing strategies and optimizing pasture management, yet conventional field methods remain labor-intensive, destructive, and difficult to scale. In this study, we exploit recent advances in computer vision to estimate multiple components of grassland biomass from overhead RGB imagery. The analysis is based on the Image2Biomass dataset, comprising 1162 annotated images of grasslands across Australia, each paired with laboratory-validated biomass measurements. A structured preprocessing pipeline was implemented, including exploratory data analysis, outlier mitigation, and logarithmic transformation of target variables, in accordance with the dataset evaluation protocol. Building on this foundation, we propose an encoder–decoder regression framework that integrates self-supervised visual representation learning with ensemble-based prediction. The encoder employs a DINOv2 Giant model as a feature extractor to capture detailed spatial and structural characteristics of the sward, while the decoder uses a stacking ensemble combining LightGBM, XGBoost, and Ridge Regression. Across 15 repetitions of shuffled four-fold cross-validation, the cross-fitted stacking ensemble achieved a weighted coefficient of determination of $R_w^2=0.7757\pm 0.0171$, a weighted mean absolute error of $8.3867\pm 0.2736$ g, and a weighted root mean squared error of $13.3994\pm 0.4995$ g. The ensemble significantly outperformed LightGBM, XGBoost, and Ridge Regression on the primary weighted $R^2$ metric in paired comparisons (Holm-adjusted $p<0.001$ for all three comparisons). These results highlight the potential of computer vision methods as scalable, non-destructive tools for operational monitoring of grassland biomass, supporting more informed agronomic decision-making in pasture-based systems. Full article

Laplace factor models (LFMs) provide a heavy-tailed alternative to Gaussian factor models by representing high-dimensional observations through a low-rank common component and Laplace-distributed idiosyncratic errors. This paper develops an assumption-consistent finite-sample analysis of matrix concentration, covariance estimation, and Monte Carlo integration under this model. We first formulate the model with explicit dimensional, independence, covariance, and identifiability conditions. Standard matrix Laplace-transform and matrix Bernstein inequalities are then recalled with their precise applicability conditions. Because untruncated Laplace variables are neither almost surely bounded nor strongly log-concave, these standard results cannot be applied directly in the forms commonly used for bounded or Gaussian-like observations. To address this issue, we analyze a coordinatewise truncated covariance estimator and derive an operator-norm bound that separates the stochastic estimation error from the truncation bias. The resulting rate depends on the effective rank and the logarithm of the ambient dimension and is therefore not dimension-free. For Monte Carlo integration, we replace strong-log-concavity arguments by a sub-exponential concentration analysis that is compatible with independent Laplace errors and yields non-asymptotic absolute- and relative-error bounds. Simulation studies compare empirical tails with the classical matrix Bernstein bound, evaluate ordinary, truncated, winsorized, PCA, POET-type, and Huberized covariance estimators, and we compare Laplace-based and Studentized confidence intervals. The results show that the classical Bernstein bound can be conservative, and truncation involves a substantial bias–variance trade-off. In a Wine chemical-analysis application, three factors explain 66.53% of the standardized variance, and POET-type covariance estimation attains a cross-validated balanced accuracy of 0.9901. These findings clarify both the scope and the limitations of finite-sample analysis for LFMs. Full article

Background: The aim of this study was to evaluate the diagnostic performance of combined testicular function indices in patients with non-obstructive azoospermia and to investigate the clinical value of these indices in predicting the diagnosis of non-obstructive azoospermia and the success of testicular sperm extraction. Methods: This retrospective observational study included 224 male patients who underwent infertility assessment. The patients were classified into a normospermic group (n = 165) and a non-obstructive azoospermia group (n = 59). Serum anti-Müllerian hormone, follicle-stimulating hormone, luteinising hormone and total testosterone levels were assessed. The Testicular Function Index, the modified Testicular Function Index and the logarithmically transformed index were calculated. Patients in the non-obstructive azoospermia group underwent micro-TESE and were classified according to sperm retrieval status. Diagnostic performance was assessed using ROC analysis. Results: Among the 59 patients with non-obstructive azoospermia who underwent micro-TESE, sperm retrieval was successful in 26 patients and unsuccessful in 33 patients. In the non-obstructive azoospermia group, anti-Müllerian hormone and testosterone levels were lower, whilst follicle-stimulating hormone and luteinising hormone levels were higher (all p < 0.001). All combined indices were found to be significantly lower in the non-obstructive azoospermia group (all p < 0.001). In the ROC analysis, follicle-stimulating hormone demonstrated the highest discriminatory performance (AUC = 0.978). In the testicular sperm extraction analysis, anti-Müllerian hormone was found to be the marker with the highest performance (AUC = 0.930). Conclusions: (Follicle-stimulating hormone demonstrated the highest diagnostic performance for distinguishing non-obstructive azoospermia, whereas anti-Müllerian hormone was the strongest predictor of TESE success. Although the combined testicular function indices showed good diagnostic performance, they did not outperform these established hormonal markers and should therefore be regarded as complementary tools for the integrated assessment of testicular function. These findings should be considered exploratory and require further validation. Full article

Flow-path switching is a key operating condition that enables flow regulation, zonal conversion, and filling-path redirection in sand-control completions. The associated flow characteristics directly govern the operational stability and service reliability of intelligent filling tools. Most existing studies have addressed erosion only under simple geometries such as pipe contractions and expansions, leaving the dominant erosion-controlling factors and rapid erosion-rate prediction methods for sand-control filling tools under flow-path switching conditions insufficiently understood. This study developed a Fluent-based numerical model of solid–liquid two-phase erosion for intelligent filling tools and characterizes the wall-erosion distribution pattern during flow-path switching. Guided by field practice, multi-factor simulations were performed over the reduction angle, cutting particle size, inlet flow capacity, flow-switching direction, opening area, and structural form. On this basis, a maximum-erosion-rate prediction model was constructed using a logarithmic transformation combined with a stepwise quadratic response-surface method. This regression-based approach was deliberately chosen over machine-learning black-box models, whose limited interpretability and small-sample reliability make it difficult to reveal the underlying physical mechanisms; in contrast, the proposed model yields an explicit algebraic expression whose significant interaction and quadratic terms directly reflect the coupling between structural and operating parameters, while the logarithmic transformation accommodates erosion-rate fluctuations spanning several orders of magnitude. The results show that the factors rank in influence as follows: flow-switching direction > opening area > reduction angle > inlet flow capacity > cutting particle size > structural form. The established prediction model attained a coefficient of determination of about 0.951 and an adjusted coefficient of determination of about 0.901; combined with the significance test and a residual analysis, the model can effectively characterize the coupling influence of structural parameters and operating parameters on the erosion rate. These findings provide quantitative guidance for the erosion-resistant structural design, field operating-parameter selection, and preliminary service-life assessment of intelligent filling tools in offshore sand-control well-completion operations. The prediction model is further validated through jetting-erosion bench tests; the measured erosion rates agree closely with the model-predicted values, confirming the practical reliability of the model and its capability to serve as an engineering reference for the erosion-resistant design and service-life assessment of intelligent filling tools in sand-control well-completion operations. Full article

This paper develops a Fourier framework internal to proportional arithmetic on the positive real line. We construct the corresponding complex scalar field, differential and integral operators, oscillatory kernel, Fourier transform, and proportional function spaces. A correspondence theorem proves that the representative of the proportional transform is the classical Fourier transform under the logarithmic identification. Consequently, inversion, Plancherel, convolution, Schwartz invariance, and Sobolev characterizations follow by transport. We establish the exact relation with Fourier analysis on the multiplicative group and with the Mellin transform on the imaginary axis. Model resolvent and heat equations illustrate the operational calculus, while a scale-localized profile shows how spectral modulus and phase encode log-scale width and preferred scale. The construction is therefore a systematic proportional-arithmetic realization of classical harmonic analysis, rather than an analytically independent Fourier theory. Full article

Monocular depth estimation aims to recover dense 3D scene geometry from a single RGB image and plays an important role in autonomous driving, robotic perception, augmented reality, and 3D reconstruction. Although Transformer-based dense prediction models have achieved strong performance, existing DPT-Hybrid frameworks still suffer from three limitations: insufficient local geometric modeling in shallow stages, inadequate cross-scale fusion for preserving fine structures, and training objectives that only weakly constrain structural consistency. To address these issues, we propose a structure-aware enhanced DPT-Hybrid framework. First, a geometry-enhanced encoder introduces lightweight depth-wise separable convolution branches into shallow Transformer stages to better capture local edge and texture cues while preserving global contextual modeling. Second, a Structure-Aware Cross-Scale Gated Attention Fusion (S-GAF) module is proposed to improve decoder-side feature aggregation by jointly modeling channel-wise and spatial importance with an auxiliary RGB-gradient input. Third, joint structure–geometric consistency loss combines scale-invariant logarithmic loss, gradient consistency loss, and edge-focused loss to improve pixel-level accuracy, geometric plausibility, and boundary sharpness. Experiments on NYUv2 and KITTI demonstrate that the proposed method achieves lower single-run error metrics than the controlled DPT-Hybrid baseline under the evaluated settings. On NYUv2, our method achieves an absolute relative error (AbsRel) of 0.099 and an RMSE of 0.334. On KITTI, it achieves an AbsRel of 0.058 and an RMSE of 2.455. The proposed method introduces only modest additional complexity while producing more accurate and structurally sharper depth predictions. Full article

Classical Laplace-, Sumudu-, and Elzaki-type transforms are formulated within additive analytical frameworks and do not naturally accommodate multiplicative scaling structures arising in non-Newtonian calculus. Motivated by this limitation, this study introduces a non-Newtonian Laplace–Sumudu–Elzaki transform (NNLSET) based on logarithmic scaling mechanisms, multiplicative measures, and power-type kernels. The proposed framework is constructed by replacing the classical measure dt with the multiplicative measure $dt/t$ and the linear scaling structure $f\left(ut\right)$ with the nonlinear scaling structure $f\left(t^u\right)$. Using the logarithmic transformation $t=e^x$, a canonical kernel representation of the form $t^{-\alpha \left(u\right)}$ is derived, establishing a correspondence between multiplicative power-type kernels and weighted exponential structures in the logarithmic domain. Within an admissible weighted function framework, several analytical properties of the transform are established, including existence, boundedness, stability, uniqueness, restricted recoverability, and a logarithmic derivative representation associated with expressions of the form $t{f}^{\prime }\left(t\right)$. A comparative analysis with the classical Laplace–Sumudu–Elzaki framework, together with illustrative differential-equation examples, a representative nonlinear MEMS oscillator, and a numerical computation, is presented. The obtained results demonstrate that the NNLSET provides a mathematically consistent framework for the analysis of multiplicative structures, logarithmic scaling phenomena, and logarithmically structured differential equations. Its applicability is further illustrated through the analysis of a representative nonlinear MEMS oscillator. Full article

This paper applies established saddlepoint approximation techniques to nonlinear statistics arising from inverse Gaussian models. In particular, we consider the product of independent inverse Gaussian random variables and the ratio of weighted linear combinations of inverse Gaussian random variables, for which exact distributions are generally unavailable in closed form. For the product statistic, a logarithmic transformation converts the problem into one involving the cumulant generating function of a sum of log-transformed variables. This cumulant generating function is expressed in terms of fractional moments including modified Bessel functions of the second kind. For the ratio statistic, the event including the ratio is reformulated in terms of a linear statistic, which enables the use of saddlepoint density and Lugannani-Rice distribution approximations. The proposed formulation accommodates heterogeneous model parameters, overlapping numerator and denominator components, and flexible coefficient structures subject to positivity of the denominator. Simulation studies show that the proposed approximations provide accurate results across a range of sample sizes, skewness regimes, and parameter configurations. Furthermore, simulation results indicate that the saddlepoint approximation is more accurate than the normal approximation. Sensitivity analysis confirms that the proposed approximations are reasonably stable under moderate inverse Gaussian parameter misspecification. Three real data applications including clinical illness scores, engineering repair times, and environmental runoff measurements illustrate the practical usefulness of the approach. Overall, the results indicate that the saddlepoint approximation provides an accurate and computationally efficient tool for inference on nonlinear statistics from inverse Gaussian models when exact distributions are not available. Full article

The Collatz map is a simple deterministic transformation whose orbit structure remains highly nontrivial. A recent direction-phase decomposition partitions each orbit into upward and downward steps, and numerical observations indicate that the number of upward phases, $N_{\uparrow }$, follows an approximate Gamma distribution. In this work, we provide a mechanistic explanation for this statistical regularity by modeling the occurrence of upward phases in the odd-compressed, or Syracuse, version of the Collatz map as a homogeneous Poisson process. From the mean-field logarithmic balance and the geometric distribution of 2-adic valuations, we derive closed-form expressions for the Gamma parameters: the scale parameter $\theta =2/{(2-{\log }_{2}3)}^2\approx 11.61$ is constant, whereas the shape parameter K grows logarithmically with the maximal initial value $X_0=2L+1$. We also analyze the closure conditions for periodic orbits, showing that nontrivial cycles are severely constrained, which supports the plausibility of the statistical framework. Numerical validation for L ranging from ${10}^5$ to ${10}^{15}$ confirms the theory with relative errors below $3\%$, and a bias-corrected mean estimate reduces the error to ${10}^{-3}$–${10}^{-2}\%$. These results establish a quantitative link between the arithmetic properties of the Collatz map and Gamma-type statistics, and suggest possible extensions to generalized Collatz-type problems. Full article