Search Results (6,527)

Achieving sustainable transport requires not only reducing aggregate emissions but also avoiding the concentration of emission burdens and transition costs in less-developed regions. Transport carbon-emission inequality therefore captures a distributional dimension of environmental and social sustainability. Using city-level transport emissions and population data for 31 mainland Chinese provincial-level regions from 2005 to 2024, this study develops an integrated framework for measuring and monitoring this inequality. Population-weighted within-province Gini coefficients and Theil T indices are combined with a modified-gravity interprovincial network, annual network analysis, CONCOR and core–periphery analysis, two-way fixed effects, and leakage-safe machine learning. The mean provincial Gini declined from 0.311 in 2005 to 0.287 in 2024, but inequality remained higher in the northwest and southwest and lower along the eastern coast. Full-network weight increased from 61.275 to 3336.040, retained directed edges increased from 174 to 217, and reciprocity rose from 0.471 to 0.571, indicating stronger interprovincial embeddedness and a greater need for coordinated governance. Population density was negatively associated with inequality across several specifications, although wild-cluster bootstrap inference did not confirm uniform significance. Under zero province-pair overlap, LightGBM achieved R² = 0.241 with distance and 0.121 without distance; residual machine learning added no stable predictive information beyond the gravity benchmark. These findings contribute to sustainability research by quantifying the distributional fairness of transport decarbonization, providing a reproducible monitoring tool for identifying regional sustainability risks, and supporting differentiated mitigation responsibilities, sustainable transport infrastructure, and cross-provincial coordination. Full article

Coupling-coordination degree (CCD) indices are often interpreted as evidence that urban systems are developing in a mutually supportive manner. The standard formula, however, combines similarity between subsystem scores with their average level. This article evaluates those two components against the following external outcome: municipal environmental services. A city panel for China in 2002–2024 combines broadband and mobile adoption, urban road provision, licensed internet data-center records, and seven indicators of water, gas, drainage, sewage, waste, and urban greening. The preferred sample contains 6094 observations for 275 cities in 28 provinces. We estimate city fixed-effect models with province-by-year fixed effects and report province-clustered, two-way-clustered, and 9999-repetition wild-cluster-bootstrap inference. In the baseline rank-based construction, a one-standard-deviation increase in relative digital-transport alignment is associated with a 0.046-standard-deviation increase in the environmental-service index (wild-bootstrap p = 0.042), whereas the portfolio-level coefficient is 0.191 (p < 0.001). The alignment estimate is concentrated in water and gas access and is not robust to complete-component outcomes, PCA or entropy weighting, logarithmic standardization, road density, or lags beyond one year. Portfolio level remains positive across these tests, although lead and reverse-direction estimates preclude causal interpretation. The findings show why infrastructure scale, relative configuration, and realized service outcomes should be monitored separately. For SDGs 9 and 11, city governments should target documented service bottlenecks and operating capacity rather than maximize a composite coordination score. Full article

Hydrogenation reactors are critical equipment in the petrochemical industry, yet their material degradation is governed by coupled multi-mechanism damage. Current assessment practices largely neglect this complexity, remaining single-factor oriented and overlooking synergistic interactions and temporal evolution. This paper proposes a regionally differentiated, multi-level framework integrating 5 primary and 17 secondary indicators with a hybrid AHP-EWM weighting strategy that synthesizes expert knowledge and measured data. A multi-factor coupling correction coefficient is introduced to provide a preliminary estimate of the synergistic acceleration effect among damage mechanisms, while a GM(1,1) gray model enables dynamic trend prediction. Applied to a 25-year 2.25Cr-1Mo steel reactor, the method produces regional degradation values of 0.378, 0.607, and 0.533 for the base metal, welds, and cladding layer, respectively, with an overall baseline of 0.453 rising by 11% to 0.503 after coupling correction. Compared with exponential regression, ARIMA, and BP neural networks, GM(1,1) is selected for its balanced performance in small-sample fitting, extrapolation stability, and physical interpretability. Sensitivity analysis confirms stable degradation grading even with ±50% coupling coefficient variations. The proposed approach mitigates the underestimation inherent in conventional single-mechanism assessments and offers a quantitative tool for full-lifecycle risk management and predictive maintenance of hydrogenation reactors. Full article

In the domain of rotating machinery fault diagnosis, challenges such as multi-operating condition distribution heterogeneity and the difficulty of distinguishing fault features within multi-scale temporal signals persist. To address these issues, this paper introduces the Time-Gated Multi-Expert Generative Adversarial Network (TGME-GAN), a fault diagnosis approach that integrates a multi-expert gated conditional generative adversarial network with a clustering structure-aware feature enhancement. This method combines unsupervised K-means clustering with supervised discriminative learning. The optimal number of clusters is selected adaptively using the silhouette coefficient, and the distance vector from each sample to the cluster centers serves as a topological prior feature. A spatial–temporal joint representation matrix is then formed by concatenating PCA principal components, differential features, cumulative statistical features, and standardized change rates, which together capture both abrupt mutations and progressive degradation in fault signals. In the model, the discriminator incorporates a multi-expert gated network. Each expert learns a feature subspace corresponding to a distinct operating condition, and the gated network dynamically assigns fusion weights, allowing the discriminator to capture heterogeneous distributions across industrial conditions. The generator extracts multi-scale local patterns with a three-layer one-dimensional convolutional network and models sequential dependencies with a two-layer LSTM, producing high-quality fault samples that preserve intrinsic consistency. At the engineering level, TGME-GAN is deployed for gearbox fault diagnosis in uneven, small-sample industrial settings. In two gearbox fault experiments, this method substantially outperforms current mainstream models. Full article

Background: Polyendocrine Metabolic Ovarian Syndrome (PMOS), previously known as Polycystic Ovary Syndrome (PCOS), is one of the most common endocrine disorders affecting women of reproductive age and represents a major cause of infertility worldwide. It is associated with hormonal imbalance, ovulatory dysfunction, and metabolic disturbances, all of which can significantly impair reproductive outcomes and quality of life. We aimed to investigate the metabolic and hormonal markers in infertile women who had PMOS in one of the rapidly developing Middle Eastern countries. Methods: This was a retrospective observational cohort study conducted at the Military Medical Specialist Center in Qatar (2019–2024). Data were extracted from patient medical records, including demographic characteristics, clinical presentation, hormonal profiles, metabolic parameters, and details of fertility treatment. Women aged 18–40 years diagnosed with PMOS according to the Rotterdam criteria were included. Correlation coefficient analysis was performed to assess the associations between PMOS-related factors. Patients were categorized by BMI (normal, overweight, and obese). Results: The mean age of patients was 31.9 ± 5.3 years, and 43.8% of patients were obese. Primary infertility was more frequent than secondary infertility (61.8% vs. 38.2%). Women with secondary infertility were significantly older and had higher body mass index (BMI) (p = 0.001 and p = 0.01, respectively). Insulin resistance was prominent (mean Homeostatic Model Assessment for Insulin Resistance [HOMA-IR] of 4.04) and increased significantly with the increase in BMI (p = 0.01). BMI showed positive correlations with serum levels of glucose, insulin, HOMA-IR, and testosterone. Hormonal parameters were largely comparable between infertility groups, except for lower FSH levels in secondary infertility (p = 0.04). The proportion of PMOS based on the HOMA-IR category was 5.6% (HOMA-IR < 1.0), 23.4% (HOMA-IR 1–1.99), 20.2% (HOMA-IR 2–2.99), and 50.8% (HOMA-IR > 3.00). In PMOS patients, there was a significant association between obesity and HOMA-IR, with each 1-unit increase in HOMA-IR associated with a 14% increase in odds (crude odds ratio 1.14; 95% confidence interval 1.02–1.28, p = 0.02). Also, obesity was associated with low LH/FSH (crude odds ratio, 0.67; 95% CI, 0.45–0.99; p = 0.04). Results: The mean age of patients was 31.9 ± 5.3 years, and 43.8% of patients were obese. Primary infertility was more frequent than secondary infertility (61.8% vs. 38.2%). Insulin resistance was prominent (mean HOMA-IR 4.04) and increased significantly across BMI categories (3.0 in normal-weight vs. 4.8 in obese women, p = 0.01). BMI was positively correlated with HOMA-IR (r = 0.17, p = 0.02) and testosterone levels (r = 0.18, p = 0.01). Secondary infertility became more frequent with increasing BMI (p = 0.02). Each 1-unit increase in HOMA-IR was associated with a 14% increase in the odds of obesity (OR 1.14, 95% CI 1.02–1.28, p = 0.02). Conclusions: Among infertile women with PMOS, obesity and insulin resistance were prominent metabolic characteristics. These findings support routine screening for insulin resistance, particularly in overweight and obese women, together with weight management and individualized fertility treatment based on BMI and metabolic profiles. Full article

In multivariate statistical analysis, accurate modeling of the covariance structure is critical for high-dimensional data analysis, variable selection, and regularization. In high-dimensional settings, strong inter-variable correlation and redundancy are key factors limiting the performance of classical sparsity-based methods. While LASSO and its variants provide effective tools for coefficient shrinkage and variable selection, they may select redundant variables and produce unnecessarily complex models in highly correlated settings. In this study, a Correlation-Sensitive Adaptive LASSO (CDA-LASSO) method is proposed to address these limitations. The proposed approach is based on a hybrid weighting mechanism that makes the penalty term sensitive not only to initial coefficient magnitudes but also to the correlation structure between variables. This structure incorporates correlation-based redundancy information and imposes stronger penalties on predictors with higher directed redundancy scores. Under fixed-dimensional regularity conditions, the bounded correlation multiplier is shown to preserve the selection consistency and oracle limiting distribution of Adaptive LASSO. The method was evaluated through 14 high-dimensional simulation scenarios covering different sample sizes, dimensionalities, sparsity levels, correlation strengths, support structures, and normal or heavy-tailed errors. The results indicate that the Max and kMean variants generally reduce the false discovery rate and model size relative to LASSO and Elastic Net while maintaining broadly comparable predictive performance. Numerical improvements over Adaptive LASSO were also observed in several scenarios, although these differences were not uniformly statistically significant. Under very high correlation, reductions in false discoveries were sometimes accompanied by modest decreases in the true positive rate. The real-world Riboflavin analysis further showed that the CDA-LASSO variants produced smaller models than LASSO and Elastic Net while retaining comparable prediction errors. Overall, CDA-LASSO directly incorporates the internal correlation structure of the data into the penalty weights without requiring a predefined graphical structure and provides a practical methodological extension for more controlled and parsimonious variable selection in high-dimensional correlated settings. Full article

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

As the primary carrier for oil and gas transportation, pipelines are critical for the entire industry. Pipelines are continuously subjected to corrosion and abrasion in the oil and gas delivery process, leading to gradual wall thickness reduction, shortened service life, and deteriorated operational safety. Ultrasonic testing has been widely adopted for monitoring pipeline wall thickness. Conventional ultrasonic transducers possess rigid configurations, which hinder large-area inspection and exhibit poor adaptability to complex curved components. In contrast, flexible ultrasonic sensors show prominent advantages, with their small size, light weight, and excellent conformal contact with curved surfaces. Flexible piezoelectric thin-film sensors have been used in a wide range of fields. As one of the most representative piezoelectric polymers, poly(vinylidene fluoride–trifluoroethylene) (P(VDF-TrFE)) combines favorable piezoelectric coefficients and intrinsic flexibility, making it popular. Some research groups have investigated the influences of modified filler particles, doping ratios, and fabrication process optimization on the performance of P(VDF-TrFE)-based piezoelectric composites, while others have concentrated on the practical applications of existing flexible piezoelectric sensors. This study emphasizes a rapid customized fabrication strategy for flexible sensors instead of single-specification standardized probes; hence, it does not share the same comparison benchmark as conventional fixed-dimension sensors. Systematic research on flexible piezoelectric thin-film sensors is presented, including piezoelectric material modification, substrate design, laminated structural design, fabrication workflows, establishment of the testing platform, and the development of matched circuit systems. The material preparation and manufacturing processes are optimized, and a scalable technical route for fabricating flexible piezoelectric sensors is proposed. Using this route, flexible piezoelectric thin-film sensors can be rapidly tailored for different application scenarios to satisfy diverse engineering demands. Multiple experiments were conducted on pipeline samples with varying wall thicknesses and curvatures. The results verify that the sensor reaches a measurement precision of 0.01 mm, meeting the demands of high-precision pipeline structural health monitoring. Full article

By analyzing the semantic information of Direct Current (DC, the zeroth-order spherical harmonic coefficient) gradients during 3D Gaussian Splatting (3DGS) optimization, this paper achieves unsupervised state classification in scenes with discrete appearance states under the proposed State-Discovery Gaussian Splatting (SD-GS) framework via SVD dimensionality reduction and K-means clustering. To improve the stability of the clustering results, an appearance-difference-weighted refinement mechanism is further proposed to confirm high-confidence labels. To address the difficulty of distinguishing similar states when the number of states exceeds two, a sequential peeling strategy is proposed that decomposes a multi-class partition into several two-class separations. On four real-world scene datasets, SD-GS achieves 100% classification accuracy with reconstruction quality of 31.98–38.83 dB PSNR. Ablation studies validate the effectiveness of the gradient direction mode and the SVD dimensionality reduction strategy. Full article

This paper investigates the spatial asymptotic behaviour of solutions to a coupled thermoelastic system of Green–Naghdi Type III in an exterior domain of ${\mathbb{R}}^3$. The system couples elastodynamics with a second-order heat conduction law and contains indefinite cross-coupling terms between mechanical and thermal variables. By constructing a weighted energy functional and deriving a first-order differential inequality in the radial direction, we establish a Phragmén–Lindelöf alternative: for each fixed time, the total energy either grows exponentially or decays exponentially as $r\to \infty$, with an explicit decay rate that depends on the material coefficients and a free parameter $\omega$. This dichotomy itself reveals a fundamental symmetry in the spatial behaviour—growth versus decay—which is intimately linked to the radial symmetry of the exterior geometry and the inherent structure of the coupled system. The result provides a complete characterization of spatial stability for this thermoelastic model in unbounded exterior domains. Full article

Integrated sensing and communication (ISAC) systems are envisioned as a key enabler for next-generation wireless networks. To simultaneously achieve multi-function of communication and sensing for this system, a hybrid reconfigurable intelligent surface (RIS) comprising both active and passive reflecting elements is proposed. An index-wise importance score matrix and a factorized representation of complex reflection coefficients are optimized by introducing a communication-sensing controllable coefficient. A novel ISAC system is investigated, which exploits the low-power advantage of passive reflecting elements while retaining the signal amplification capability of active reflecting elements. In the multiple-input multiple-output (MIMO) communication networks, the RIS reflection coefficient matrix is adaptively optimized via a Riemannian Hessian-based method. At the same time, the transmit precoding matrix is obtained using an extended weighted minimum mean square error (WMMSE) and Lagrange multiplier methods. Compared with baseline schemes including active-only RIS, passive-only RIS, random-phase RIS, and non-RIS, the proposed scheme enables multi-mode adjustability of communication and sensing which can be easily transplanted in current ISAC system. Under the constraint of transmit power, it exhibits more robust performance on communication-sensing with varying number of RIS elements and different signal-to-noise ratios (SNRs). Full article

Water resource conservation and intensive utilization is an important pathway for promoting sustainable regional water resource management and high-quality development. Against the backdrop of increasing constraints on water resources, existing studies have paid insufficient attention to the multidimensional comprehensive assessment of water resource conservation and intensive utilization capacity and its underlying evolutionary mechanisms. Therefore, Heilongjiang Province was selected as the study area, and an evaluation system comprising 15 indicators was established. The game-theoretic combination weighting method, TOPSIS model, obstacle degree model, and GM(1,1) grey forecasting model were employed to comprehensively evaluate, diagnose obstacle factors, and predict the trend of water resource conservation and intensive utilization capacity in Heilongjiang Province from 2004 to 2023. The results showed that the overall capacity exhibited a fluctuating upward trend, with the comprehensive evaluation value increasing from 0.44 to 0.62. The industrial water reuse rate, effective utilization coefficient of farmland irrigation water, comprehensive water consumption rate, per capita water consumption, and ecological water use rate were the indicators with relatively high obstacle contributions. The obstacle factors exhibited distinct stage-specific characteristics: the constraining effects of efficiency-related indicators gradually weakened, whereas those of the comprehensive water consumption rate and per capita water consumption generally intensified, indicating that the factors constraining water resource conservation and intensive utilization in Heilongjiang Province underwent distinct stage-specific changes. The prediction results indicated that the capacity for water resource conservation and intensive utilization in Heilongjiang Province would continue to increase steadily in the future. However, balancing ecological water use requirements with growing water demand remains an important factor affecting sustainable water resource utilization. The evaluation–diagnosis–prediction framework developed in this study can provide a reference for the assessment and optimized management of regional water resource conservation and intensive utilization. Full article

High reclaimed asphalt pavement (RAP) contents are often limited by insufficient restoration of field-aged SBS-modified asphalt and the lack of a comprehensive method for rejuvenator dosage selection. This study aimed to develop a multi-performance-based approach for determining the dosage of an epoxy-based reactive rejuvenator under high-RAP conditions. Rejuvenated binders with different dosages were evaluated using conventional tests, DSR, MSCR, BBR, and LAS tests. Continuous low-temperature grading temperature, dissipated energy ratio, and entropy-weight TOPSIS were used for comprehensive evaluation, while GPC was employed to characterize molecular-weight distribution. The rejuvenator improved low-temperature relaxation, fatigue resistance, energy dissipation, and workability, whereas excessive dosages reduced rutting resistance and elastic recovery. Entropy-weight TOPSIS ranked RA-6 highest, with a relative closeness coefficient of 0.66504, and this ranking was consistent with the overall trends obtained from individual performance tests, supporting the feasibility of the proposed evaluation method. GPC results showed systematic changes in molecular-weight distribution after rejuvenation. For the investigated material system, 6% is recommended among the tested dosages. The proposed framework provides a practical basis for dosage determination when material characteristics and performance requirements vary. Full article

Continuous-wave mud pulse telemetry offers high spectral efficiency and transmission rates, making it an important technology for high-speed information transmission under complex well conditions. However, surface-received signals are highly susceptible to periodic pressure pulsations generated by mud pumps, which degrade phase extraction and symbol decision performance. Dual-pressure-sensor delayed differential processing can exploit the correlated propagation characteristics of pump noise between two measurement locations to suppress its correlated components; however, its performance depends on accurately matching the propagation delay and amplitude compensation coefficient. To specifically address the dynamic variation in the pump noise propagation relationship between two measurement locations under actual operating conditions, a joint delay–amplitude compensation method is developed, in which pump noise suppression is formulated as the joint estimation of the signal propagation delay and amplitude compensation coefficient. Built upon LMS-based time delay estimation, the proposed method employs an enhanced time-varying step-size LMS time delay estimation algorithm (HTVSS-LMSTDE) to improve dynamic retracking capability following changes in propagation delay. A sliding-window weighted least-squares method (SWLS) is further introduced to estimate the amplitude compensation coefficient and correct differential mismatch caused by variations in the amplitude transfer ratio. With non-pump interference modeled as additive white Gaussian noise independent of the telemetry signal and pump noise, simulation results demonstrate that, when the propagation delay and amplitude transfer ratio vary simultaneously, the proposed method yields delay estimates and amplitude compensation coefficients close to their theoretically optimal values. Field wellbore tests further verify that the proposed method effectively attenuates low-frequency pump noise interference in continuous-wave mud pulse telemetry signals while preserving the BPSK-modulated information. Full article

To address the mismatch between agricultural development and water resource conditions, this study examined 22 provinces and four municipalities in China over the period 2014–2023 and incorporated terrain factors into the analytical framework. An integrated evaluation system was developed for the Agricultural Development Index (ADI) and Water Resource Condition Index (WCI). The entropy weight method, coupling coordination degree model, standard deviation ellipse model, spatial difference coefficient, and two-way fixed-effects model were employed to characterize the spatiotemporal dynamics and coordination between agricultural development and water resource conditions. The results indicate that the ADI increased steadily throughout the study period, whereas the WCI remained relatively stable, reflecting distinct evolutionary trajectories of the two systems. Although the coordination level of the Agriculture–Water Resources System improved overall, pronounced regional disparities persisted. Plain regions exhibited stronger coordination, whereas mountainous and plateau regions showed lower coordination levels because of terrain constraints and limited resource endowments. Spatial mismatches between agricultural and water resource advantage regions were evident, although the overall matching relationship between the two systems gradually strengthened. The proposed evaluation framework provides a scientific basis for optimizing regional agricultural layouts, improving water resource allocation, and promoting coordinated agricultural and water resource development. Full article