Search Results (326)

Against the backdrop of the “dual carbon” goals, carbon emission trading prices serve as a core signal of market operational efficiency. Accurately predicting carbon prices facilitates scientific decision-making, and model optimization is key to improving prediction accuracy. This study takes five major carbon trading pilots in China—Shenzhen, Guangdong, Hubei, Beijing, and Shanghai—as the research objects. An indicator system is constructed from four dimensions: macroeconomy, energy prices, climate and environment, and international markets. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm is employed to identify the key influencing factors of carbon prices across different markets. Among them, “WTI crude oil price” and “EUA futures closing price” are consistently significant factors common to all five pilots. On this basis, four models—Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Transformer—are constructed for multi-method prediction comparison. The results show that ARIMAX and GRU achieve the best prediction performance among the four models. To further enhance prediction accuracy, hybrid optimization models are respectively developed: Support Vector Regression (SVR) is used to optimize the nonlinear residuals of ARIMAX (SVR-ARIMAX), and Genetic Algorithm (GA) is used to optimize the key hyperparameters of GRU (GA-GRU). The hybrid models significantly reduce prediction errors in most markets. Specifically, SVR-ARIMAX shows particularly notable improvements in Beijing and Hubei, while GA-GRU outperforms standard GRU in Guangdong, Shenzhen, Shanghai, and Hubei. Based on the optimized models, 12-month-ahead forecasts indicate that the Shenzhen market exhibits high volatility and greatest uncertainty; Guangdong remains relatively stable; Hubei, Beijing, and Shanghai are characterized by narrow-range fluctuations. The findings provide empirical support for corporate emission reduction decision-making, carbon market risk management, and price mechanism improvement. Full article

Ceramic thermal protective coatings during long-term service in high-temperature environments are prone to micropore shrinkage, grain coarsening, and porous structure collapse, leading to severe densification. This consequently degrades the durability and reliability of the ceramic coatings. This paper elucidates the sintering densification mechanism of ceramic coatings, analyzes innovations in material systems and multidimensional structural design strategies, and summarizes the state-of-the-art research progress on anti-sintering densification of ceramic coatings. The limitations of conventional techniques that inhibit high-temperature sintering densification via “passive pore retention” are highlighted. A novel strategy based on phase transformation-induced pore formation for achieving “active in situ pore generation” is explored. On this basis, future research directions for enhancing the anti-sintering densification performance of ceramic thermal protective coatings are proposed. Full article

Depopulation and urban–rural population redistribution are challenges that reshape settlement patterns, landscapes, and local economies in many regions, from Europe to China and from Japan to North America. This study examines spatial and demographic transformations in the Baltic States (Europe), using Lithuania as a detailed case study. The analysis is based on high-resolution GIS population data derived from official population registers and linked to georeferenced settlement polygons for the years 2011 and 2021, combined with a linear projection of population change to 2026 (five-year period). The results reveal that population decline, which appears modest at the aggregated statistical level (approximately −1.1% to −1.5% per year), is territorially concentrated and reaches 45–48% in the most affected areas, which can only be identified through fine-scale spatial analysis. The most pronounced decline (−46%) is observed in the population of detached rural dwellings between 2011 and 2021, with trend-based estimation indicating that vacant rural houses may exceed 50% by 2026. At the same time, peri-urban zones surrounding the largest cities show clear population growth, largely driven by internal migration from ageing urban districts, smaller towns, and peripheral rural areas, compensating aggregated values and masking underlying processes. The findings reveal a dual process of rural shrinkage and suburban expansion, increasing pressures on territorial cohesion, service provision, infrastructure planning, and the preservation of cultural landscapes. The application of high-resolution spatial data allows the detection of localized demographic processes that remain insufficiently captured in conventional municipality-level statistics and that have rarely been analyzed at this level of spatial detail. Based on these results, this study emphasizes policy approaches such as adaptive rural regeneration and managed shrinkage. Although the empirical analysis is focused on Lithuania, the identified trends are relevant to many shrinking regions worldwide and may be reproduced using local population register data in other countries to support evidence-based regional planning. Full article

Background and Objectives: Transcatheter edge-to-edge repair (TEER) has emerged as an effective treatment option for patients with severe mitral regurgitation who are at high surgical risk. However, clinical outcomes after TEER remain heterogeneous and are influenced not only by cardiac parameters but also by systemic comorbidities and multiorgan dysfunction. The albumin–bilirubin (ALBI) score, derived from serum albumin and bilirubin levels, has recently been proposed as a simple marker of hepatic dysfunction and cardio-hepatic interaction. This study aimed to evaluate the prognostic value of baseline ALBI score in predicting long-term mortality after TEER. Materials and Methods: In this single-center retrospective cohort study, 106 consecutive patients with symptomatic moderate-to-severe or severe mitral regurgitation who underwent TEER between January 2019 and December 2025 were included. Baseline ALBI score was calculated using pre-procedural serum albumin and bilirubin levels. Cox proportional hazards regression analysis was used to identify predictors of long-term mortality. Variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression, followed by ridge-penalized multivariable Cox modeling to minimize overfitting. The incremental prognostic value of ALBI was assessed using concordance index (C-index) comparison between predictive models. Receiver operating characteristic (ROC) analysis and Kaplan–Meier survival analysis were also performed. Results: During a median follow-up of 17.9 months, 30 patients (28.3%) died. Higher baseline ALBI scores were significantly associated with increased mortality risk. In multivariable analysis, ALBI score (HR 3.35, 95% CI 1.46–7.71; p = 0.004), left atrial volume index (LAVI) (HR 1.02, 95% CI 1.01–1.03; p = 0.005), and log-transformed B-type natriuretic peptide (BNP) (HR 1.37, 95% CI 1.02–1.86; p = 0.039) remained independent predictors of mortality. Addition of the ALBI score improved model discrimination, increasing the C-index from 0.845 to 0.886. ROC analysis demonstrated good predictive performance of the ALBI score (area under the curve [AUC] = 0.877), with an optimal cut-off value of −1.67. Conclusions: Baseline ALBI score is independently associated with long-term mortality after TEER and may provide potential incremental prognostic information. However, the observed improvement is modest and should be interpreted cautiously. These findings support a potential role of ALBI as a complementary marker, which requires validation in larger prospective studies. Full article

The growing demand for long-distance green methanol transportation highlights the critical need to evaluate the safety and reliability of pipeline sealing materials. This study investigates the swelling mechanisms of fluorocarbon rubber (FKM), nitrile butadiene rubber (NBR), and polytetrafluoroethylene (PTFE) under simulated methanol pipeline conditions. Static immersion tests were conducted under simulated pipeline conditions with water contents of 0–20% and temperatures of 25–55 °C, supplemented by thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and gas chromatography–mass spectrometry (GC–MS). FKM exhibited severe physical swelling, with the volume increase reaching up to 80% in pure methanol. Notably, the addition of 5% water markedly suppressed this swelling, reducing the volume change of FKM sealing rings to approximately 3% and the mass change to 1%. Conversely, NBR experienced volume shrinkage and mass loss due to the extraction of the plasticizer Bis(2-ethylhexyl) phthalate by methanol, a process also inhibited by water. PTFE demonstrated exceptional chemical stability and negligible dimensional changes owing to its high crystallinity and rigid structure. Consequently, PTFE is recommended as the optimal sealing material for pure methanol pipelines. When utilizing FKM or NBR, strict control over the fluid’s water content and operating temperature is essential to prevent degradation and ensure long-term pipeline integrity. Full article

The performance of fluid solidified soil (FSS) depends on the curing agents as well as, to a great extent, the soil type. Currently, most studies focus on a single type of soil, which limits the applicability of research findings to practical engineering scenarios involving diverse soil conditions. To address this issue, this study selects two representative soil types—clay (CL) and silt (ML)—and employs alkali-activated red mud–slag as curing agent to prepare FSS. Laboratory experiments were conducted to evaluate the influence of soil type on the engineering properties and durability of the specimens. Specifically, the effects of soil type on flowability and unconfined compressive strength were comparatively analyzed. Durability was assessed through shrinkage, water stability and wet–dry cycle tests. Furthermore, X-ray diffraction, Thermogravimetric, Fourier transform infrared spectroscopy, field emission scanning electron microscopy and Brunauer–Emmett–Teller were utilized to characterize the microstructure and hydration products of the samples. The results indicate that an increasing proportion of ML leads to a decrease in overall flowability but a significant enhancement in late-age unconfined compressive strength. Meanwhile, the drying shrinkage of ML is gradually reduced, and both water stability and resistance to wet–dry cycles are correspondingly improved. Microstructural analyses reveal that the primary hydration product across all samples is C-(A)-S-H gel. Samples with higher ML content exhibit a denser structure and an increased volume of hydration products, which is consistent with the observed macroscopic performance trends. Full article

Certain clayey soils are susceptible to swelling and shrinkage due to moisture variations, which can lead to ground deformation and structural damage. Although traditional stabilization methods using lime and cement are effective, they involve high energy consumption and significant CO₂ emissions. In response to sustainability concerns, this study investigates the potential of in situ geopolymer stabilization of clay soils using industrial by-products as eco-friendly binders. Experimental studies were conducted on clay specimens stabilized with geopolymer binders produced from fly ash and waste brick powder activated by alkaline solutions. The selected clay exhibited stiff to very stiff behavior and was used as a reference material to ensure reliable evaluation without the influence of severe initial degradation. Reference samples with identical water content but without alkaline activation were also prepared. The primary objective was to assess geopolymers as a sustainable alternative to conventional binders, focusing on moisture sensitivity and long-term mechanical performance. Laboratory strength tests demonstrated that geopolymer-treated specimens exhibited significantly higher strength compared to untreated samples, indicating substantial improvement in engineering properties. Furthermore, Scanning Electron Microscopy (SEM) analyses revealed that the combination of dual activators (NS+NH) and thermal curing at 85 °C transformed the weak clay matrix into a dense, fibrous geopolymer network. However, the high curing temperature was primarily used to study the reaction mechanisms; the practical applicability of the method should be evaluated based on results obtained at ambient temperature. This structure enhanced particle bonding and mechanical interlocking by filling voids within the matrix. Overall, the findings confirm that geopolymer stabilization using industrial waste materials is an effective and environmentally sustainable alternative to conventional soil stabilization techniques, contributing to reduced carbon emissions in geotechnical engineering. Full article

Dental restorative resins available today still have limitations that may affect their durability. This study explores reinforcing a universal microhybrid dental composite resin with organomodified nanoclay at low filler loadings (0, 0.5, 1, 3, and 5 wt%). The morphology, structural features, and light transmittance of the composites were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), attenuated total reflection–Fourier transform infrared (ATR–FTIR), and UV–Vis spectroscopy. The degree of conversion and polymerization shrinkage were measured with ATR–FTIR and a linear variable displacement transducer (LVDT). Water sorption and solubility parameters and flexural properties were assessed gravimetrically and with a dynamometer, respectively. The composites mainly showed exfoliated structures and an improved degree of conversion. Polymerization shrinkage and solubility were lower than those of unmodified dental resin. The highest degree of conversion was observed in composites with 0.5–1 wt% nanoclay. The incorporation of 1 wt% nanoclay resulted in the lowest shrinkage and sorption, along with the highest flexural modulus and strength. Overall, the results suggest that low nanoclay concentrations can improve the physicochemical and mechanical properties of dental composites, highlighting their potential to develop advanced restorative materials that can address current clinical challenges. Full article

The paper deals with the concept of how to regulate structure formation in the interfacial transition zone (ITZ) between the Ordinary Portland Cement (OPC) matrix and basalt to ensure the durability of basalt fiber-reinforced concretes. It has been demonstrated that the alkali–silica reaction (ASR) can be transformed from a destructive (negative) process into a constructive one in OPC concrete through activation by sodium water glass combined with the incorporation of an Al₂O₃-containing additive, namely metakaolin. Alkaline activation increased the compressive strength of OPC basalt fiber-reinforced concrete by 1.6–1.9 times. The formation of stable zeolite-like hydration products within the Na₂O-CaO-Al₂O₃-SiO₂-H₂O system promoted self-healing of the ITZ. This resulted in a 5.6-fold increase in ITZ microhardness compared to the cement matrix, as well as transforming expansion into shrinkage of concrete with a final value of 0.01 mm/m after 360 days. The structure-forming processes in the ITZ ensured a 1.14-fold increase in the compressive strength of 180-day alkali-activated OPC basalt fiber-reinforced concrete compared to its 30-day strength, in contrast to a 0.92-fold decrease in the strength of the non-modified OPC analog under conditions accelerating the development of ASR. Full article

Quantitative trading systems require predictive models that simultaneously deliver accurate forecasts, calibrated uncertainty quantification, and actionable risk measures. This paper proposes an information-theoretic semiparametric regression framework combining a convolutional neural network–Transformer (CNN–Transformer) network for nonlinear temporal dependencies with a Gaussian process (GP) prior for residual autocorrelation and calibrated predictive distributions. Three theoretical results are established: an identifiability theorem guarantees joint recoverability of the nonparametric and GP components; a consistency theorem showing that the penalised maximum likelihood estimator converges at a rate $n^{-1/(2+d_{\mathrm{eff}})}$; and a coverage theorem proving asymptotic nominal coverage of the GP’s credible intervals. The framework enables an entropy-regulated trading module where predictive differential entropy informs position sizing via an uncertainty-penalised Kelly criterion, Kullback–Leibler divergence quantifies model uncertainty, and CVaR-constrained optimisation controls the tail risk. Simulations show the method outperforms the CNN, long short-term memory (LSTM), Transformer, XGBoost, random forest, least absolute shrinkage and selection operator (LASSO), and standard GP regression approaches. Backtesting on four Chinese A-share stocks yielded annualised returns of 15.9–22.4% with Sharpe ratios of 0.49–0.62, maximum drawdowns below 15%, and daily 95% CVaR reductions of 28–31% relative to a full-Kelly baseline, confirming both predictive accuracy and risk management effectiveness. Full article

Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming the modeling of complex spatiotemporal urban processes such as urban growth, sprawl, shrinkage, redevelopment, and Land Use/Land Cover Change (LULCC). However, despite rapid methodological innovation, applications remain fragmented, and there is limited synthesis of how AI-based models complement, extend, or supersede conventional approaches. This study addresses this gap through a systematic review of 6356 records, from which 120 articles were selected for detailed analysis. It investigates: (i) how ML/DL techniques are embedded within spatiotemporal modeling frameworks; (ii) their use in simulating urbanization dynamics and land-use (LU) transitions; (iii) methodological and performance gains relative to traditional statistical and rule-based models; and (iv) emerging research frontiers and limitations. The review shows that LULCC dominates current applications, with Artificial Neural Networks (ANNs) as the most prevalent ML method, increasingly complemented by DL architectures. Across cases, AI is primarily used to learn non-linear transition dynamics, represent spatial and temporal dependencies, identify influential drivers, and improve classification performance and computational efficiency. Building on these insights, the paper synthesizes the roles of AI in spatiotemporal urban modeling and outlines forward-looking research directions to support more robust, transparent, and policy-relevant applications for urban sustainability. Full article

Lignin is an abundant aromatic biopolymer, but its conversion into high-performance fibers remains challenging due to intrinsically poor spinnability, structural heterogeneity, and inefficient stress transfer in lignin-rich systems. In this study, a processing and structure strategy is demonstrated to overcome these limitations by transforming industrial black-liquor kraft lignin into a spinnable and load-bearing fiber component. Kraft lignin recovered from black-liquor waste was extracted and subsequently purified using a hot-water treatment to remove inorganic impurities and thermally unstable fractions, increasing lignin purity to 95.9% through extensive deionized water purification using a water-to-lignin ratio of 300:1. The purified lignin was then blended with poly(vinyl alcohol) (PVA), wet-spun into continuous filaments, and subjected to post-spinning hot drawing to induce molecular orientation. This sequential extraction, purification, blending, spinning, and drawing approach enables stable wet spinning and the continuous formation of lignin-rich lignin/PVA filaments without filament breakage, directly addressing the primary processing bottleneck of lignin-based fibers. Molecular-level miscibility between lignin and PVA is confirmed by the presence of a single glass transition temperature at 88.3 °C, indicating the formation of a homogeneous amorphous phase. SEM observations reveal composition-dependent surface roughness and non-circular cross-sectional morphologies arising from differential coagulation and shrinkage, demonstrating that lignin actively participates in the load-bearing fiber network rather than acting as a passive filler. As a result of purification-enabled spinnability, true blend miscibility, and post-spinning hot drawing, fibers with a lignin-to-PVA composition of 40:60 achieve a maximum tensile strength of 2.8 GPa, approaching the performance range of commercial high-strength polymer fibers. This work establishes a clear relationship between material structure, processing strategy, and resulting properties, highlighting the potential of industrial lignin waste as a sustainable precursor for advanced fiber applications. Full article

Background: To investigate the association between the uric acid-to-high-density lipoprotein cholesterol ratio (UAHDLr) and the risk of new-onset atrial fibrillation (NOAF) during hospitalization in patients with non-ST-elevation myocardial infarction (NSTEMI). Methods: This retrospective cohort study included NSTEMI patients without prior atrial fibrillation. UAHDLr was log2-transformed to evaluate the effect of doubling. Feature selection was performed using least absolute shrinkage and selection operator regression. Cox proportional hazards models with sequential adjustments were applied. Nonlinear associations were assessed using restricted cubic spline analysis, and discrimination was evaluated with time-dependent receiver operating characteristic analysis. Results: UAHDLr was independently associated with in-hospital NOAF across all models. Doubling (1-unit increase) UAHDLr significantly increased NOAF risk in unadjusted (HR: 5.97, 95% CI: 3.71–9.61) and clinically adjusted models (HR: 5.98, 95% CI: 3.58–9.99). The association was stronger in the LASSO-adjusted (HR: 8.19, 95% CI: 4.13–16.24) and fully adjusted models (HR: 13.40, 95% CI: 5.90–30.44). Spline analysis showed a progressive increase in NOAF risk with higher UAHDLr values. Discrimination was stable, with an area under the curve of approximately 0.76. Conclusions: UAHDLr is a strong, easily accessible biomarker for predicting in-hospital NOAF in NSTEMI patients and may support early risk stratification. Full article

Chaotic signals commonly exhibit nonlinear and nonstationary characteristics, while noise contamination reduces signal interpretability and degrades subsequent feature extraction and dynamical analysis. To improve the stability of mode-boundary determination and mitigate reconstruction distortion, this paper proposes a hybrid denoising framework that integrates feature mode decomposition (FMD), amplitude-aware permutation entropy (AAPE), dual-tree complex wavelet transform (DTCWT), and Savitzky–Golay (SG) filtering. First, the noisy signal is decomposed into multiple mode components using FMD. Then, the AAPE of each mode is calculated to adaptively distinguish high-frequency noise-dominant modes from non-high-frequency modes. For the high-frequency noise-dominant modes, improved logarithmic threshold shrinkage is applied to the magnitudes of DTCWT complex coefficients to suppress random noise and reduce threshold-induced bias. For the non-high-frequency modes, SG filtering is employed to further attenuate residual noise while preserving local waveform structures. Finally, the processed modes are reconstructed to obtain the denoised signal. Experiments on a simulated Lorenz chaotic signal and a real-world sunspot time series demonstrate that, across different noise levels, AAPE provides more stable mode partitioning than ApEn, CC, and CMSE. Moreover, under Gaussian white noise, Poisson noise, and uniform noise, the proposed method generally achieves a higher output signal-to-noise ratio (SNR) and a lower root mean square error (RMSE) than WT, CEEMD, EEMD, CEEMDAN+LMS, and VMD, while also yielding better performance in phase-space reconstruction and temporal-detail recovery. These results verify the effectiveness and practical applicability of the proposed method for chaotic signal denoising. Full article

Urban shrinkage has become a critical constraint on China’s pursuit of high-quality economic development. As a core driver of new-quality productive forces, digital technological innovation warrants systematic examination for its role in mitigating urban shrinkage. Given the current lack of research on multidimensional measures of urban shrinkage and the mechanisms through which digital technologies influence this phenomenon, this study utilizes panel data from 269 prefecture-level and higher cities in China from 2014 to 2022. By employing two-way fixed-effects models, mediation models, and threshold regression models, the study systematically examines the impact, mechanisms, and nonlinear characteristics of digital technology innovation on urban shrinkage. The empirical results demonstrate that digital technological innovation has a significant mitigating effect on urban shrinkage; this conclusion holds even after conducting a series of robustness tests, including replacing the core explanatory variable, accounting for lag effects, using SYS-GMM estimation, and adjusting the sample range. Heterogeneity analysis indicates that the mitigating effect is more pronounced in shrinking cities, peripheral cities, resource-based cities, and cities with lower educational attainment. Mechanism analysis reveals that agricultural-related innovation acts as a mediating channel, whereas rural entrepreneurship exhibits a “partial masking effect” in the relationship between digital technological innovation and urban shrinkage. Moderation analysis further shows that higher levels of ecological environmental resilience amplify the inhibitory effect of digital technological innovation. Finally, threshold regression results identify a significant double-threshold effect, with the mitigating impact of digital technological innovation emerging only after exceeding the first threshold value of 5.690. Based on these findings, this study recommends implementing differentiated digital-technology-driven innovation strategies, with agriculture-related innovation serving as a strategic entry point to stimulate regional innovation and entrepreneurial vitality. At the same time, strengthening ecological resilience should be promoted to support coordinated green and digital transformation. These findings provide empirical evidence for the formulation of differentiated urban digital transformation policies aimed at mitigating urban shrinkage. Full article