Search Results (1,090)

To address the dual challenges of improving precast cable trench joint performance and promoting solid waste recycling for carbon neutrality, this study developed a jute fiber-reinforced recycled aggregate concrete (JFRAC) and established a complete technical chain via experiments and numerical simulations. Compressive strength tests were conducted on JFRAC with varying jute fiber volume content and recycled coarse aggregate (RCA) replacement ratio to obtain their influence on the stress–strain relationship. A modified Concrete Damaged Plasticity (CDP) model was proposed by introducing correction coefficients for compressive strength and elastic modulus, achieving over 95% agreement with experimental data. Finite element simulations of cable trench joints showed that JFRAC outperforms C30 concrete, with the same compressive strength, in ultimate bearing capacity (↑4.17%), peak displacement (↑18.78%), and ductility (↑14.66%). JFRAC provides substantial environmental and economic advantages by reducing carbon emissions by 2.29% and saving costs of CNY 62.43 per meter of precast cable trench. Parametric studies indicated bolt grade and number are the primary performance influencers. Bolt grade’s impact diminishes as it increases from 8.8 to 10.9, while bolt number linearly enhances load-bearing capacity. This study provides a feasible path for JFRAC to replace conventional concrete in cable trenches, realizing both economic and environmental benefits. Full article

The adaptive reuse of industrial heritage is increasingly recognized as an effective low-carbon strategy that reduces resource consumption, lowers embodied carbon emissions, and supports sustainable urban transitions. Developing appropriate reuse strategies, however, requires a robust understanding of heritage value. As material evidence of China’s modern industrialization, railway-associated industrial heritage possesses the characteristics of linear cultural heritage. Yet systematic and multi-scalar value assessments from a linear heritage perspective remain limited. Focusing on the Guanzhong Section of the Longhai Railway—one of the most representative industrial development axes in Northwest China—this study establishes a two-level value assessment framework and conducts a comprehensive evaluation of fourteen textile industrial heritage units. At the individual level, five dimensions—historical significance, architectural features, structural integrity, authenticity, and rarity—were assessed through field investigation, and type-specific weights were introduced to correct structural imbalances between quantity and value across building categories. At the unit level, the Analytic Hierarchy Process (AHP) was employed to determine the weights of spatial–functional integrity, process completeness, railway connectivity, industrial landscape characteristics, and the integrated individual-level value. The results show that factory workshops and warehouses consistently exhibit the highest value, whereas structures and residential buildings, despite their numerical dominance, contribute relatively little. Spatially, a clear west–east gradient emerges: high-value units cluster in Baoji and Xi’an, medium-value units in Xianyang, and low-value units mainly in Weinan and surrounding counties. The findings indicate that textile industrial heritage along the Guanzhong Section forms a railway-linked linear cultural heritage system rather than isolated sites. The proposed evaluation framework not only supports heritage identification and conservation planning but also provides a theoretical basis for promoting low-carbon adaptive reuse of existing industrial buildings. Full article

We compared scatter correction (SC) in single-photon emission computed tomography (SPECT) images using effective scatter source estimation (ESSE) and the triple-energy window (TEW) method. We acquired ^99mTc and ¹²³I images of brain, myocardial, and performance phantoms containing rods with different diameters. We assessed contrast ratios (CRs) and ROI-based noise metrics (coefficient of variation, signal-to-noise ratio, and contrast-to-noise ratio [CNR] ). Under ^99mTc, ESSE yielded higher CRs than TEW across all phantoms (mean difference 0.04, range 0.01–0.05) and produced the highest CNR in the myocardial phantom, improving the conspicuousness of the simulated defect. Under ¹²³I, CR differences between ESSE and TEW were small and inconsistent (performance phantom: −0.04 to 0.06; brain phantom: −0.01 to 0.00). A Monte Carlo simulation (point source in air) showed substantial photopeak window penetration for cardiac high-resolution collimators (40.0%) but low penetration for medium-energy general-purpose collimators (5.1%), supporting photopeak contamination as a contributor to the ¹²³I findings and potentially attenuating the apparent advantage of model-based SC that does not explicitly account for penetration photons. These findings suggest that SC selection should consider the radionuclide and imaging target and that ESSE might be a reasonable option for ^99mTc myocardial imaging under the settings examined. Full article

This study developed a multi-source data assimilation system based on the WRF-Chem model integrated with 3DVAR and EAKF methods. By assimilating a multi-source satellite fused XCO₂ concentration dataset, the system achieved simultaneous optimization of CO₂ concentration fields and emission fluxes over China. During the December 2019 experiment, the system successfully reconstructed high-precision CO₂ concentration fields and dynamically corrected the MEIC inventory through emission error inversion derived from concentration differences before and after assimilation. Comparative analysis with the EDGAR inventory demonstrated the superior performance of the EAKF method, which reduced RMSE by 56% and increased the correlation coefficient to 0.360, while the 3DVAR method achieved a 9% RMSE reduction and improved the correlation coefficient to 0.294. In terms of total emissions, 3DVAR and EAKF increased national emissions by 13.6% and 5.1%, respectively, but reduced emissions in Xinjiang by 3.24 MT and 7.99 MT. A comparison of three simulation scenarios (prior emissions, 3DVAR-optimized, and EAKF-optimized) showed significant improvement over the EGG4 dataset, with systematic bias decreasing by approximately 75% and RMSE reduced by about 49%. The assimilation algorithm developed in this study provides a reliable methodological support for regional carbon monitoring and can be extended to multi-pollutant emissions and high-resolution satellite data integration. Full article

Crack type classification in concrete structures is essential for assessing structural integrity, yet traditional visual inspections and RA–AF parameter-based Acoustic Emission (AE) methods suffer from subjectivity and limited ability to capture temporal signal dependencies. This study proposes TemporalAE-Net, a self-attention-based machine learning framework designed to classify tensile and shear cracks while explicitly incorporating the temporal evolution of AE signals. AE data were collected from axial tension tests, shear-failure tests, and four-point bending tests on reinforced concrete beams, and a sliding-window reconstruction method was used to transform sequential AE signals into two-dimensional temporal matrices. TemporalAE-Net integrates one-dimensional convolution for local feature extraction and multi-head self-attention for global temporal correlation learning, followed by multilayer perceptron classification. The proposed model achieved an accuracy of 99.72%, outperforming both its ablated variants without convolutional or attention modules and conventional time-series architectures. Generalization tests on 12 unseen specimens yielded 100% correct classifications, and predictions for reinforced concrete beams closely matched established crack-evolution patterns, with shear cracks detected approximately 15 s prior to visual observation. These results demonstrate that TemporalAE-Net effectively captures temporal dependencies in AE signals. Moreover, it provides accurate and efficient tensile–shear crack identification, making it suitable for real-time structural health monitoring applications. Full article

While domestic green finance is widely recognized for its role in fostering green innovation and supporting climate change mitigation, the impact of international green finance (IGF) remains critical, particularly for developing economies where external finance inflows can catalyse transitions toward low-carbon development. This study investigates the long-run and short-run effects of IGF on green innovation and further examines the influence of green innovation on carbon dioxide (CO₂) emissions across a panel of 76 developing countries from 2000 to 2019. Using second-generation panel cointegration and the vector error correction mechanism, our findings reveal a nonlinear long-run relationship between IGF and total innovation, indicating that IGF must exceed a threshold before significantly boosting total innovation in developing economies. We also identify an inverted U-shaped relationship between IGF and green innovation, in which the positive effects of IGF diminish beyond a certain point. Crucially, IGF emerges as a significant driver of CO₂ emissions reduction in both the short- and long-run. While total innovation is associated with increased emissions over the long term, green innovation contributes to a substantial and sustained decrease in CO₂ emissions. These results emphasize the need to design targeted policies that prioritize green innovation and scale up IGF to support sustainable growth in developing countries. Full article

Mining and sewage treatment wastes have been accumulating at growing rates in urban areas. Recycling these wastes can be used to generate safe products for various agro-environmental uses, including the synthesis of fertilizers with the potential to sequester carbon (C) in the soil. Therefore, this study evaluated the physicochemical characteristics and C sequestration potential of biochar obtained by co-pyrolysis (500 °C) of sewage sludge (SS) individually or combined at a 1:1 (w:w) ratio with zeolitic basalt (ZB), referred to as SS + ZB_BC. Subsequently, the raw materials and biochars were characterized by X-ray diffraction analysis, proximate analysis, elemental analysis, and FTIR spectroscopy, as well as pH and electrical conductivity (EC) determination. The results show that pyrolysis optimized material properties, especially SS biochar (SSB), which exhibited high stability with the highest fixed C content (13.6%) and thermostable fraction (TSF) of 43%. On the other hand, ZB had a higher pH and a lower EC than SS. Co-pyrolysis promoted complementary effects on the chemical and C stability properties of the SS + ZB_BC combination. The combination raised the pH to a value close to neutrality (6.5), indicating potential corrective action for acidic soils. Furthermore, after co-pyrolysis, the TSF remained high (25.2%) and was classified as a high-longevity material (>1000 years), indicating high aromaticity and C condensation. Therefore, the co-pyrolysis of SS and ZB optimized the individual characteristics of the materials, thereby providing a promising and sustainable alternative for agro-environmental use that addresses the need to reduce C emissions and promote waste recycling. Full article

On-road vehicles are a primary source of urban air pollution. It is known that high-emitting vehicles represent a fraction of the fleet but contribute significantly to the total emissions. Usually, road transportation emission inventories do not capture the impact of these types of vehicles, underestimating emissions. This study introduces a simple method to refine vehicle emission inventories by incorporating data from Ecuador’s Inspection and Maintenance (I/M) program. We analyzed I/M data from Quito to develop a correction factor for the Vehicular Emissions INventory (VEIN) model, accounting for the higher emissions from vehicles that fail inspection. Our analysis shows that while less than 10% of gasoline and 20% of diesel vehicles failed inspection, their emissions were substantially higher; for instance, accounting for reproved vehicles produced 60% more Carbon Monoxide (CO), 18% more Non-Methanic Volatile Organic Compounds (NMVOC), 40% more Particulate Matter with aerodynamical diameter of 2.5 µm or less (PM_2.5), and 34% more or lower than 10 µm (PM₁₀). These findings demonstrate that incorporating I/M data is crucial for accurately quantifying vehicular pollution. The proposed methodology offers a way to create more accurate emission estimates, providing a tool for policymakers to manage air quality. Full article

Accurate nitrogen dioxide (NO₂) forecasting is crucial for proactive emission control and issuing public health warnings. This study provides the first evaluation of the China Meteorological Administration’s (CMA) operational CUACE/Haze-Fog V3.0 numerical prediction system, assessing its daily NO₂ forecast accuracy against independent satellite measurements and in situ observations. We compare model forecasts with TROPOspheric Monitoring Instrument (TROPOMI) satellite column data and observations from 1677 Chinese ground monitoring stations, focusing on four key regions: the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Urumqi. An optimal spatial resolution of 0.15° × 0.15° was determined for TROPOMI data processing. The results indicate a strong seasonal dependency in model performance. The model systematically underestimates NO₂ concentrations in winter but performs significantly better in summer. This systematic bias is confirmed by a Normalized Mean Bias (NMB) consistently below −20% in northern regions during the winter. In the Beijing–Tianjin–Hebei region, the Root Mean Square Error (RMSE) reached 3.57 × 10¹⁵ molec/cm² (vs. TROPOMI) and 1.09 × 10¹⁵ molec/cm³ (vs. ground stations) in winter, decreasing to 0.95 and 0.91, respectively, in summer. Critically, this winter bias pertains to pollution magnitude rather than temporal correlation; the model captures pollution trends but underestimates peak severity. Our study reveals a ‘vertical decoupling’ in the operational forecasting system. While the model utilizes surface data assimilation to correct surface pollutants, this study demonstrates that these corrections fail to propagate vertically to the total NO₂ column during winter stable boundary layer conditions. This finding has broader implications for chemical transport models (CTMs): relying solely on surface data assimilation is insufficient for constraining column burdens in regions with complex vertical stratification. We propose that future operational systems integrate satellite-based vertical constraints to resolve the systematic winter bias identified here. Full article

Applying coatings that suppress the radiance changes related to temperature-dependent blackbody emission enables temperature-independent optical and sensing systems. Phase-change materials can significantly modify their optical properties within their transition window, but compensating for the large mid-wave infrared (MWIR, 3–5 µm) variation is demanding: blackbody radiance at 3 µm increases nearly 10-fold as the temperature rises from 30 °C to 80 °C. Vanadium dioxide VO₂, whose insulator–metal transition offers a sharp contrast and a low-loss insulating state, is attractive for applications in thermal management, but simple thin-film designs cannot provide full compensation. We demonstrate metasurface coatings that provide this compensation by constructing an array of metal–VO₂–metal antennas tuned to maintain constant thermal emission at a target wavelength over a temperature range of 30 °C to 80 °C. Antennas of several lateral sizes are combined, so their individual resonances collectively track the Planck change. This design provides both optical contrast and the correct temperature derivative, which are unattainable with homogeneous layers. Our approach results in a negligible apparent temperature change of the metasurface across the 30–80 °C range, effectively masking thermal signatures from MWIR detectors stemming from the low losses of VO₂. Full article

The ab initio calculations of the electronic structure of the low-lying electronic states of the CdBr molecule are characterized in the ^2S+1Λ^(+/−) and Ω^(+/−) representations using the complete active-space self-consistent field (CASSCF) method, followed by the multireference configuration interaction (MRCI) method with Davidson correction (+Q). The potential energy curves are investigated, and spectroscopic parameters (T_e, R_e, ω_e, B_e, ${\alpha }_e$, ${\mu }_e$, and D_e) of the bound states are determined and analyzed. In addition, the rovibrational constants (E_v, B_v, D_v, R_min, and R_max) are reported for the investigated states with and without spin–orbit coupling. The electronic transition dipole moment curve (TDMC) is obtained for the C²Π_1/2 − X²Σ⁺_1/2 transition. Based on these data, Franck–Condon factors (FCFs), Einstein coefficient of spontaneous emission A_ν’ν, radiative lifetime τ, vibrational branching ratios, and the associated slowing distance are evaluated. The results indicated that CdBr is a promising candidate for direct laser cooling, and a feasible cooling scheme employing four pumping and repumping lasers in the ultraviolet region with suitable experimentally accessible parameters is presented. These findings provide practical guidance for experimental spectroscopists exploring ultracold diatomic molecules and their applications. Full article

Background: Emerging Asian economies face a critical policy dilemma: macroeconomic and sustainability factors affect high-performing and struggling logistics exporters in fundamentally different ways. Methods: Analysing transportation trade data from China, South Korea, India, Vietnam, Malaysia, and Indonesia (2000–2023) using Panel Quantile Autoregressive Distributed Lag (P-QARDL) methodology, this study investigates asymmetric relationships between macroeconomic indicators (real GDP, inflation, real effective exchange rate), sustainability variables (energy intensity, energy prices, CO₂ emissions), and logistics performance measured through transportation trade flows. Results: The results reveal striking performance-dependent heterogeneities that conventional approaches overlook. Economic growth provides 55% larger benefits to high performers (0.345) versus strugglers (0.222), confirming scale advantages. Energy constraints intensify for successful exporters, with energy intensity penalties 12% larger in upper quantiles. CO₂ emissions correlate positively with logistics performance, with effects doubling from lower (0.142) to upper quantiles (0.341), highlighting an intensifying sustainability trade-off. Error correction operates 39% faster during high-performance periods. Conclusions: These asymmetric relationships challenge one-size-fits-all policies, necessitating targeted energy efficiency interventions for high performers and growth-enabling support for struggling exporters. Full article

This study investigates the propagation of climate model variability to coastal groundwater systems under the high-emission RCP8.5 scenario, focusing on the Almyros Basin in Greece. Using Med-CORDEX bias-corrected climate projections, an Integrated Modelling System (IMS) combines UTHBAL (surface hydrology) and MODFLOW (groundwater hydrology) to simulate future conditions, including precipitation, temperature, evapotranspiration, groundwater recharge, water balance, and seawater intrusion (as a quantity). The analysis quantifies both central tendencies and inter-model spread, revealing substantial declines in groundwater recharge and intensified seawater intrusion, while highlighting the uncertainty introduced by climate model projections. These findings provide critical insights for adaptive water resource management and planning in Mediterranean coastal aquifers under climate change. Full article

In the context of intensifying global efforts to mitigate climate change, methane emissions from the oil and gas sector have emerged as a critical environmental and regulatory challenge, given methane’s high global warming potential over short timeframes. This study investigates methane emissions from representative extraction and production of oil and gas facilities in Romania, focusing on fugitive emissions from wells and associated processing infrastructure. The research is grounded in the implementation of a comprehensive Leak Detection and Repair (LDAR) program, aligned with OGMP 2.0 standards, and utilizes advanced detection technologies such as Flame Ionization Detectors (FID), Optical Gas Imaging (OGI), and Quantitative Optical Gas Imaging (QOGI). A systematic inventory and screening of thousands of components enabled the precise identification and quantification of methane leaks, providing actionable data for maintenance and emissions management. The findings highlight that, although the proportion of leaking components is relatively low, cumulative emissions are significant, with block valves, connectors, and compressor shaft seals identified as the most frequent sources of major leaks. The study underscores the importance of rigorous preventive and corrective maintenance, rapid leak remediation, and the adoption of modern detection and continuous monitoring technologies. The approach developed offers a robust framework for regulatory compliance and supports the transition from inventory-based to measurement-based emissions reporting, in line with recent European regulations. Ultimately, effective methane management not only fulfills environmental obligations but also delivers economic benefits by reducing product losses and enhancing operational efficiency, contributing to the decarbonization and sustainability objectives of the energy sector. Full article

The energy–water–carbon (EWC) nexus has become a critical concern for industrial systems seeking sustainable development, yet existing assessment approaches often require intensive computation and lack practical adaptability. This study proposes a probability-pinch analysis (P-PA) framework that enhances conventional pinch analysis (PA) by integrating allocation-based correction factors to account for system inefficiencies across all time intervals explicitly. The framework incorporates PA tools, specifically the Power Cascade Table (PCT), Water Cascade Table (WCT), and Energy Planning Pinch Diagram (EPPD), to design ideal energy–water systems that do not consider losses. Correction factors based on probable energy and water flows are then incorporated to capture system inefficiencies, with design modifications proposed to meet annual carbon reduction targets. Results from an industrial plant case study validate the effectiveness of P-PA in establishing minimum resource targets while achieving a 46% reduction in carbon emissions through system modifications. Deviations from conventional PA were within 10%, confirming the framework’s accuracy and reliability in designing integrated energy–water systems within the EWC nexus. It could serve as a handy tool for designing large-scale energy–water systems that require substantial computational effort, but it may be less accurate for small-scale applications. Nevertheless, compared with conventional PA-based approaches, P-PA offers a balanced combination of rigor, simplicity, and adaptability, making it well-suited for industrial EWC nexus analysis and decision support in sustainable process design. Full article