Search Results (9,800)

As the spatial carrier of urban development, construction land is a fundamental element for achieving high-quality urban development (HQUD). This study examines the impact of construction land supply on HQUD across 285 Chinese cities. A HQUD evaluation model is created to assess each city’s development level. The GTWR model is then applied to explore the dynamic spatial effects of land supply on HQUD level. The results show the following: (1) The construction land supply exhibited a fluctuating trend accompanied by notable spatial disparities, with hotspots concentrated in coastal areas and cold spots in the northwest and northeast. (2) The HQUD levels consistently increased, forming a stepwise spatial pattern—highest in the east, followed by central and western regions—with localized spatial convergence. (3) The factors influencing HQUD are highly volatile. Industrial agglomeration, resource optimization, and land investment returns drive commercial land supply, investment intensity, and land prices, fostering positive development. However, excessive population density and inadequate public service land may impose pressure on resources and strained public services, hindering progress. Industrial land supply has supported industrial upgrading, shifting its impact from negative to positive. Over-reliance on real estate development can cause resource waste, social instability, and hinder sustainability, reversing the positive effects of residential land supply. This paper clarifies the complex relationship between construction land supply and HQUD, providing empirical guidance for region-specific land supply strategies. Full article

Precise perception of subtle anomalies in power equipment—such as insulator cracks, conductor corrosion, or foreign intrusions—is vital for ensuring the reliability of smart grids. However, foundational vision-language models (VLMs) like CLIP exhibit poor domain transfer and fail to capture minute defect semantics. We propose VisPower, a curriculum-guided multimodal alignment framework that progressively enhances fine-grained perception through two training stages: (1) Semantic Grounding, leveraging 100 K long-caption pairs to establish a robust linguistic-visual foundation, and (2) Contrastive Refinement, using 24 K region-level and hard-negative samples to strengthen discrimination among visually similar anomalies. Trained on our curated PowerAnomalyVL dataset, VisPower achieves an 18.4% absolute gain in zero-shot retrieval accuracy and a 16.8% improvement in open-vocabulary defect detection (OV-DD) over strong CLIP baselines. These results demonstrate the effectiveness of curriculum-based multimodal alignment for high-stakes industrial anomaly perception. Full article

This study aimed to extract rice bran oil rich in γ-oryzanol from white (WB) and parboiled rice bran (PB) using ultrasound as a pre-treatment to supercritical fluid extraction (US + SFE), supercritical fluid extraction (SFE), and conventional solvent extraction. PB oil exhibited superior quality compared to WB, with low free fatty acid (FFA) levels and higher γ-oryzanol content. PB oil extracted by US + SFE achieved a yield of 18.2 ± 0.4%, γ-oryzanol content of 1.53 ± 0.19 g 100 g⁻¹, and low FFA content (0.27 ± 0.01%), showing improved oil quality compared to SFE (yield 13.5 ± 0.3%, γ-oryzanol 1.13 ± 0.08%, FFA 0.55 ± 0.01%) and conventional extraction (yield 25.0 ± 1.3%, γ-oryzanol 2.03 ± 0.04%, FFA 1.12 ± 0.01%). The US + SFE oil also showed lower peroxide value (1.7 mEq kg⁻¹) and preserved fatty acid profiles containing palmitic, oleic, and linoleic acids. US induced structural disruption in bran, enhancing oil release. Additionally, chitosan–gelatin nanoemulsions were developed to protect the extracted oil. Formulations exhibited droplet sizes of 119–352 nm, polydispersity indices below 0.3, and zeta potentials from –12.4 to 38.8 mV. Gelatin-based nanoemulsions maintained FFAs at 0.56 ± 0.2% and peroxide values at 4.71 ± 0.2 mEq kg⁻¹ over 90 days, demonstrating superior oxidative stability. These results highlight the potential of US and SFE combined with nanostructured delivery systems to valorize agro-industrial byproducts and develop stable, functional ingredients and drug carrier systems. Full article

This work addresses the critical need for documentation and validation of structural flight loads for Medium-Altitude Long-Endurance (MALE) Unmanned Aerial Systems (UAS). Despite the increasing prevalence of these aircraft, the industrial and research landscape still exhibits a significant data gap regarding loads under extreme operating conditions, particularly for unconventional geometric configurations. This study presents a rigorous and comprehensive load analysis for the certification of a fixed-wing MALE UAS, which is distinguished by its unique V-Tail configuration, characteristic of platforms such as the Elbit Hermes series. The entire investigation was conducted in strict adherence to the requirements of the NATO airworthiness standard STANAG 4671, aiming to precisely define the aerodynamic behavior and structural integrity of the airframe under an exhaustive set of critical flight conditions. The implemented methodology relies on the use of high-fidelity Computational Fluid Dynamics (CFD) data, derived from RANS simulations to create a complete aerodynamic database. This advanced approach is crucial for the accurate modeling of forces and moments, especially those generated by the coupled control surfaces, known as the ruddervators of the V-Tail. The results obtained include the precise derivation of the operational envelope, which defines the maximum load factors for both maneuver and atmospheric gust conditions. A detailed analysis of balancing and specific loads on the control surfaces was performed, leading to the definition of structural load distributions essential for subsequent stress analysis. Notably, the analysis identified the Unchecked Pitch-Up maneuver performed at the maximum load factor as the dimensioning design condition, particularly for the empennage structure. This work not only provides fundamental data for demonstrating compliance with applicable airworthiness criteria but also establishes a robust and repeatable methodology for the evaluation of flight loads in structurally complex UAS configurations. Full article

As a highly valued orchid species, Cymbidium ensifolium (C. ensifolium) exhibits a natural flowering period mainly from July to September, which does not align with the market demand and shows low flowering quality, thereby significantly constraining the development of the C. ensifolium floriculture industry. To address this key issue, the study used C. ensifolium ‘Longyan Su’ as the experimental material, with white light as the control and composite light with varying ratios of red and blue light as the treatments, and investigated the influence of light quality on flowering. The results showed that blue light could significantly advance the flowering time, while red light could markedly improve the flower quality. Blue light promoted the accumulation of soluble protein and soluble sugar during flower bud differentiation, while red light enhanced their accumulation during floral organ development. During the flower bud differentiation and development stage, blue light increased the synthesis of abscisic acid (ABA) in leaves, and red light promoted the production of gibberellic acid (GA₃) and zeatin riboside (ZR). The study provides an important foundation and reference for further analysis of the flowering mechanism of C. ensifolium under different light quality treatments, and also provides technical support for flowering regulation of orchids in practical production. Full article

Using the Ministry of Environment’s fixed-site air quality monitoring network, we analyzed multiple hazardous air pollutants (HAPs)—including volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), and heavy metals—during 2021–2024 and compared their concentrations with internationally reported levels. Pronounced spatial heterogeneity was observed across stations, particularly for VOCs and heavy metals. Stations A, E, and F were dominated by alkanes, whereas stations B, C, and D exhibited higher proportions of oxygenated VOCs (mainly aldehydes and ketones). Across the network, formaldehyde (0.015 μg/m³), dichloromethane (2.60 μg/m³), toluene (2.53 μg/m³), and acetaldehyde (0.004 μg/m³) were identified as the most abundant species. Stations A and E served as VOC hotspots—formaldehyde peaked at station A and toluene at station E—likely due to nearby industrial and port activities. Concentrations of BTEX generally decreased throughout the study period, with a minor rebound at station C in 2022. Regarding heavy metals, elevated concentrations of lead (16.83 ng/m³), nickel (4.71 ng/m³), and arsenic (1.29 ng/m³) were observed at station A, again suggesting influences from industrial or port-related emissions. Overall, formaldehyde, benzene, and 1,2-dichloroethane were identified as key pollutants of concern, with station A representing the most critical hotspot in the monitoring network. Full article

The kiln head temperature of a rotary kiln is a core process parameter in cement clinker production, and its accurate prediction coupled with uncertainty quantification is crucial for process optimization, energy consumption control, and safe operation. To tackle the prediction challenges arising from strong multi-variable coupling and nonlinear time series characteristics, this paper proposes a prediction approach integrating feature selection, heterogeneous model ensemble, and probabilistic interval estimation. Firstly, the Relief algorithm is adopted to select key features and construct a time series feature set with high discriminability. Then, a hierarchical architecture encompassing deep feature extraction, heterogeneous model fusion, and probabilistic interval quantification is devised. CNN is utilized to extract spatial correlation features among multiple variables, while BiLSTM is employed to bidirectionally capture the long-term and short-term temporal dependencies of the temperature sequence, thereby forming a deep temporal–spatial feature representation. Subsequently, RF is introduced to establish a heterogeneous model ensemble mechanism, and dynamic weight allocation is implemented based on the Mean Absolute Error of the validation set to enhance the modeling capability for nonlinear coupling relationships. Finally, Gaussian probabilistic regression is leveraged to generate multi-confidence prediction intervals for quantifying prediction uncertainty. Experiments on the real rotary kiln dataset demonstrate that the R² of the proposed model is improved by up to 15.5% compared with single CNN, BiLSTM and RF models, and the Mean Absolute Error is reduced by up to 27.7%, which indicates that the model exhibits strong robustness to the dynamic operating conditions of the rotary kiln and provides both accuracy guarantee and risk quantification basis for process decision-making. This method offers a new paradigm integrating feature selection, adaptive heterogeneous model collaboration, and uncertainty quantification for industrial multi-variable nonlinear time series prediction, and its hierarchical modeling concept is valuable for the intelligent perception of complex process industrial parameters. Full article

Cities are major sources of anthropogenic carbon dioxide (CO₂) emissions, making the study of intra-urban CO₂ concentration patterns an emerging research priority. However, limited data availability and the complexity of urban environments have impeded detailed spatiotemporal analyses at the city scale. To address these challenges, an analysis supported by multi-source data and GeoAI methods is carried out to examine the spatial distribution, vertical variation, temporal dynamics, and driving factors of CO₂ concentrations in urban areas. We combined OCO-2 satellite-derived XCO₂ data (2014–2024) with ground-based measurements from the Shanghai Tower (August 2024 to March 2025), alongside meteorological and socioeconomic variables. The analysis employed spatial interpolation (inverse distance weighting), nonparametric testing (Mann–Whitney U test), time series decomposition, ordinary least squares (OLS) regression, and machine learning techniques including random forest and SHAP (SHapley Additive exPlanations) analysis. Results reveal that CO₂ concentrations are significantly higher in central urban districts compared to suburban areas, with notable spatial heterogeneity. Elevated levels were detected near ports and ferry routes, with airports and industrial emissions identified as principal contributors. Vertically, CO₂ concentrations decline with increasing altitude but exhibit a peak at mid-level heights. Temporally, a pronounced seasonal pattern was observed, characterized by higher concentrations in winter and lower levels in summer. Both OLS regression and machine learning models highlight proximity to emission sources, wind speed, and temperature as key determinants of spatial CO₂ variability, with these factors collectively explaining 67% of the variance in OLS models. This study demonstrates how multi-source data and advanced methods can capture the spatial, vertical, and seasonal dynamics and driving factors of urban CO₂ concentrations, offering insights for policy, planning, and mitigation. Full article

Compared with single indicators such as total factor productivity and financial performance, enterprise competitiveness represents the pivotal factor for energy storage enterprises (ESEs) to survive, develop and maintain a leading position in the market. Government subsidies are crucial for guiding the development of the energy storage industry. As countries globally increase their financial backing for ESEs, efficiently utilizing these subsidies has become a major focus. In this study, we examine the impact and mechanisms of government subsidies on the competitiveness of ESEs, using panel data from 248 listed ESEs in China between 2014 and 2023. Employing a range of analytical methods, including two-way fixed effects regression, instrumental variable estimation, and propensity score matching (PSM) tests, the findings demonstrate that government subsidies significantly enhance the competitiveness of ESEs, particularly for non-state-owned ESEs, energy storage system integration enterprises, and ESEs in resource-rich provinces. Further analysis indicates that research and development (R&D) expenditure and financial constraints act as key channels through which subsidies influence competitiveness. Furthermore, electricity prices exert a positive effect on the competitiveness of ESEs, with government subsidies and electricity prices exhibiting a significant substitution relationship in this regard. These findings offer valuable insights for exploring the role of government subsidies in advancing the sustainable development of the energy storage industry and supporting the transition towards achieving dual-carbon goals, while also providing important references for the development of the energy storage industry in other emerging economies. Full article

To achieve the optimal energy allocation between the auxiliary power unit (APU) and battery of connected automated range-extended electric vehicle (CAR-EEV), the hierarchical eco-driving control with dynamic game energy management were investigated and the optimization design of APU working mode was carried out from a multi-objective perspective. Initially, the acceleration and speed of the host vehicle were adjusted in real time, based on the driving status of the preceding vehicle, and the ecological driving speed was obtained in the adaptive car-following eco-driving mode. The dynamic game energy management strategy was proposed, leveraging the real-time interactive information between the vehicle and the traffic environment, and intelligently allocating and scheduling the energy flow within the powertrain. Dynamic game optimization was adopted to achieve dynamic decision-making and control optimization on whether to switch the APU operating speed or not. The multi-objective optimization analyses are carried out based on the weight coefficient matrix. The hierarchical dynamic game energy management strategy based on eco-driving speed (HDGEMS) is implemented through dynamic games and exhibits excellent performance. This strategy enables dynamic adjustment of power distribution between the APU and the battery, thereby allowing the APU to operate efficiently under optimal operating conditions. Meanwhile, it effectively reduces secondary charging losses and the dynamic switching time of the APU, and ultimately achieves energy optimization. Eventually, the results of simulation and experimental thoroughly indicated that economy improvement, emission reduction, and battery life enhancement of CAR-EEV were effectively kept in balance under the control of the proposed HDGEMS with intelligent optimization mode. New research ideas and technical directions are provided for the field of EMS, which is expected to promote technological progress in the industry. Full article

Coal gasification slag (CGS), an industrial solid waste produced during high-temperature (1200–1600 °C) coal gasification, was utilized as the primary raw material, combined with minor additions of coal gangue and calcium oxide, to synthesize ceramsite filter via high-temperature sintering (900–1160 °C) for phosphorus-containing wastewater treatment. The resulting ceramsite was evaluated for compressive strength, apparent porosity, water absorption, mineral phase composition, hydrolysis properties, and phosphorus removal performance. Experimental results revealed that increasing sintering temperature and calcium oxide content shifted the dominant crystalline phases from anorthite and hematite to gehlenite, anorthite, wollastonite, and esseneite, promoting the formation of porous structures. This transition increased apparent porosity while reducing compressive strength. Under optimal conditions (1130 °C, 20 wt.% CaO, 1 h sintering), the ceramsite (CM-20-1130) exhibited an apparent porosity of 43.12%, compressive strength of 3.88 MPa, apparent density of 1.084 g/cm³, and water absorption of 33.20%. The high porosity and abundant gehlenite and wollastonite phases endowed CM-20-1130 with enhanced hydrolysis capacity. Static phosphorus removal experiments demonstrated a maximum phosphorus removal capacity of 2.77 mg/g, driven by the release of calcium and hydroxide ions from gehlenite and wollastonite, which form calcium-phosphate precipitates on the ceramsite surface, enabling efficient phosphorus removal from simulated wastewater. Full article

Wind power has become a strategic cornerstone of China’s renewable-energy transition and industrial upgrading, making it essential to understand how policy interventions shape the behaviour of its industry chain. This study examines how major wind power policies issued between 2015 and 2024 transmit shocks across nine upstream, midstream, and downstream sectors. Using four contagion tests based on higher-order co-moments, combined with a policy sensitivity index, the analysis identifies distinct transmission patterns across policy types. The results show that market-mechanism reforms induce the strongest and most systemic contagion effects, reflecting their ability to align financial incentives with renewable-integration objectives. Upstream sectors—particularly equipment and key material industries—exhibit the highest responsiveness, while midstream construction and downstream operation and maintenance display more moderate and delayed adjustments. Development and construction policies generate broader but less intensive contagion, whereas industry-support measures trigger selective, sector-specific responses. These findings offer practical guidance for improving policy coordination, investment planning, and industrial upgrading within China’s wind power value chain. Future research could extend the analysis by incorporating firm-level data, longer policy cycles, and interactions with other structural shocks such as electricity-market reforms and climate-related risks. Full article

Selective adsorption is regarded as a promising alternative for propylene/propane separation. However, the similar physicochemical properties of these two components pose a challenge in developing adsorbents that simultaneously exhibit high selectivity and substantial adsorption capacity. This study aims to achieve molecular sieving of propylene and propane by precisely controlling the pore size of silicoaluminophosphate (SAPO) molecular sieve. The pore size of the eight-membered-ring SAPO-35 molecular sieve is tuned via ion exchange to fall between the kinetic diameters of propylene and propane, enabling selective adsorption of propylene while excluding propane molecules. Ion exchange treatment increased the equilibrium adsorption selectivity of the SAPO-35 from 2.2 to 11.4, placing it among the highest-performing molecular sieve-based adsorbents. This modification also substantially improved the material’s regeneration capability at ambient temperature. Theoretical calculations reveal that steric hindrance effects, arising when gas molecules diffuse through the eight-membered-ring channels, contribute significantly to the high adsorption selectivity. Breakthrough experiments demonstrated that Na-SAPO-35 achieves a dynamic selectivity of 15.9 for propylene/propane separation. The development of Na-SAPO-35 adsorbents with high selectivity, substantial adsorption capacity, and robust durability is critical for advancing the industrial implementation of adsorption-based separation technologies. Full article

Sunflower meal represents a protein- and fiber-rich by-product of the oil industry with potential application as a natural stabilizer in food emulsions. Building upon previous findings that emphasized the role of protein content in emulsion stability, the present study further investigated the combined effect of protein level and particle size distribution of sunflower meal fractions on the formation and stability of oil-in-water emulsions. Two sets of sunflower meal fractions were prepared from finely milled material, fractionated, and blended in controlled proportions to obtain four protein-enriched (30 ± 1%) and four cellulose-rich (15 ± 1%) fractions, each defined by particle size ranges of 250/200, 200/125, 125/100, and <100 µm. Emulsion stability was evaluated through droplet size analysis, zeta potential measurements, and creaming index determination during seven days of storage. The results demonstrated that both protein content and particle size significantly affected the emulsifying and stabilizing behavior of sunflower meal fractions. For the low-protein group (15%), larger particle sizes (250/200 µm) yielded smaller emulsion droplets (D[4.3] = 66.03 µm) and higher zeta potential values (−15.53 mV), while in the high-protein group (30%), droplet size distribution was more uniform (D[4.3] from 72.13 to 76.29 µm). During seven days of storage, all emulsions exhibited a gradual increase in creaming index, followed by partial stabilization at later time points. Emulsions prepared with sunflower meal fractions of higher-protein content showed consistently lower creaming index values, indicating improved physical stability throughout storage. Overall, the study confirmed that the interplay between composition (protein level) and physical structure (particle size) governs the emulsification efficiency of sunflower meal fractions, providing insights for their potential application as plant-based stabilizers in food systems. Full article

C₆F₁₂O has been recognized as an environmentally friendly substitute applied in the fire protection, insulation equipment, and refrigeration industry. The stability and catalytic decomposition characteristics of C₆F₁₂O in the presence of metals are crucial for evaluating the applicability of such alternatives across different scenarios and recycling treatment. In this study, the adsorption and decomposition mechanisms of C₆F₁₂O on Cu (1 0 0), Cu (1 1 0), and Cu (1 1 1) surfaces have been investigated based on the density functional theory (DFT). The adsorption structures and energies of C₆F₁₂O and its key dissociation products are investigated to obtain the most stable adsorption configurations. Additionally, the projected density of states (PDOS) and electron density difference calculations are performed to explore the electronic properties of the adsorption systems. Four major dissociation reactions involving the C-C bond breakage of C₆F₁₂O that occurred on Cu surfaces are examined individually, with comparisons made to the corresponding homolytic reactions of free C₆F₁₂O. The results indicate that Cu surfaces exhibit a promising catalytic effect for C₆F₁₂O decomposition, which depends on both the kind of surfaces and the reaction pathway. Furthermore, most decomposition pathways of C₆F₁₂O on Cu surfaces are exothermic and C₆F₁₂O tends to decompose into C₅F₉O and CF₃ under the Cu catalytic effect. Full article