Search Results (1,225)

The global climate crisis has driven unprecedented agreements among nations on carbon mitigation. With China’s commitment to carbon peaking and carbon neutrality targets, the building sector has emerged as a critical focus for emission reduction, particularly because office buildings account for over 30% of building energy consumption. However, a systematic and regionally adaptive low-carbon technology evaluation framework is lacking. To address this gap, this study develops a multidimensional decision-making system to quantify and rank low-carbon technologies for office buildings in Beijing. The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. The results indicate distinct priority patterns across the building types: low-rise buildings prioritize roof-mounted photovoltaic (PV) systems, LED lighting, and thermal-break aluminum frames with low-E double-glazed laminated glass. Mid- and high-rise buildings emphasize integrated PV-LED-T8 lighting solutions and optimized building envelope structures. Ranking analysis further highlights LED lighting, T8 high-efficiency fluorescent lamps, and rooftop PV systems as the top-recommended technologies for Beijing. Additionally, four policy recommendations are proposed to facilitate the large-scale implementation of the program. This study presents a holistic technical integration strategy that simultaneously enhances the technological performance, economic viability, and carbon reduction outcomes of architectural design and renovation. It also establishes a replicable decision-support framework for decarbonizing office and public buildings in cities, thereby supporting China’s “dual carbon” goals and contributing to global carbon mitigation efforts in the building sector. Full article

This study introduced basalt fibres as a reinforcing material and employed notched beam three-point bending tests combined with digital image correlation (DIC) technology to comprehensively evaluate key fracture parameters—namely, initial fracture toughness, unstable fracture toughness, fracture energy, and ductility index—of expanded polystyrene (EPS)-based geopolymer concrete with different mix proportions. The results demonstrate that the optimal fracture performance was achieved when the basalt fibre volume content was 0.4% and the EPS content was 20%, resulting in respective increases of 12.07%, 28.73%, 98.92%, and 111.27% in the above parameters. To investigate the toughening mechanisms, scanning electron microscopy was used to observe the fibre–matrix interfacial bonding and crack morphology, while X-ray micro-computed tomography enabled detailed three-dimensional visualisation of internal porosity and crack development, confirming the crack-bridging and energy-dissipating roles of basalt fibres. Furthermore, the crack propagation process was simulated using the extended finite element method, and the evolution of fracture-related parameters was quantitatively analysed using a linear superposition progressive assumption. A simplified predictive model was proposed to estimate fracture toughness and fracture energy based on the initial cracking load, peak load, and compressive strength. The findings provide theoretical support and practical guidance for the engineering application of basalt fibre-reinforced EPS-based geopolymer lightweight concrete. Full article

Deadwood is essential for the forest ecosystem productivity and stability. A growing body of evidence indicates that deadwood-inhabiting microbes are effective decomposition agents, yet little is known about how changes in microbial communities during the initial deadwood decay. In a small forest area, we performed dense sampling from the top, middle, and bottom portions of two representative Pinus massoniana cultivars logs to track deadwood xylem microbiota shift during the initial deadwood decay. We found xylem mycobiota varied dramatically during the initial deadwood decay. Deadwood microbes might largely originate from the endophytic microbes of living trees during the initial deadwood decay. Notably, bark type is an important driving factor for xylem mycobiota changes during the initial deadwood decay. Ten upregulated metabolites were screened out by a univariate analysis approach. Moreover, our correlation analysis suggests that enriched microbes at class level was significantly correlated with the upregulated metabolites during the initial deadwood decay. Our work provides new insights into the process of mycobiota and metabolite changes during the initial deadwood decay. Full article

Accurate and reliable multi-scene positioning remains a critical challenge in autonomous driving systems, as conventional fixed-noise fusion strategies struggle to handle the dynamic error characteristics of heterogeneous sensors in complex operational environments. This paper proposes a novel noise-adaptive fusion framework integrating Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) measurements. Our key innovation lies in developing a dual noise estimation model that synergizes priori weighting with posterior variance compensation. Specifically, we establish an a priori weighting model for satellite pseudorange errors based on elevation angles and signal-to-noise ratios (SNRs), complemented by a Helmert variance component estimation for posterior refinement. For INS error modeling, we derive a bias instability noise accumulation model through Allan variance analysis. These adaptive noise estimates dynamically update both process and observation noise covariance matrices in our Error-State Kalman Filter (ESKF) implementation, enabling real-time calibration of GNSS and INS contributions. Comprehensive field experiments demonstrate two key advantages: (1) The proposed noise estimation model achieves 37.7% higher accuracy in quantifying GNSS single-point positioning uncertainties compared to conventional elevation-based weighting; (2) in unstructured environments with intermittent signal outages, the fusion system maintains an average absolute trajectory error (ATE) of less than 0.6 m, outperforming state-of-the-art fixed-weight fusion methods by 36.71% in positioning consistency. These results validate the framework’s capability to autonomously balance sensor reliability under dynamic environmental conditions, significantly enhancing positioning robustness for autonomous vehicles. Full article

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent condition worldwide and represents a major global health challenge. Pharmacological and pharmacodynamic results indicate that aspirin eugenol ester (AEE) performs various pharmacological activities. However, it is unclear whether AEE can ameliorate the NAFLD. This study investigated the ameliorative effects of AEE on glucose and lipid metabolism disorders by in vitro and in vivo experiments. In the cellular model, TC increased to 0.104 μmol/mg and TG increased to 0.152 μmol/mg in the model group, while TC decreased to 0.043 μmol/mg and TG decreased to 0.058 μmol/mg in the AEE group. In the model group, the area occupied by lipid droplets within the visual field was significantly elevated to 17.338%. However, the administration of AEE resulted in a substantial reduction in this area to 10.064%. AEE significantly reduced the lipid droplet area and TC and TG levels (p < 0.05), increased bile acids in the cells and in the medium supernatant (p < 0.05), and significantly up-regulated the expression of LRH-1, PPARα, CYP7A1, and BSEP mRNA levels (p < 0.05) compared to the model group. In the animal model, different doses of AEE administration significantly down-regulated the levels of TC, TG, LDL, GSP, and FBG (p < 0.05) compared to the high-fat-diet (HFD) group, and 216 mg/kg of AEE significantly improved hepatocellular steatosis, attenuated liver injury, and reduced the area of glycogen staining (p < 0.05). In the HFD group, the glycogen area within the visual field exhibited a significant increase to 18.250%. However, the administration of AEE resulted in a notable reduction in the glycogen area to 13.314%. Liver and serum metabolomics results show that AEE can reverse the metabolite changes caused by a HFD. The major metabolites were involved in seven pathways, including riboflavin metabolism, glycerophospholipid metabolism, tryptophan metabolism, primary bile acid biosynthesis, biosynthesis of unsaturated fatty acids, nicotinate and nicotinamide metabolism, and tryptophan metabolism. In conclusion, AEE had a positive regulatory effect on NAFLD. Full article

Understanding the price dynamics of oil futures is crucial for advancing green finance strategies and supporting sustainable energy transitions. This study investigates the macroeconomic and energy market determinants of oil futures prices through Granger causality, cointegration analysis, and the error correction model, using daily data. It focuses on the influence of economic development levels, exchange rate fluctuations, and inter-energy price linkages. The empirical findings indicate that (1) oil futures prices exhibit strong correlations with other energy prices, macroeconomic factors, and exchange rate variables; (2) economic development significantly affects oil futures prices, while exchange rate impacts are statistically insignificant based on the daily data analyzed; (3) there exists a stable long-term equilibrium relationship between oil futures prices and variables representing economic activity, exchange rates, and energy market trends; (4) oil futures prices exhibit significant short-term dynamics while adjusting steadily toward a long-run equilibrium driven by macroeconomic and energy market fundamentals. By enhancing the accuracy of oil futures price forecasting, this study offers practical insights for managing financial risks associated with fossil energy markets and contributes to the formulation of low-carbon investment strategies. The findings provide a valuable reference for integrating energy pricing models into sustainable finance and climate-aligned portfolio decisions. Full article

As a unique woody oil crop in China, Camellia oleifera Abel. germplasm resources show significant genetic diversity in Ya’an City. This study measured 60 phenotypic traits (32 quantitative, 28 qualitative) of 302 accessions to analyze phenotypic variation, establish a classification system, and screen high-yield, high-oil germplasms. The phenotypic diversity index for fruit (H’ = 1.36–1.44) was significantly higher than for leaf (H’ = 1.31) and flower (H’ < 1), indicating genetic diversity concentrated in reproductive traits, suggesting potential genetic variability in these traits. Fruit quantitative traits (e.g., single fruit weight CV = 35.37%, fresh seed weight CV = 38.93%) showed high genetic dispersion. Principal component analysis confirmed the fruit factor and economic factor as main phenotypic differentiation drivers. Quantitative traits were classified morphologically, and correlation analysis integrated them into 13 key indicators classified using LSD and range methods. Finally, TOPSIS evaluation selected 10 excellent germplasms like TQ122 and TQ49, with fruit weight, fresh seed yield, and kernel oil content significantly exceeding the population average. This study provides data for C. oleifera DUS test guidelines and proposes a multi-trait breeding strategy, supporting high-yield variety selection and germplasm resource protection. Full article

Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper proposes an end-to-end polygon dynamic adjustment algorithm (PDAA) to improve the accuracy and geometric consistency of building contour extraction by dynamically generating and optimizing polygon vertices. The method first locates building instances through the region of interest (RoI) to generate initial polygons, and then uses four core modules for collaborative optimization: (1) the feature enhancement module captures local detail features to improve the robustness of vertex positioning; (2) the contour vertex tuning module fine-tunes vertex coordinates through displacement prediction to enhance geometric accuracy; (3) the learnable redundant vertex removal module screens key vertices based on a classification mechanism to eliminate redundancy; and (4) the missing vertex completion module iteratively restores missed vertices to ensure the integrity of complex contours. PDAA dynamically adjusts the number of vertices to adapt to the geometric characteristics of different buildings, while simplifying the prediction process and reducing computational complexity. Experiments on public datasets such as WHU, Vaihingen, and Inria show that PDAA significantly outperforms existing methods in terms of average precision (AP) and polygon similarity (PolySim). It is at least 2% higher than existing methods in terms of average precision (AP), and the generated polygonal contours are closer to the real building geometry. Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response. Full article

In this investigation, a hierarchical FeCo-layered double hydroxide (FeCo-LDH) electrochemical membrane material was prepared by a simple in situ hydrothermal method. The prepared material formed a 3D honeycomb-structured FeCo-LDH-modified carbon felt (FeCo-LDH/CF) catalytic layer with uniform open pores on a CF substrate with excellent catalytic activity and was served as the cathode in a flow-through electro-Fenton (FTEF) reactor. The electrocatalyst demonstrated excellent treatment performance (99%) in phenol simulated wastewater (30 mg L⁻¹) under the optimized operating conditions (applied voltage = 3.5 V, pH = 6, influent flow rate = 15 mL min⁻¹) of the FTEF system. The high removal rate could be attributed to (i) the excellent electrocatalytic oxidation performance and low interfacial charge transfer resistance of the FeCo-LDH/CF electrode as the cathode, (ii) the ability of the synthesized FeCo-LDH to effectively promote the conversion of H₂O₂ to •OH under certain conditions, and (iii) the flow-through system improving the mass transfer efficiency. In addition, the degradation process of pollutants within the FTEF system was additionally illustrated by the •OH dominant ROS pathway based on free radical burst experiments and electron paramagnetic resonance tests. This study may provide new insights to explore reaction mechanisms in FTEF systems. Full article

The detection of aerial imagery captured by Unmanned Aerial Vehicles (UAVs) is widely employed across various domains, including engineering construction, traffic regulation, and precision agriculture. However, aerial images are typically characterized by numerous small targets, significant occlusion issues, and densely clustered targets, rendering traditional detection algorithms largely ineffective for such imagery. This work proposes a small target detection algorithm, SR-YOLO. It is specifically tailored to address these challenges in UAV-captured aerial images. First, the Space-to-Depth layer and Receptive Field Attention Convolution are combined, and the SR-Conv module is designed to replace the Conv module within the original backbone network. This hybrid module extracts more fine-grained information about small target features by converting image spatial information into depth information and the attention of the network to targets of different scales. Second, a small target detection layer and a bidirectional feature pyramid network mechanism are introduced to enhance the neck network, thereby strengthening the feature extraction and fusion capabilities for small targets. Finally, the model’s detection performance for small targets is improved by utilizing the Normalized Wasserstein Distance loss function to optimize the Complete Intersection over Union loss function. Empirical results demonstrate that the SR-YOLO algorithm significantly enhances the precision of small target detection in UAV aerial images. Ablation experiments and comparative experiments are conducted on the VisDrone2019 and RSOD datasets. Compared to the baseline algorithm YOLOv8s, our SR-YOLO algorithm has improved mAP@0.5 by 6.3% and 3.5% and mAP@0.5:0.95 by 3.8% and 2.3% on the datasets VisDrone2019 and RSOD, respectively. It also achieves superior detection results compared to other mainstream target detection methods. Full article

Screwed connections are widely adopted in timber–concrete composite (TCC) structures. Owing to the diverse connection configurations and complex shear mechanisms, existing empirical models or theoretical formulas cannot accurately and efficiently predict the shear modulus of a screwed connection. Therefore, this study develops machine learning (ML) algorithms to accurately predict the slip modulus. A data set including 222 sets of testing results was established by collecting the values of the slip modulus and associated ten features. Four ML methods, including decision tree (DT), random forest (RF), adaptive boosting machine (AdaBoost), and gradient boosting regression tree (GBRT), are adopted to develop the ML algorithm. The Shapley Additive Explanation (SHAP) framework was employed to interpret the effects of related features on the slip modulus. GBRT demonstrated the best accuracy compared with the other three ML methods in terms of four popular quantitative metrics. Moreover, all ML methods showed an evident accuracy advantage compared to existing analytical methods. Through a SHAP analysis, it was found that concrete strength, screw inclination, timber density, and timber type have a large impact on the slip modulus of a screwed connection compared to other input features. Full article

The Oligocene Dongying Formation beach-bar system, widely distributed in the HHK Depression of the Bohai Bay Basin, constitutes a key target for mid-deep hydrocarbon exploration, though its provenance remains controversial due to complex peripheral source terrains. To address this, we developed an integrated methodology combining LA-ICP-MS zircon U-Pb dating with whole-rock rare earth element (REE) analysis, facilitating provenance studies in areas with limited drilling and heavy mineral data. Analysis of 849 high-concordance zircons (concordance >90%) from 12 samples across 5 wells revealed that Geochemical homogeneity is evidenced by strongly consistent moving-average trendlines of detrital zircon U-Pb ages among the southern/northern provenances and the central uplift zone, complemented by uniform REE patterns characterized by HREE (Gd-Lu) enrichment and LREE depletion; geochemical disparities manifest as dual dominant age peaks (500–1000 Ma and 1800–3100 Ma) in the southern provenance and central uplift samples, contrasting with three distinct peaks (65–135 Ma, 500–1000 Ma, and 1800–3100 Ma) in the northern provenance; spatial quantification via multidimensional scaling (MDS) demonstrates closer affinity between the southern provenance and central uplift (d_ij = 4.472) than to the northern provenance (d_ij = 6.708). Collectively, these results confirm a dual (north–south) provenance system for the central uplift beach-bar deposits, with the southern provenance dominant and the northern acting as a subsidiary source. This work establishes a dual-provenance beach-bar model, providing a universal theoretical and technical framework for provenance analysis in hydrocarbon exploration within analogous settings. Full article

Rechargeable aqueous zinc-ion batteries (AZIBs) have garnered significant research attention in the energy storage field owing to their inherent safety, cost-effectiveness, and environmental sustainability. Nevertheless, critical challenges associated with zinc anodes—including dendrite formation, hydrogen evolution corrosion, and mechanical degradation—substantially impede their practical implementation. Grain boundary engineering (GBE) emerges as an innovative solution for zinc anode optimization through the precise regulation of grain boundary density, crystallographic orientation, and chemical states in metallic materials. This study comprehensively investigates the fundamental mechanisms and application prospects of GBE in zinc-based anodes, providing pivotal theoretical insights and technical methodologies for designing highly stable electrode architectures. The findings are expected to promote the development of aqueous zinc batteries toward a high energy density and long cycle life. Full article

Organic nonvolatile transistor memories (ONVMs) using a hybrid spiro [fluorene-9,7′-dibenzo [c, h] acridine]-5′-one (SFDBAO)/polystyrene (PS) film as bulk-heterojunction-like tunneling and trapping elements were fabricated. From the characterization of the 10% SFDBAO/PS based on ONVM, a sterically hindered small-molecule SFDBAO with rigid orthogonal configuration and a donor–acceptor (D-A) structure as a molecular-scale charge storage element demonstrated significantly higher charge trapping ability than other small-molecule materials such as C₆₀ and Alq₃. The ONVM based on 10% SFDBAO/PS presents ambipolar memory behaviors with a wide memory window (146 V), a fast-switching speed (20 ms), an excellent retention time (over 5 × 10⁴ s), and stable reversibility (36 cycles without any noticeable decay). By applying different gate voltages, the above ONVM shows reliable four-level data storage characteristics. The investigation demonstrates that the strategical bulk-heterojunction-like tunneling and trapping elements composed of small-molecule materials and polymers exhibit promising potential for high-performance ambipolar ONVMs. Full article

The application of fluidized bed reactors to biomass fast pyrolysis is regarded as a promising technology for enabling high-value utilization of biomass. This work established a three-dimensional numerical model of an industrial-scale fluidized bed reactor for biomass catalytic pyrolysis, employing the multiphase particle-in-cell method (MP-PIC) and coupling catalytic pyrolysis kinetics. Primary gas flow rate and biomass–catalyst injection modes were optimized to further improve the performance of the reactor. The model received additional validation from experimental data, primarily to ensure prediction accuracy. The results revealed that an optimal primary gas flow rate of 4 kg/s achieved a peak catalytic efficiency of 71.3%. Using maximum high-quality liquid fuels and adopting a relatively dispersed inlet mode with opposite jetting for biomass and catalyst promoted uniform particle distribution and thermal homogeneity in the dense phase zone, further increasing the catalytic efficiency to 75.6%. With the integration of the multiphase particle-in-cell (MP-PIC) method with catalytic pyrolysis kinetics at the industrial-scale, this work could provide theoretical guidance for designing fluidized bed catalytic pyrolysis reactors and optimizing biomass catalytic pyrolysis processes. Full article