Search Results (1,586)

To address the challenge of balancing economic growth with carbon emission reduction, improving regional Carbon Emission Efficiency (CEE) has emerged as a central pathway to achieving the “dual carbon” goals. While most existing studies focus on inter-regional CEE linkages through pairwise interaction networks, such approaches fall short in capturing the high-order mechanisms of multi-regional collaboration. This study integrates the Super-SBM model with a modified gravity model to construct a CEE correlation network across 30 provincial administrative regions in China from 2007 to 2023. To overcome the limitations of traditional pairwise networks, simplicial complex theory is introduced to establish a high-order topological representation framework. Furthermore, by applying the multiorder Laplacian to assess the synchronization stability of the network, a directed second-order degree swap strategy is proposed to optimize its high-order structure. The findings reveal that the CEE correlation network has evolved from a single-pole aggregation pattern toward a multi-center equilibrium. Provinces with high connectivity play a dominant role in both pairwise and triadic synergies, though their collaborative advantages are gradually diffusing to central and western regions. Notably, with only a limited number (approximately five) of second-order degree swaps among key node pairs, the network’s synchronization stability can be substantially improved. When first-order and second-order interaction strengths reach comparable levels (coupling strength ${\alpha }^{*}\approx 0.5$), the system achieves optimal resistance to external perturbations. This study highlights the pivotal role of high-order collaboration in shaping regional CEE linkages and offers a practical optimization pathway for structurally enhancing CEE through coordinated efforts in pursuit of the “dual carbon” goals. Full article

Under continuous erosion of dynamic water pressure generated by vehicle–water–pavement coupling interaction, asphalt mixture will gradually deteriorate and severe moisture damage finally emerges. The fine aggregate mixture (FAM) component is notably eroded and stripped, while the aggregate component even cracks sometimes. Sufficient attention has not been paid to these critical phenomena. This study employed the 3D laser scanning technique to detect changes in surface roughness of the asphalt mixture before and after it was eroded by dynamic water pressure. The degree of erosion of the asphalt mixture, FAM component, and aggregate component were thereby evaluated. The influences of experimental parameters such as water temperature and pore water pressure magnitude, as well as variable parameters including lithology and asphalt type, were also taken into account. By integrating the detection of physical and mechanical properties evolution of aggregates, the mechanism of moisture damage was comprehensively illustrated from the perspectives of both components of FAM and aggregate. The findings revealed that the 3D laser scanning technique could clearly detect and quantitatively assess the morphological changes on the asphalt mixture surface after been eroded in dynamic water pressure. Both types of asphalt mixtures exhibited varying degrees of erosion and wear, and obvious increases in surface unevenness were observed in each case. Variations in either temperature or pore water pressure magnitude showed limited influence on moisture damage in basalt-based asphalt mixture. In contrast, moisture damage sustained by limestone-based asphalt mixture was notably sensitive to temperature changes but remained largely insensitive to fluctuations in pore water pressure magnitude. The increase in surface roughness of asphalt mixture was primarily attributed to the scouring action of dynamic water pressure, which removed the FAM component surrounding coarse aggregate particles. Degradation in coarse aggregate particles would lead to the deterioration of the entire asphalt mixture. The compatibility between the stripping rate of FAM component and the deterioration rate of coarse aggregate governed the macroscopic manifestation of overall moisture damage in the asphalt mixture. Full article

In many situations, the complete microscopic structure of a network is empirically inaccessible and has to be inferred from aggregate information using some probabilistic model. While several network reconstruction methods have been developed in the case of single-layer networks where nodes can be connected only by one type of link, the problem is still largely unexplored in the case of multiplex networks where several interdependent layers, each representing a distinct mode of connection, coexist. Even the most advanced network reconstruction techniques, when applied to each layer separately, may fail in replicating the inter-layer dependencies embodying the essence of multiplex networks. Here we develop a methodology to reconstruct a class of correlated multiplexes which includes, as a specific example that we study in detail, the multiplex network of international trade in different products. Our method starts from virtually any reconstruction model that successfully reproduces a set of desired marginal properties of each layer separately, i.e., node strengths and/or node degrees. It then introduces the minimal dependency structure required to replicate an additional set of higher-order properties, namely the portion of each node’s degree and each node’s strength that is shared and/or reciprocated across pairs of layers. These properties are found to provide empirically robust measures of inter-layer coupling, allowing for an accurate reconstruction of the world trade multiplex network. Our method allows for joint multi-layer connection probabilities to be reliably reconstructed from marginal ones, effectively bridging the gap between single-layer information and global multiplex properties. Full article

Against the backdrop of dual-carbon goals and resource constraints, the high-value utilization of recycled fine aggregates (RFAs) remains limited, leading to inconsistent engineering performance and insufficient durability. Enzyme-induced carbonate precipitation (EICP) represents a promising low-carbon cementation method, yet its deposition uniformity and cementation efficiency are influenced by the pore structure of granular media and associated mass transfer pathways. This study employs a two-stage experimental design to investigate the synergistic effects of particle size distribution characteristics, represented primarily by d₅₀, and fiber addition on EICP-cemented RFA. Phase I (fiber-free; d₅₀ = 0.67–1.14 mm) results indicate that, across the tested gradation schemes, the CaCO₃ content generally decreased from 9.49% to 7.72% as the representative d₅₀ increased, while the dry density changed only slightly (1.637–1.617 g/cm³). However, the unconfined compressive strength (UCS) decreased from 1000 kPa to 541 kPa (45.9% reduction), indicating that strength is primarily governed by the connectivity of the cementation network rather than solely by the degree of densification. In Phase II, glass fiber (GF), polypropylene fiber (PPF), and jute fiber (JF) were incorporated into the ERFA4 gradation scheme selected for fiber modification. All three systems exhibited a unimodal optimum pattern: the peak CaCO₃ contents reached 10.71% (GF 0.5%), 10.11% (PPF 0.7%), and 11.46% (JF 0.7%), corresponding to peak UCS values of 1917, 1874, and 2450 kPa, respectively. Microscopic analysis suggested that fiber bridging coupled with CaCO₃ deposition may contribute to the formation of a “fiber-CaCO₃-particle” stress-transfer network, which is consistent with the observed enhancements in load-bearing capacity, ductility, and post-peak stability. Full article

Improving the interfacial compatibility between cellulose and poly(butylene adipate-co-terephthalate) (PBAT) is critical for enhancing the performance of PBAT-based composites. Here, microcrystalline cellulose (MCC) was homogeneously silanized at the molecular chain level using t-hexyldimethylchlorosilane (TDMS-Cl) as the modifier, yielding t-hexyldimethylsilylated cellulose (TDMS-Cell). TDMS-Cell/PBAT composite films were then prepared by solution blending and casting in tetrahydrofuran (THF). Structural characterizations confirmed the successful grafting of TDMS-Cl onto cellulose chains, resulting in TDMS-Cell with a degree of substitution of approximately 2. Microstructural observations combined with thermal analysis revealed that TDMS-Cell exerted a dual effect on the crystallization behavior of PBAT: it acted as a heterogeneous nucleating agent that increased the crystallization temperature, while the pronounced steric hindrance simultaneously suppressed crystal growth. Mechanical testing showed that simultaneous strengthening and toughening were achieved at an optimal TDMS-Cell loading of 3–5 wt%. Specifically, the tensile strength increased from ~16 MPa for neat PBAT to 21 MPa (31.25% improvement), and the elongation at break increased from ~700% to 964% (37.7% improvement). In addition, the incorporation of an appropriate amount of TDMS-Cell effectively enhanced the surface hydrophobicity of the composite films. At higher filler loading, however, solvent evaporation-induced phase separation led to self-aggregation of TDMS-Cell, which in turn deteriorated both the mechanical properties and surface hydrophobicity of the composites. Overall, this work systematically elucidates the structure–property relationships of silanized cellulose/PBAT composites in a homogeneous solution system, providing a rational basis for interfacial design and property optimization of PBAT/biomass-based composite materials. The prepared TDMS-Cell/PBAT composite films with balanced mechanical strength, tunable crystallization behavior, and improved surface hydrophobicity exhibit great potential for practical applications in high-performance flexible packaging materials, functional film substrates, lightweight composite structural components, and tunable hydrophobicity coating substrates. Full article

Understanding how landscape structure affects nutrient pollution is essential for contemporary effective river basin management. This study examined the influence of landscape composition and configuration on concentrations of nitrate (NO₃⁻), nitrite (NO₂⁻), and ammonium (NH₄⁺) in 30 small lowland catchments of central–eastern Poland during the cold period. Water samples were collected monthly from September 2021 to April 2022, and land-use patterns were quantified using landscape metrics derived from high-resolution spatial data at the catchment scale and within riparian buffer zones. The results showed that the impact of land use on nitrogen concentrations was strongly dependent on both landscape type and spatial scale. Forests, meadows, wetlands, and water bodies generally acted as sink landscapes, reducing nitrate and nitrite levels. The effect was more pronounced in catchments where forest patches (mainly coniferous) covered a larger area, had greater total Edge Length, and were more complex in shape. It was advantageous when meadow patches were large, cohesive, and weakly fragmented. In contrast, arable land and built-up areas consistently functioned as source landscapes, contributing to higher nitrogen concentrations when characterized by a larger share, size (both), and aggregation degree of patches (arable land). Higher landscape diversity at the catchment scale was associated with lower nitrate and nitrite concentrations. Overall, land-use effects were best explained at larger spatial extents, especially the entire catchment and the 500 m buffer zone. These findings emphasize the need to integrate landscape structure and appropriate spatial scale into nutrient management strategies for lowland agricultural catchments. Full article

This research explores the use of industrial waste as an alternative to natural raw materials, promoting a circular economy in the construction sector. It specifically investigates the manufacturing of paving blocks using blast furnace slag and recycled aggregates. Paving blocks were produced without altering typical industry conditions, entirely replacing cement with alkaline-activated blast furnace slag. The study replaced natural aggregate in three proportions (20%, 50%, and 100%) with three types of recycled aggregates: concrete recycled aggregate (CA), masonry recycled aggregate (MA), and recycled mixed aggregate (RMA), in both coarse and fine fractions. The experimental procedure analysed the impact of recycled aggregates in an alkaline-activated slag matrix through three phases: characterising physical properties (mechanical properties, water absorption, density, abrasion resistance, and slip resistance), evaluating leaching behaviour, and conducting a life cycle analysis. The results of physical characterisation were statistically analysed using principal component analysis (PCA). The results obtained show the feasibility of manufacturing paving blocks with blast furnace slag by completely replacing the natural aggregate with the coarse fraction of the three recycled aggregates used and replacing up to 20% in the case of using the fine fraction. The properties of the paving blocks manufactured with slag depend mainly on the degree of substitution of natural aggregate with the recycled aggregate. All paving blocks can be considered environmentally safe from leaching according to the Dutch Soil Quality Decree. Paving blocks made from alkali-activated ground granulated blast furnace slag and recycled aggregates generate a lower carbon footprint compared to concrete paving blocks. Full article

The asphalt industry, essential for the global transport infrastructure, requires substantial investments to increase the durability of production facilities. The quality of asphalt depends, essentially, on the degree of drying of mineral aggregates. Therefore, the rotary dryer is of major importance for ensuring the quality of asphalt. The rotary dryer flights are subjected to an erosive-abrasive wear process during operation, generated by the impact of abrasive aggregates. These phenomena lead to severe degradation of the flights. Experimental research, carried out by the authors, on-site, aimed at identifying solutions to improve the wear behavior of the flights, by hardfacing with four wear-resistant materials (FLUXOFIL 51, FLUXOFIL 56, SAFER R 400, SAFER R 600), using the GMAW and SMAW processes. The results revealed a decrease in the wear rate and a flattening effect of the wear curve along the profile of the flight. The research targeted the upper rear surface of the flights, which is predominantly affected by erosive-abrasive wear phenomena. The resistance to abrasive wear of the flights was improved by hardfacing with FLUXOFIL 51 wear-resistant tubular wire, resulting in the lowest wear rate, especially between the areas marked 14–26, which are the areas most affected during operation. Full article

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response. Full article

Semiconductor quantum dots (QDs) are attractive materials for light-emitting devices, and the photoluminescence (PL) from QDs can be enhanced near a metal surface due to surface plasmon (SP) resonance. To integrate QDs into metal structures, QD/poly(methyl methacrylate) (PMMA) composite thin films are generally used. However, it has been reported that QDs tend to aggregate in the PMMA matrix. In this study, we fabricated two types of QD/polymer composite thin films with different degrees of QD aggregation by additionally using poly(methyl methacrylate-co-methacrylic acid) (PMMA-co-MA), which is known to prevent QD aggregation. Furthermore, these two types of films were fabricated on Ag films, with the distance between the Ag films and the QDs controlled by Al₂O₃ spacer layers, and the PL enhancement was compared between the two film types. Finally, we reveal that QD aggregation in the polymer matrix significantly affects the PL enhancement. Although the aggregation trends differed between PMMA and PMMA-co-MA, the results suggest a possible increase in the internal quantum efficiency (IQE) in both film types. Full article

In recent years, the research on source-free domain adaptation has received increasing attention and has achieved considerable progress. It can overcome the dependence on source domain data and obtain a target domain model with robust performance on the target domain only by using the source domain model and unlabeled target domain data. However, existing studies typically handle the target domain distribution in a relatively coarse manner and are consistently susceptible to model noise interference. Therefore, we propose a progressive optimization strategy for the target domain distribution, including two parts: inter-category and intra-category. Regarding inter-category, we decide whether to separate category pairs based on the degree of discrepancy between them. Regarding intra-category, we consider whether to aggregate sample pairs based on whether their pairwise similarity—among samples assigned to the same predicted category—is sufficiently high. And as the training progresses, all data will be optimized. Additionally, for some difficult-to-distinguish categories, we propose a screening strategy that fuses source domain and target domain knowledge. We also further optimized the samples belonging to these categories. Our results on three image datasets demonstrate the effectiveness of our method. Full article

The accurate identification of the degree of alkali reactivity of aggregates is crucial for preventing alkali–silica reaction (ASR) damage in concrete. Two siltstones were collected from the main stream and a tributary of the Yarlung Tsangpo River. The alkali reactivity of these siltstones was examined through a combination of the Petrographic Method, the Accelerated Mortar Bar Test (AMBT), and the Chinese Universal Accelerated Mortar Bar Test (CAMBT). The influence of the petrographic characteristics of siltstones on the applicability of two accelerated expansion tests was also evaluated. Results show that both siltstones exhibit blocky textures and contain cryptocrystalline–microcrystalline quartz. However, the major alkali reactive components in the two siltstones show significant differences in morphology, distribution and content, with cryptocrystalline–microcrystalline quartz contents of approximately 20% in FS-1 and 10% in FS-2. This study reveals that the CAMBT identified two siltstones as alkali-reactive, whereas the standard AMBT classified them as non-reactive. The petrographic structures of the siltstones and the microstructural distribution of reactive quartz constituents are the key factors governing their expansion behavior and the applicability of different testing methods. By employing a single-graded aggregate that preserves the original rock fabric, the CAMBT improves the reliability of the detection of alkali reactivity in siltstones. Full article

The existing research on Android malware detection using graph neural networks (GNNs) has largely focused on architectural improvements, while input node feature representations have received less systematic attention. This study adopts a representation-centric approach to enhance function call graph (FCG)-based malware classification through interpretability-driven feature engineering. We propose a dual-level structural feature framework integrating local topological patterns with global graph-level properties. The initial feature set comprises 13 dimensions: five local degree profile (LDP) features and eight global structural features capturing community structure, execution flow, and connectivity patterns. To mitigate the curse of dimensionality, we apply an interpretability-driven selection using integrated gradients (IG), gradient-weighted class activation mapping (GradCAM), and Shapley additive explanations (SHAP), yielding an optimized seven-dimensional subset. Experiments on the MalNet-Tiny benchmark demonstrate that the proposed approach achieves 94.47 ± 0.25% accuracy with jumping knowledge GraphSAGE (JK-GraphSAGE), improving the LDP-only baseline by 0.32 percentage points while reducing feature dimensionality by 46%. The selected features exhibit consistent importance across four GNN architectures and multiple message-passing layers, demonstrating model-agnostic effectiveness. The results reveal that aggregation mechanisms critically influence feature utility, highlighting the necessity of interpretability-guided design for robust malware detection. This work provides a systematic methodology for feature engineering in graph-based security applications. Full article

The plant hormone abscisic acid (ABA) plays an important role in crop growth and development, so it is urgent to establish a simple and sensitive method for the detection of ABA. (1) As one of the most sensitive spectral detection methods, surface-enhanced Raman spectroscopy (SERS) has made some progress in the detection of ABA, but it involved a complicated modification process of noble metal nanoparticles and was time-consuming. (2) In this work, a SERS and (local surface plasmon resonance) LSPR dual-signal aptamer (Apt) sensor based on the aggregation of dispersed (gold nanoparticles) AuNPs and the improved plasmonic coupling with formed SERS was developed and applied to the detection of the plant hormone ABA. Through the specific recognition of Apt and ABA, the prepared crystal violet (CV) and Apt modified AuNPs tended to aggregate in a high concentration salt solution, resulting in changes in LSPR characteristics of the detection system and enhanced SERS intensity of CV signal molecules. Thus, the quantitative relationship between ABA concentration and SERS intensity of signal molecule CV and the degree of absorbance change of AuNPs were established. (3) The linear range detection of SERS was 0.04~40 µM, the detection limit lod (LOD) was 17.6 nM, the linear range detection of LSPR was 0.4~80 µM, and the LOD was 36 nM. (4) The sensor has a good ability to detect ABA in the samples of common plants such as cucumber and tomato and has the characteristics of no chemical bond modification, more reliable detection results, and a universal detection platform. Full article

Cross-domain data collaboration is a core requirement for the intelligent development of critical areas such as the Internet of Vehicles and intelligent transportation systems. In this scenario, vehicles and various sensors deployed roadside continuously generate massive amounts of time-series data, yet this data often forms “data silos” due to privacy regulations and a lack of trust between collaborating entities. Existing integrated schemes combining “Federated Learning + Blockchain” have achieved a certain degree of process traceability and automated payments, but risks of gradient-level privacy leakage persist, and inflexible and delayed incentive mechanisms result in low participation quality. To systematically address these bottlenecks, this paper proposes the Federated Learning with Assured Privacy and Reputation-Driven Incentives (FLARE) architecture, whose core innovation lies in the native integration of cryptographic security and mechanism design theory. It includes the Secure and Faithfully Executed Gradient aggregation (SafeGrad) protocol, which integrates partial homomorphic encryption and zero-knowledge proofs to provide verifiable privacy guarantees for gradient contributions while enabling efficient secure aggregation, defending against inversion attacks at the source; alongside this, it includes the Economy-on-Chain incentive (EconChain) mechanism, which designs an on-chain economic system based on blockchain, achieving precise measurement and sustainable incentivization of training process contributions through fine-grained instant micro-rewards and a dynamic reputation model. Experiments show that, compared to baseline schemes, FLARE can effectively enhance node participation enthusiasm and contribution quality without compromising model accuracy, providing a new paradigm with both strong security and high vitality for the trusted and efficient circulation of data. Full article