Search Results (2,684)

Trajectory prediction is a critical component of autonomous driving decision-making systems, directly impacting driving safety and traffic efficiency. Despite advancements, existing reviews exhibit limitations in timeliness, classification frameworks, and challenge analysis. This paper systematically reviews multi-agent trajectory prediction technologies, focusing on generating future position sequences from historical trajectories, high-precision maps, and scene context. We propose a multi-dimensional classification framework integrating input representation, output forms, method paradigms, and interaction modeling. The review comprehensively compares conventional methods and deep learning architectures, including diffusion models and large language models. We further analyze five core challenges: complex interactions, rule and map dependence, long-term prediction errors, extreme-scene generalization, and real-time constraints. Finally, interdisciplinary solutions are prospectively explored. Full article

Grout propagation is a critical aspect of fracture grouting. This study investigated grout propagation at fracture intersections under flowing conditions using a simplified two-dimensional (2D) fracture network. Transparent soil technology was employed to simulate the porous filling material within the fractures. The results showed that the penetration velocity of grout decreased significantly after passing through an intersection, and the velocity in the main fracture was consistently higher than that in the branch fractures. In the unfilled fracture network, the diffusion ratio between branch and main fractures ranged from 0.35 to 0.88, whereas after filling, it ranged from 0.71 to 0.86. For each intersection, the ratio of grout length in the downstream branch to that in the main fracture (RDM) was positively correlated with branch width. This trend was especially evident in unfilled fractures, whereas in filled fractures, the increase in RDM was much less pronounced. Regarding the upstream ratio (RUM), it was consistently lower than RDM. RUM increased with branch width in unfilled fractures but decreased in filled fractures. Additionally, higher fluid velocity amplified these anisotropic propagation behaviors. Based on the simplified filled fracture model, it was concluded that porous filling materials reduce permeability differences between fractures with different aperture widths. Furthermore, increased flow rate intensified the anisotropic diffusion of grout. This study provides valuable insight into the mechanism of anisotropic grout propagation and offers guidance for engineering grouting applications. Full article

In order to obtain a gradient coating with excellent performance, novel titanium-modified plasma nitriding was primarily used as a support layer for the PVD coating of 38CrMoAl steel. The samples were subjected to titanium-modified plasma nitriding by placing sponge titanium around the samples, resulting in a thicker ductile diffusion layer and a thinner and denser compound layer. The research results showed that this thinner, denser compound layer formed by titanium-modified plasma nitriding provides stronger support for the AlCrN coating and thus bring about better performance compared to a conventional plasma nitrided layer, with the adhesion strength increasing from 16.8 N to 29.4 N, which is 42.8% higher than the conventional PN compound layer; the surface hardness increasing from 3650 HV_0.05 to 3780 HV_0.05; the friction coefficient and wear rate reducing from 0.64 and 5.4849 × 10⁻⁶ mm³/(N·m) to 0.61 and 2.3060 × 10⁻⁶ mm³/(N·m), respectively; and the wear performance improving by 137.85%. Additionally, the corrosion potential increased from −979.2 mV to −711.51 mV, and the value of impedance increased from 1.5515 × 10⁴ Ω·cm² to 9.4518 × 10⁴ Ω·cm², resulting in a significant improvement in corrosion resistance. In all, the novel support layer by titanium-modified plasma nitriding can provide much better support for AlCrN coating and thus bring about excellent enhanced performances, including adhesion strength and wear and corrosion resistance. Therefore, it is of great value in the PVD coating field, and it can provide valuable insights into gradient coating technology. Full article

Image datasets with imbalanced sampling, masking, missing and noise brought challenges to the development of an intelligent agricultural monitoring system. To tackle these issues, this paper proposes a cloud-based, multi-model integrated intelligent monitoring vision platform for agricultural greenhouse crops (named the CloudCropFuture platform), complete with algorithmic APIs, facilitating streamlined data-driven decision-making. For the CloudCropFuture platform, we first propose an image augmentation technology that employs an improved diffusion model to rectify deficiencies in image data, thereby enhancing the accuracy of agricultural image analysis. Experimental results demonstrate that on datasets enhanced by this method, the average precision of multiple YOLO models is improved by 5.6%. Then, a multi-level growth monitoring platform is introduced, integrating enhanced YOLOv11-based image models for more accurate and efficient crop observation. Furthermore, an intelligent model base comprising multiple integrated detection methods is established for assessing agricultural pests, maturity, and quality, leveraging the enhanced performance of vision models. CloudCropFuture offers a holistic solution for intelligent monitoring in agricultural greenhouses throughout the entire crop growth cycle. Through model verification and application across various greenhouse crops, this work has demonstrated the ability of the intelligent platform to provide reliable and stable monitoring performance. This research paves the way for the future development of agricultural technologies that can adapt to the dynamic and challenging conditions of modern farming practices. Full article

With the impact of fossil energy on the climate environment and the development of energy technologies, new energy vehicles, represented by electric cars, have begun to receive increasing attention and emphasis. The rapid proliferation of public charging infrastructure for NEVs has concurrently influenced traditional petrol station networks, creating measurable disparities in their spatial distributions that warrant systematic investigation. This research examines Wenzhou City, China, as a representative case area, employing multi-source Point of Interest (POI) data and spatial analysis models to analyse differential characteristics in spatial layout accessibility, service equity, and underlying driving mechanisms between public electric vehicle charging stations (EV) and traditional gas stations (GS). The findings reveal that public electric vehicle charging stations exhibit a pronounced “single-centre concentration with weak multi-centre linkage” spatial configuration, heavily reliant on dual-core drivers of population density and economic activity. This results in marked service accessibility declines in peripheral areas, resembling a cliff-like drop, and a relatively low spatial equity index. In contrast, traditional gas stations demonstrate a “core-axis linkage” diffusion pattern with strong coupling to urban road networks, showing gradient attenuation in service coverage efficiency along transportation arteries, fewer suburban service gaps, and more gradual accessibility reductions. Location entropy analysis further indicates that charging station deployment shows significant capital-oriented tendencies, with certain areas exhibiting paradoxical “excess facilities” phenomena, while gas station distribution aligns more closely with road network topology and transportation demand dynamics. Furthermore, the layout characteristics of public charging stations feature a more complex and diverse range of land use types, while traditional gas stations have a strong dependence on industrial land. This research elucidates the spatial distribution patterns of emerging and legacy energy infrastructure in the survey regions, providing critical empirical evidence for optimising energy infrastructure allocation and facilitating coordinated transportation system transitions. The findings also offer practical insights for the construction of energy supply facilities in urban development frameworks, holding substantial reference value for achieving sustainable urban spatial governance. Full article

3D bioprinting has emerged as a key tool in tissue engineering by facilitating the creation of customized scaffolds with properties tailored to specific needs. Among the design parameters, porosity stands out as a determining factor, as it directly influences critical mechanical and biological properties such as nutrient diffusion, cell adhesion and structural integrity. This review comprehensively analyses the state of the art in scaffold design, emphasizing how porosity-related parameters such as pore size, geometry, distribution and interconnectivity affect cellular behavior and mechanical performance. It also addresses advances in manufacturing methods, such as additive manufacturing and computer-aided design (CAD), which allow the development of scaffolds with hierarchical structures and controlled porosity. In addition, the use of computational modelling, in particular finite element analysis (FEA), as an essential predictive tool to optimize the design of scaffolds under physiological conditions is highlighted. This narrative review analyzed 112 core articles retrieved primarily from Scopus (2014–2025) to provide a comprehensive and up-to-date synthesis. Despite recent progress, significant challenges persist, including the lack of standardized methodologies for characterizing and comparing porosity parameters across different studies. This review identifies these gaps and suggests future research directions, such as the development of unified characterization and classification systems and the enhancement of nanoscale resolution in bioprinting technologies. By integrating structural design with biological functionality, this review underscores the transformative potential of porosity research applied to 3D bioprinting, positioning it as a key strategy to meet current clinical needs in tissue engineering. Full article

With the rapid development of big data and artificial intelligence, the problem of image privacy leakage has become increasingly prominent, especially for images containing sensitive information such as faces, which poses a higher security risk. In order to improve the security and efficiency of image privacy protection, this paper proposes an image encryption scheme that integrates face detection and multi-level encryption technology. Specifically, a multi-task convolutional neural network (MTCNN) is used to accurately extract the face area to ensure accurate positioning and high processing efficiency. For the extracted face area, a hierarchical encryption framework is constructed using chaotic systems, lightweight block permutations, RNA cryptographic systems, and bit diffusion, which increases data complexity and unpredictability. In addition, a key update mechanism based on dynamic feedback is introduced to enable the key to change in real time during the encryption process, effectively resisting known plaintext and chosen plaintext attacks. Experimental results show that the scheme performs well in terms of encryption security, robustness, computational efficiency, and image reconstruction quality. This study provides a practical and effective solution for the secure storage and transmission of sensitive face images, and provides valuable support for image privacy protection in intelligent systems. Full article

Breakthrough advances in artificial intelligence (AI) are propelling de novo protein design past the boundaries of natural evolution, making it possible to engineer proteins with entirely novel structures and functions. Benefiting from iterative improvements in machine learning algorithms, AI-driven de novo strategies have overcome traditional reliance on natural templates. These approaches autonomously optimize catalytic sites and overall stability, significantly enhancing enzyme performance and applicability. Generative models, including large language models and diffusion models, can rapidly produce novel protein structures with specialized functions, offering innovative technological paths for biomolecule development. This review systematically discusses recent key developments and representative examples of AI applications in enzyme engineering and design. We highlight a fundamental shift from traditional “structure-based function analysis” to a new paradigm of “function-driven structural innovation.” Furthermore, we comprehensively evaluate current challenges in AI-driven protein engineering and suggest promising future directions. Full article

The synergistic effect of using D2EHPA and TOPO together to enhance the extraction of samarium(III) from aqueous media via emulsion liquid membrane (ELM) technology was explored. D2EHPA in binary mixtures with TBP and in ternary mixtures with TOPO and TBP was also tested. Among the tested extractants, a binary mixture of 0.1% (w/w) D2EHPA and 0.025% (w/w) TOPO achieved 100% samarium(III) extraction at a low loading. This mixture outperformed D2EHPA-TBP and other systems because D2EHPA strongly binds to Sm(III) ions, while TOPO increases the solubility and transport efficiency of metal complexes. Additionally, process factors that optimize performance and minimize emulsion breakage were examined. Key insights for successfully implementing the process include the following: 5 min emulsification with 0.75% Span 80 in kerosene at pH 6.7 (natural), 250 rpm stirring, a 1:1 internal/membrane phase volume ratio, a 20:200 treatment ratio, and a 0.2 N HNO₃ stripping agent. These insights produced stable, fine droplets, enabling complete recovery and rapid carrier regeneration without emulsion breakdown. Extraction kinetics accelerate with temperature up to 35 °C but declined above this limit due to emulsion rupture. The activation energy was calculated to be 33.13 kJ/mol using pseudo-first-order rate constants. This suggests that the process is diffusion-controlled rather than chemically controlled. Performance decreases with Sm(III) feed concentrations greater than 200 mg/L and in high-salt matrices (Na₂SO₄ > NaCl > KNO₃). Integrating these parameters yields a scalable, low-loading ELM framework capable of achieving complete Sm(III) separation with minimal breakage. Full article

The present study investigates passive microdroplet generation in a T-junction microchannel using microscopic observations, microscale particle image velocimetry (Micro-PIV) visualization, and lattice Boltzmann simulations. The key flow regimes, i.e., dripping, threading, and parallel flow, are characterized by analyzing the balance between hydrodynamic forces and surface tension, revealing the critical role of the flow rate ratio of the continuous to dispersed fluids in regime transitions. Micro-PIV visualizes velocity fields and vortex structures during droplet formation, while a lattice Boltzmann model with wetting boundary conditions captures interface deformation and flow dynamics, showing good agreement with experiments in the dripping and threading regimes but discrepancies in the parallel flow regime due to neglected surface roughness. The present experimental results highlight non-monotonic trends in the maximum head interface and breakup positions of the dispersed fluid under various flow rates, reflecting the competition between the squeezing and shearing forces of the continuous fluid and the hydrodynamic and surface tension forces of the dispersed fluid. Quantitative analysis shows that the droplet size increases with the flow rate of continuous fluid but decreases with the flow rate of dispersed fluid, while generation frequency rises monotonically with the flow rate of dispersed fluid. The dimensionless droplet length correlates with the flow rate ratio, enabling tunable control over droplet size and flow regimes. This work enhances understanding of T-junction microdroplet generation mechanisms, offering insights for applications in precision biology, material fabrication, and drug delivery. Full article

As China advances toward its 2060 carbon neutrality goal, the electrification of inland waterway shipping has emerged as a strategic pathway for reducing emissions. This study constructs a 2025–2060 dynamic material flow analysis framework that integrates three core dimensions: (1) all-electric ships (AES) diffusion, estimated via a GDP-elasticity model and carbon emission accounting; (2) battery technology evolution, including lithium iron phosphate and solid-state batteries; and (3) recycling system improvements, incorporating direct recycling, cascade utilization, and metallurgical processes. The research sets up three AES penetration scenarios, two battery technologies, and three recycling technology improvement scenarios, resulting in seven combination scenarios for analysis. Through multi-scenario simulations, it reveals synergistic pathways for resource security and decarbonization goals. Key findings include that to meet carbon reduction targets, AES penetration in inland shipping must reach 25.36% by 2060, corresponding to cumulative new ship constructions of 51.5–79.9k units, with total lithium demand ranging from 49.1–95.9 kt, and recycling potential reaching 5.4–25.2 kt. Results also reveal that under current allocation assumptions, the AES sector may face lithium shortages between 2047 and 2057 unless recycling rates improve or electrification pathways are optimized. The work innovatively links battery tech dynamics and recycling optimization for China’s inland shipping and provides actionable guidance for balancing decarbonization and lithium resource security. Full article

The Portevin-Le Chatelier (PLC) effect has been studied for many decades, yet the influence of testing modes has received limited attention. In the past 20 years, it has become increasingly recognized that the stiffness of the testing machine can significantly affect the occurrence of jerky flow, particularly the serrations observed during tensile tests. This study addresses this issue by conducting tests on the Al-Mg alloy AA5083H111, which contains a substantial amount of diffusible magnesium in solid solution and exhibits dynamic strain aging, resulting in a pronounced PLC effect. Both electromechanical and servohydraulic testing machines were used in the tests; these machines differ in stiffness and control technology for applied strain rates. The study also explored different control modes, including stroke control for both machines and true strain control for the servohydraulic machine. The findings indicate that machine stiffness has a moderate effect on material behavior, and no single machine or testing mode can precisely control the strain rate in the sample during the PLC effect. However, it was noted that true strain rate control using a servohydraulic machine comes closest to accurately reflecting the material’s behavior during jerky flow. Full article

Cholangiocarcinoma is a rare, highly aggressive malignancy of the hepatobiliary tract with poor prognosis, often diagnosed at advanced stages when curative surgical resection is not feasible. Management increasingly relies on advanced endoscopic interventions to address malignant biliary obstruction and improve clinical outcomes. Beyond conventional biliary stenting, adjunctive endoscopic ablation therapies have emerged as promising strategies to improve both stent patency and survival. This review comprehensively examines the evolving role of radiofrequency ablation and photodynamic therapy in the treatment of unresectable cholangiocarcinoma. Radiofrequency ablation utilizes localized thermal energy to induce coagulative tumor necrosis and offers advantages including procedural simplicity, favorable safety profile, and cost-effectiveness; however, its efficacy may be limited by tumor size, location, and proximity to critical structures. In contrast, photodynamic therapy employs light-activated photosensitizers to selectively induce cytotoxicity in malignant tissue, demonstrating superior outcomes in prolonging both stent patency and overall survival across multiple studies and meta-analyses. Photodynamic therapy’s ability to treat more diffuse and peripheral lesions represents an important advantage, though its use is limited by photosensitivity reactions and shallow tissue penetration. Ultimately, endoscopic ablation therapies represent valuable adjunctive options in the multidisciplinary care of patients with unresectable cholangiocarcinoma. As technological advances continue and more comparative data emerge, optimized patient selection and individualized integration of these therapies hold potential to significantly improve outcomes in this challenging malignancy. Full article

The rapid dissipation of soft metal lubricants would deteriorate the self-lubricating properties of the coatings at elevated temperatures. In this study, the core-shell structured Mo@Ag@Ni particles were prepared via electroless plating to suppress the rapid dissipation of Ag and facilitate tribochemical reactions at high temperatures. The NiCrAlY-Mo@Ag@Ni composite coating was sprayed on the substrate of Inconel 718 alloy using atmospheric plasma spraying technology. The results of this study show that the structural design of Mo@Ag@Ni can enhance the bonding strength of the particle interface, resulting in a high microhardness of approximately 332.2 HV. During high-temperature friction tests, Mo@Ag@Ni can provide excellent tribological properties by promoting the silver molybdate formation on the worn surface. At 800 °C, the friction coefficient and wear rate are only about 0.32 and 1.58 × 10⁻⁵ mm³N⁻¹m⁻¹, respectively. Moreover, the Ni shell layer can inhibit the rapid diffusion of Ag and provide sufficient Ag₂O to maintain the continuity of Ag₂MoO₄ lubricating film, which endows the coating with a longer lubrication life. Over multi-thermal cycles, the friction coefficient and wear rate constantly maintain at about 0.3 and 2.5 × 10⁻⁵ mm³N⁻¹m⁻¹, respectively. Full article

Despite high expectations for Fintech growth, its real-world expansion has fallen short due to its inherent complexity. Although Fintech is innovative, its multidimensional nature has made it difficult for companies to develop effective, tailored solutions for its diverse user groups. To foster the development of effective and practical Fintech solutions that can expand the user base, a novel and integrative approach is required. Therefore, this study aims to explore specific solutions to enhance Fintech use by holistically combining and intertwining various attributes. Based on the diffusion of innovation theory and the information systems success model, we propose a conceptual Fintech model consisting of three dimensions: innovation, financial service, and information technology. To investigate this model, we adopt fuzzy-set qualitative comparative analysis (fsQCA), a set-theoretic method suited to identifying combinations of Fintech attributes that lead to specific outcomes. The results reveal that the configurations of Fintech attributes leading to high Fintech use differ across four user groups: Infrequent users, Lurkers, Task-driven users, and Power users. The findings also show that information technology plays multifaceted roles depending on its combination with other Fintech attributes. This study explains the interdependencies among Fintech attributes and their combined effects on Fintech use, offering deeper insights into Fintech research through a configurational lens. Full article