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AbstractRoad network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e. travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography. [ABSTRACT FROM AUTHOR]

Exploring and controlling surface tension‐driven phenomena in liquid metals may lead to unprecedented possibilities for next‐generation microfluidics, electronics, catalysis, and materials synthesis. In pursuit of these goals, the impact of minor constituents within liquid alloys is largely overlooked. Herein, it is showed that the presence of a fraction of solute metals such as tin, bismuth, and zinc in liquid gallium can significantly influence their electrocapillarity and electrochemistry. The instability‐driven fractal formation of liquid alloy droplets is investigated with different solutes and reveals the formation of distinctive non‐branched droplets, unstable fractals, and stable fractal modes under controlled voltage and alkaline solution conditions. In their individually unique fractal morphology diagrams, different liquid alloys demonstrate significantly shifted voltage thresholds in transition between the three fractal modes, depending on the choice of the solute metal. Surface tension measurements, cycle voltammetry and surface compositional characterizations provide strong evidence that the minor alloy components drastically alter the surface tension, surface electrochemical oxidation, and oxide dissolution processes that govern the droplet deformation and instability dynamics. The findings that minor components are able to regulate liquid alloys' surface tensions, surface element distributions and electrochemical activities offer great promises for harnessing the tunability and functionality of liquid metals. [ABSTRACT FROM AUTHOR]

Liquid metals provide new dimensions for controlling and governing of reactions. The concept of freely moving metal solutes in liquid metals can be potentially used for enhancing and tuning interfacial reactions. In this work, zinc (Zn), as the solute metal is shown that, can move to the interface, enriching the interfacial area for the efficient synthesis of highly crystalline and porous zeolitic imidazolate framework‐8 (ZIF‐8). The highest rate of reaction is illustrated, and the best quality ZIF‐8, are obtained when a eutectic liquid metal system of Zn with gallium (Ga), containing 3.6 wt.% of Zn, is implemented. It is computationally shown that a combination of Ga activation and freely moving Zn leads to the highest reaction rate and better coordination with organic linkers. The comparisons with solid samples and non‐eutectic systems of Ga liquid demonstrate the advantages of eutectic mix. The work can be expanded to a variety of future interfacial reactions of zeolite imidazole frameworks for commercial scale up. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSWith the development of geographic information services, applications based on map data are increasing. Internet and mobile communication technologies have brought great convenience to the distribution, sharing, and acquisition of map data. Unfortunately, it also brings great challenges to protect the security and privacy of confidential map data. Although several encryption algorithms have been developed for map data, most of the existing methods are simple extensions of classical encryption algorithms for text data in cryptography, ignoring special structures and characteristics of geospatial data. Existing methods mainly take the entire map or layers as encryption units, which makes the encryption efficiency for large-scale map data low and limits the ability to perform local and incremental encryption for local areas, making it still challenging to meet the needs of map data in applications, such as autonomous driving. To solve the limitations, a local encryption approach for large-scale vector map data is proposed in this paper considering the structure characteristics of map data. Experiments on map data of different types (including points, lines, and polygons) demonstrate the effectiveness of the proposed method. With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.A local encryption approach for vector map data is proposed, which can be applied to large-scale vector map data and support localized encryption/decryption for user-interested areas instead of the whole map data.The proposed method estimates different encryption parameters for different units which could significantly enhance the resistance to attacks and naturally supports parallel computing.With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.The method has great value and potential for ensuring map data security in ubiquitous GIS-based applications, such as map data sharing and dynamic updating in automatic driving and crowdsourced mapping. [ABSTRACT FROM AUTHOR]

Crystallization of alloys from a molten state is a fundamental process underpinning metallurgy. Here the direct imaging of an intermetallic precipitation reaction at equilibrium in a liquid‐metal environment is demonstrated. It is shown that the outer layers of a solidified intermetallic are surprisingly unstable to the depths of several nanometers, fluctuating between a crystalline and a liquid state. This effect, referred to herein as crystal interface liquefaction, is observed at remarkably low temperatures and results in highly unstable crystal interfaces at temperatures exceeding 200 K below the bulk melting point of the solid. In general, any liquefaction process would occur at or close to the formal melting point of a solid, thus differentiating the observed liquefaction phenomenon from other processes such as surface pre‐melting or conventional bulk melting. Crystal interface liquefaction is observed in a variety of binary alloy systems and as such, the findings may impact the understanding of crystallization and solidification processes in metallic systems and alloys more generally. [ABSTRACT FROM AUTHOR]

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning. [ABSTRACT FROM AUTHOR]

Low‐temperature polycrystalline silicon (LTPS) thin‐film transistor devices can be used as core components of pixel driving circuits for active matrix organic light‐emitting displays （AMOLED）. Poly silicon (P‐Si) film and device performance directly affects the display effect. This paper investigates polysilicon grain boundary reduction for LTPS devices on PI substrates, exploring the effects of grain boundary reduction on LTPS devices and image sticking. Reduction of polysilicon grain boundaries through optimization of a‐Si deposition process, a‐Si two‐step deposition and excimer laser annealing (ELA) doublescan process, ELA double‐scan process reduces grain boundaries by 67 %. We prepared LTPS devices with thinner gate interlayers (GI) based on P‐Si grain boundary reduction, LTPS devices with positive threshold voltage bias and increased mobility, AMOLED displays with thinner GI can have 23% better image sticking. [ABSTRACT FROM AUTHOR]

To efficiently establish a finite element model that is highly compatible with the intricate microstructure of foam materials, this study carries out simulation and experimental investigations into open cell foams and explores their compression behavior assuming isotropy. Using an in-house algorithm, we optimize the traditional power diagram model, which is widely used in foam finite element simulation. Specifically, a parameterized geometric foam model is developed based on the centroidal and capacity constrained power diagram (CCCPD), which accurately reflects the internal meso-structure of foam materials. The optimized foam model can restore the typical deformation process of foams under compressive load, thus addressing the limitations of existing foam models. The accuracy of the optimized model is validated by conducting uniaxial Quasi-static compression experiments on foamed thermoplastic polyurethane (TPU) material and comparing the experimental results of the 3D printing model with the simulation results of the optimized model. The improved foam model serves as a basis for future research on open cell foam materials. [ABSTRACT FROM AUTHOR]

Silicon carbide fiber reinforced silicon carbide matrix (SiCf/SiC) composite is the key cladding material of nuclear fuel, which determines the safety and reliability of nuclear fuel storage and transportation. The replacement of its storage and transportation scenario needs to be completed by the manipulator, but the application of SiCf/SiC wear, fracture, and nuclear leakage in the snatching process of brittle-flexible-rigid contact in the irradiation environment has been seriously restricted due to unclear understanding of the damage mechanism. Therefore, the effects of irradiation dose and clamping load on the friction characteristics of the contact interface between SiCf/SiC clad tube are studied in this paper, and the effects of irradiation parameters and clamping force on the static friction coefficient of the contact interface between the clad tube and flexible nitrile are obtained. Based on the Greenwood-Williamson tribological model, a numerical model of the shape and structure of the contact micro-convex at the micro-scale of the clamping interface is constructed by introducing the multi-surface integral, and finally verified by experiments. The research results show that there is a unique "Irradiation suppression zone" under the clamping condition of SiCf/SiC cladding tube under the nuclear irradiation environment, and the growth of static friction coefficient slows down until stagnates after irradiation reaches a certain extent (600 kGy), and there will be a decline when the irradiation dose continues to increase, among which the clamping force of 15.2 N within the irradiation interval of 1,000 kGy can meet the safety of nuclear environment operation. The results of this paper can provide an important theoretical basis and application guidance for the safe operation of SiCf/SiC cladding tubes in the storage and transportation clamping process. [ABSTRACT FROM AUTHOR]

Electrocrystallization is a promising method for controlled charge‐transfer complex (CTC) deposition on microfabricated electrodes for gas sensing applications. However, there remains a gap in our understanding of CTC electrodeposition. In this study, we focus on investigating the electrocrystallization of cobalt tetracyanoquinodimethane (Co‐TCNQ) on a microdisk electrode to elucidate and control the process. Leveraging the microelectrode technique, we conduct steady‐state measurements to observe nucleation and crystal growth dynamics, particularly in the early stages of electrocrystallization. We use cyclic voltammetry and chronoamperometry to examine Co‐TCNQ electrocrystallization under various electrolytic conditions. We identify electrocrystallization kinetics, ranging from electrokinetic to diffusion‐limited growth, governing the nucleation and growth of Co‐TCNQ crystals. Notably, we pinpoint the applied overpotential and precursor concentration range necessary for a single nucleation site on the microelectrode. Moreover, we demonstrate control over crystal orientation and morphology. Our findings reveal a nonclassical growth pathway for Co‐TCNQ crystals characterized by oriented attachment of small crystallites along the conductive long axis. Importantly, electrodeposited Co‐TCNQ on patterned microelectrodes exhibits selective sensing capabilities for nitrogen dioxide gas. Overall, this study sheds light on CTC electrodeposition through a proof‐of‐concept demonstration involving Co‐TCNQ electrodeposition on microelectrodes, presenting potential applications across diverse materials. [ABSTRACT FROM AUTHOR]

Gas sensors are of great interest to portable and miniaturized sensing technologies with applications ranging from air quality monitoring to explosive detection and medical diagnostics, but the existing chemiresistive NO2 sensors still suffer from issues such as poor sensitivity, high operating temperature, and slow recovery. Herein, a high‐performance NO2 sensors based on all‐inorganic perovskite nanocrystals (PNCs) is reported, achieving room temperature operation with ultra‐fast response and recovery time. After tailoring the halide composition, superior sensitivity of ≈67 at 8 ppm NO2 is obtained in CsPbI2Br PNC sensors with a detection level down to 2 ppb, which outperforms other nanomaterial‐based NO2 sensors. Furthermore, the remarkable optoelectronic properties of such PNCs enable dual‐mode operation, i.e., chemiresistive and chemioptical sensing, presenting a new and versatile platform for advancing high‐performance, point‐of‐care NO2 detection technologies. [ABSTRACT FROM AUTHOR]