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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]

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]

In nature, snowflake ice crystals arrange themselves into diverse symmetrical six-sided structures. We show an analogy of this when zinc (Zn) dissolves and crystallizes in liquid gallium (Ga). The lowmelting-temperature Ga is used as a “metallic solvent” to synthesize a range of flake-like Zn crystals. We extract these metallic crystals from the liquid metal solvent by reducing its surface tension using a combination of electrocapillary modulation and vacuum filtration. The liquid metal–grown crystals feature high morphological diversity and persistent symmetry. The concept is expanded to other single and binary metal solutes and Ga-based solvents, with the growth mechanisms elucidated through ab initio simulation of interfacial stability. This strategy offers general routes for creating highly crystalline, shape-controlled metallic or multimetallic fine structures from liquid metal solvents. [ABSTRACT FROM AUTHOR]

Ever since the introduction of thrombolysis and the subsequent expansion of endovascular treatments for acute ischemic stroke, it remains to be identified why the actual outcomes are less favorable despite recanalization. Here, by high spatio-temporal resolution imaging of capillary circulation in mice, we introduce the pathological phenomenon of dynamic flow stalls in cerebral capillaries, occurring persistently in salvageable penumbra after reperfusion. These stalls, which are different from permanent cellular plugs of no-reflow, were temporarily and repetitively occurring in the capillary network, impairing the overall circulation like small focal traffic jams. In vivo microscopy in the ischemic penumbra revealed leukocytes traveling slowly through capillary lumen or getting stuck, while red blood cell flow was being disturbed in the neighboring segments under reperfused conditions. Stall dynamics could be modulated, by injection of an anti-Ly6G antibody specifically targeting neutrophils. Decreased number and duration of stalls were associated with improvement in penumbral blood flow within 2–24 h after reperfusion along with increased capillary oxygenation, decreased cellular damage and improved functional outcome. Thereby, dynamic microcirculatory stall phenomenon can be a contributing factor to ongoing penumbral injury and is a potential hyperacute mechanism adding on previous observations of detrimental effects of activated neutrophils in ischemic stroke. [ABSTRACT FROM AUTHOR]

Conventional color Doppler ultrasound imaging suffers from mutual frequency cancellation when applied to quantify axial blood flow velocities in the rodent brain where inverse flows exist within an ultrasound measurement voxel. Here, we report an improved color Doppler-based functional ultrasound imaging method (iCD-fUS) for axial blood flow velocity imaging of the rodent brain. By applying a directional filter and high frequency noise thresholding, iCD-fUS is able to accurately quantify blood flow velocities within the brain as validated with the ultrasound localization microscopy velocimetry method. We show that iCD-fUS is able to image and resolve the directional axial blood flow velocity throughout the entire coronal section of the brain at a temporal frame rate of up to 10 Hz with a spatial resolution of ~100 $\mu \text{m}$. We further applied iCD-fUS to image the axial blood flow velocity change in response to whisker stimulation in an awake mouse, showing its potential for studying brain activation. With these capabilities, iCD-fUS provides a powerful, quantitative tool for in vivo chronic research. [ABSTRACT FROM AUTHOR]

A high‐speed, contrast‐free, quantitative ultrasound velocimetry (vUS) for blood flow velocity imaging throughout the rodent brain is developed based on the normalized first‐order temporal autocorrelation function of the ultrasound field signal. vUS is able to quantify blood flow velocity in both transverse and axial directions, and is validated with numerical simulation, phantom experiments, and in vivo measurements. The functional imaging ability of vUS is demonstrated by monitoring the blood flow velocity changes during whisker stimulation in awake mice. Compared to existing Power‐Doppler‐ and Color‐Doppler‐based functional ultrasound imaging techniques, vUS shows quantitative accuracy in estimating both axial and transverse flow speeds and resistance to acoustic attenuation and high‐frequency noise. [ABSTRACT FROM AUTHOR]

AbstractRecognizing common travel paths of crowds in a road network is valuable for understanding human mobility patterns and developing intelligent ride-sharing services. To achieve this, it is critical to measure the similarity of their trajectories. Although many measures have been proposed in the past decades, they often ignore movement consistency, exhibit one or more deficiencies in the face of noise and misaligned trajectories, or require extra parameters to tune predictions. In this paper, we propose an improved similarity measure called the directed segment path distance (DSPD), which considers the spatial proximity and movement consistency of trajectories. By integrating the spatial proximity distance and moving direction similarity between trajectories, the DSPD is a competitive parameter-free similarity measure that can effectively distinguish trajectories with different movement characteristics. To verify the effectiveness of the DSPD, we conducted a quantitative comparative study between the DSPD measure and 11 state-of-the-art trajectory similarity measures on six simulated trajectory datasets and applied the DSPD to two typical application scenarios: trajectory clustering for road network generation and retrieving common trajectories for ride-sharing path planning. The results demonstrate the effectiveness, robustness, and superiority of the DSPD and its great potential in trajectory search, clustering, and classification. [ABSTRACT FROM AUTHOR]

Filament winding is a technique that precisely places continuous fibers along a preset path on a rotating mandrel. However, traditional 2D winding paths based on the original mandrel surface ignore the effect of thickness stacking on doffing points. To physically simulate the build-up of tows on the mandrel, a novel numerical method, including the mesh-based directional projection (MDP) and normal adaptive convex helix (NACH) algorithms, is proposed. The MDP algorithm is employed to sort out tow overlaps in the thickness direction, and is 200 times more efficient than the existing algorithms, such as the backward search algorithm. Subsequently, the "cliffs" generated in the overlap removal process are smoothed by the NACH algorithm. Furthermore, the advantages of these two algorithms in terms of efficiency, reliability, and universality are discussed in detail. In summary, a method for developing a physical-based 3D path and ever-updating workpiece contour of pressure vessels is proposed, which serves as an essential reference for high-precision winding and workpiece shape optimization. [ABSTRACT FROM AUTHOR]

• A CZM is developed to describe bridging fibers and rate-dependent interfaces. • Constitutive expressions about viscoelasticity are established by GMM and CZM. • New algorithms are developed to evaluate the pseudo-viscoelasticity of fibers. To investigate the mode-I failure of viscoelastic interfaces, novel tensile tests are conducted on rubber/fiber sheets with loading rates of 0.1–500 mm/min. Based on these tests, a new tri-linear cohesive zone model (CZM) is proposed. Different from the traditional CZM, the developed CZM is able to model the fiber bridging behavior as well as describe the loading rate dependence of viscoelastic interfaces. The previous CZM does not have this advantageous feature. In addition, the tensile experiments reveal that the bridging fibers exhibit pseudo-viscoelasticity. This refers to the fact that the mechanical properties of carbon fibers without viscoelasticity exhibit a loading rate dependence. To explain this, the concept of the "fiber bridging degree" is proposed, and a quantitative analysis is conducted using image processing techniques. The results indicate that the fiber bridging degree increases from 109.5 at a loading rate of 0.1 mm/min to 1747.5 at 500 mm/min. Furthermore, by combining the tri-linear CZM with a rheological model, a new constitutive model is derived, which can successfully simulate the mode-I failure of viscoelastic interfaces. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) allows label-free imaging of red blood cell (RBC) flux within capillaries with high spatio-temporal resolution. In this study, we utilized time-series OCT-angiography to demonstrate interruptions in capillary RBC flux in mouse brain in vivo. We noticed ∼7.5% of ∼200 capillaries had at least one stall in awake mice with chronic windows during a 9-min recording. At any instant, ∼0.45% of capillaries were stalled. Average stall duration was ∼15 s but could last over 1 min. Stalls were more frequent and longer lasting in acute window preparations. Further, isoflurane anesthesia in chronic preparations caused an increase in the number of stalls. In repeated imaging, the same segments had a tendency to stall again over a period of one month. In awake animals, functional stimulation decreased the observance of stalling events. Stalling segments were located distally, away from the first couple of arteriolar-side capillary branches and their average RBC and plasma velocities were lower than nonstalling capillaries within the same region. This first systematic analysis of capillary RBC stalls in the brain, enabled by rapid and continuous volumetric imaging of capillaries with OCT-angiography, will lead to future investigations of the potential role of stalling events in cerebral pathologies. [ABSTRACT FROM AUTHOR]

Recognition of building groups is a critical step in building generalization. To find building groups, various approaches have been developed based on the principles of grouping (or the Gestalt laws of grouping), and the effectiveness of these approaches needs to be evaluated. This study presents a comparative analysis of nine typical such approaches, including three approaches that only consider proximity principle and six approaches that consider multiple grouping principles. Real-life dataset at 1:5000, 1:10,000, and 1:50,000 scales provided by National Geomatics Center of China is used to evaluate the performance of these approaches. Buildings at smaller scales are used to construct the benchmarks to test the grouping results at larger scales, and the adjusted Rand index is adopted to indicate the accuracy of the detected groups. Significant tests (Friedman test and Wilcoxon signed-rank test) are also performed to provide both the overall and pairwise comparisons of these approaches. The results show that (1) the average accuracy of most existing approaches is between 0.3 and 0.5, and the performances of these approaches are significantly different; (2) when only proximity is considered, thebuffer analysis approachperforms significantly better than other approaches; (3) when multiple grouping principles are considered, thelocal constraint-based approachusually performs better than other approaches; (4) existing approaches that consider similarity and/or continuity seldom improve the performance of building grouping. [ABSTRACT FROM AUTHOR]

A novel filament winding virtual tool is proposed to develop the 3D winding paths on the workpiece contour in real-time. The semi-geodesic and constant-wall-thickness (CWT) trajectories are used to generate the centerline of the 3D path. A tow overlaps processing method and the outer-contour expanding algorithm (OEA) are developed to eliminate the tow interference. The convex helix algorithm (CHA) is proposed to smooth the cliff generated in the interference elimination. The OEA and CHA algorithms are considered due to their high computational efficiency as compared to the traditional algorithms, such as the Backward Search algorithm (BSA) and l -BFGS Algorithm. The prospective virtual tool develops a stable winding path, and experimental results have shown that the 3D path does have advantages over the traditional 2D path in terms of high preservation of the tow path and the prevention of the tow slippage. [ABSTRACT FROM AUTHOR]

This study focuses on the influence of driving fluid properties on the mass transfer process of ejector apparatus. A liquid-actuated ejector system is firstly designed and three experimental cases working with different kinds of driving fluids, i.e., gallium-based liquid metal, aqueous NaI solution and water, are successively investigated. The results show that the ejector system working with liquid metal has high-vacuum ejecting capacity and good temperature stability. With a system power consumption of about 50 W, the liquid metal driving ejector obtains a no-entrainment vacuum pressure of 33 Pa which is three orders of magnitudes lower than that of the water driving case. When the gas-suction pressure exceeds 40 kPa, NaI solution driving case reveals the highest entrainment. The reasons for the differences in the ejector performance are later discussed. Based on the experimental results and theoretical analyses, the method of using high density, low viscosity and low vapor pressure working fluid to improve ejector performance is proposed. Other than conventional methods which focus on optimizing the geometrical design and operating parameters of ejectors, the method discussed here puts forward a new perspective to improve ejector performance. Moreover, the ejector system working with liquid metal demonstrates its advantages such as compact system design, oil-free vacuum pumping as well as high temperature operation. [ABSTRACT FROM AUTHOR]

As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization. [ABSTRACT FROM AUTHOR]

We present a noninvasive method of photoacoustic tomography (PAT) for imaging cerebral hemodynamics in awake-moving rats. The wearable PAT (wPAT) system has a size of 15 mm in height and 33 mm in diameter, and a weight of ~8 g (excluding cabling). The wPAT achieved an imaging rate of 3.33 frames/s with a lateral resolution of 243 μm. Animal experiments were designed to show wPAT feasibility for imaging cerebral hemodynamics on awake-moving animals. Results showed that the cerebral oxy-hemoglobin and deoxy-hemoglobin changed significantly in response to hyperoxia; and, after the injection of pentylenetetrazol (PTZ), cerebral blood volume changed faster over time and larger in amplitude for rats in awake-moving state compared with rats under anesthesia. By providing a light-weight, high-resolution technology for in vivo monitoring of cerebral hemodynamics in awake-behaving animals, it will be possible to develop a comprehensive understanding on how activity alters hemodynamics in normal and diseased states. [ABSTRACT FROM AUTHOR]

A fundamental element of exploratory spatial data analysis is the discovery of clusters in a spatial point dataset. When clusters with distinctly different local densities exist, the determination of suitable density level is still an unsolved problem. On that account, an iterative detection and removal method is proposed in this study. In each step of the novel method, there are two stages. In the detection stage, density level is statistically modeled as a significance level controlled by the number and support domain of the points in the dataset, and then a hypothesis test is used to detect the high-density points. In the removal stage, the Delaunay triangulation network is used to construct clusters and support domains for the identified high-density points, and then the high-density points and their support domains are removed from the dataset. The detection and removal operation are iteratively implemented until no high-density points can be detected. Experiments and comparisons show that the proposed method, on the one hand, outperforms four state-of-the-art methods for detecting clusters of complex shapes and diverse densities, and on the other hand, no user-specified parameters are required. In addition, the support domains of clusters are very useful for spatial analysis. [ABSTRACT FROM AUTHOR]

Conventional methods of machine learning have been widely used to generate spatial prediction models because such methods can adaptively learn the mapping relationships among spatial data with limited prior knowledge. However, the direct application of these methods to build a global model without considering spatial heterogeneity cannot accurately describe the local relationships among spatial variables, which might lead to inaccurate predictions. To avoid these shortcomings, we have presented a unified framework for handling spatial heterogeneity by incorporating the geographically weighted scheme into machine learning methods. The proposed framework has the potential to extend the existing models of machine learning for analysing heterogeneous spatial data. Furthermore, geographically weighted support vector regression (GWSVR) has been introduced as an implementation of the proposed framework. Experimental studies on environmental datasets were used to test the ability of model predictions. The results show that the mean absolute percentage error, normalized mean square error, and relative error percentage of the GWSVR model are 0.436, 0.903, and 0.558, respectively, when analysing soil metal chromium (Cr) concentrations and 0.221, 0.287, and 0.206, respectively, when predicting PM2.5 concentrations; these values are lower than those obtained using support vector regression, geographically weighted regression (GWR), and GWR-kriging models. These case studies have proved the validity and feasibility of the proposed framework. [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]

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]

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]

Liquid metal droplets of gallium (Ga) and Ga‐based alloys are traditionally incorporated as deformable additives into soft elastomers to make them conductive. However, such a strategy has not been implemented to develop conductive sponges with real sponge‐like characteristics. Herein, polyurethane‐based sponges with Ga microdroplets embedded inside the polyurethane walls are developed. The liquid phase (at 45 °C) and solid phase (at room temperature) transition of the Ga fillers shows the temperature‐dependent functional variations in the mechanical, thermal, and electrical properties on the prepared composite sponges, which are investigated in detail. Unlike elastomers, the sponge possesses excellent elastic recovery, at ≈90%, and conductivity durability without sacrificing structural integrity. The reversible change of resistivity range is remarkable. When the Ga fillers account for 18% of the total sponge volume, the electrical resistivity varies from infinite values (insulator) under no applied pressure to 39.0 Ω m for the solid phase and 3.8 Ω m for the liquid phase under 386.8 kPa. New opportunities in developing flexible electrically conductive composite sponges with tunable mechanical and electrical properties that can be implemented for a variety of future applications are proposed. [ABSTRACT FROM AUTHOR]

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently. [ABSTRACT FROM AUTHOR]

Heavy metal ions contamination is an ever‐growing threat to human health and ecosystems. Analytic sensing of heavy metal ions is a prerequisite step toward addressing this challenge. Electroanalytical technique is an effective and convenient way to detect heavy metal ions. Especially, stripping voltammetry techniques have been utilized for sensing metal ions at trace levels in different sample matrices. The choice of the working electrode materials plays a decisive role in the precision and sensitivity of stripping voltammetry. Post‐transition metals (including elements in group 12) are elements with low melting points, demonstrating unique properties including amalgamation with heavy metal ions that enhance the performance of stripping voltammetry during the pre‐concentration step, which make them particularly suitable for heavy metal ion sensing. Here, for better understanding the features and advantages of post‐transition metals in stripping voltammetry, eight classes of working electrodes based on zinc (Zn), mercury (Hg), gallium (Ga), indium (In), tin (Sn), lead (Pb), antimony (Sb), and bismuth (Bi), are reviewed, with their efficiency and effectiveness compared and recent progresses discussed. Additionally, liquid metal electrodes based on post‐transition metals and alloys are also presented. This review provides viable directions for future development of heavy metal ion sensors based on post‐transition metals. [ABSTRACT FROM AUTHOR]

Background: The rapid global spread of COVID-19 has seriously impacted people's daily lives and the social economy while also posing a threat to their lives. The analysis of infectious disease transmission is of significant importance for the rational allocation of epidemic prevention and control resources, the management of public health emergencies, and the improvement of future public health systems. Methods: We propose a spatiotemporal COVID-19 transmission model with a neighborhood as an agent unit and an urban spatial network with long and short edge connections. The spreading model includes a network of defined agent attributes, transformation rules, and social relations and a small world network representing agents' social relations. Parameters for each stage are fitted by the Runge-Kutta method combined with the SEIR model. Using the NetLogo development platform, accurate dynamic simulations of the spatial and temporal evolution of the early epidemic were achieved. Results: Experimental results demonstrate that the fitted curves from the four stages agree with actual data, with only a 12.27% difference between the average number of infected agents and the actual number of infected agents after simulating 1 hundred times. Additionally, the model simulates and compares different "city closure" scenarios. The results showed that implementing a 'lockdown' 10 days earlier would lead to the peak number of infections occurring 7 days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections. Discussion: Our methodology emphasizes the crucial role of timely epidemic interventions in curbing the spread of infectious diseases, notably in the predictive assessment and evaluation of lockdown strategies. Furthermore, this approach adeptly forecasts the influence of varying intervention timings on peak infection rates and total case numbers, accurately reflecting real-world virus transmission patterns. This highlights the importance of proactive measures in diminishing epidemic impacts. It furnishes a robust framework, empowering policymakers to refine epidemic response strategies based on a synthesis of predictive modeling and empirical data. [ABSTRACT FROM AUTHOR]

Traffic congestion detection poses challenges in spatiotemporal data mining and intelligent transportation research. Existing studies primarily detect traffic congestion based on the speed estimation of traffic flows. Such detection techniques may overlook the formation of traffic congestion in space and time. This research proposes a density-based approach to moving object clustering that extracts the spatiotemporal extents of traffic congestion in three steps. The first step applies a map-matching strategy to project original trajectory points in a planar space onto a road network space and segments the trajectories into consecutive time windows. In the second step, we statistically detect moving clusters with significantly high-density subject to network constrained clustering. The final third step determines moving clusters indicative of traffic congestion through the analysis of both vehicle speed and time spans. Comparative experiments on both simulated trajectories and the real-life taxi trajectories in Wuchang demonstrate that the proposed method outperforms other methods through quantitative evaluations using three indicators, i.e. the precision, recall and F1 value. The proposed approach can illustrate the spatiotemporal regularities of traffic congestion, which can inform dynamic route planning and network design optimization. [ABSTRACT FROM AUTHOR]

As soft conductive materials with high liquid fluidity, the room‐temperature liquid metal alloys (LMs) offer a superior alternative to the fabrication of flexible electronics. So far, techniques aiming at patterning LMs are seriously limited by the alloy's high surface tension and poor wettability with many substrates. Additionally, LMs based mass production with fast and efficient printing on desired target still encounters tremendous unsolved challenges. Here, a one‐step liquid metal transfer printing method with wide range substrate adaptability, comprising of polymer‐based adhesive glue, its printing machine, the LMs ink, and the soft substrate is presented. It is demonstrated that even on those substrates with weak wettability to LMs, the liquid metal transfer printing still works well to create complex conductive geometries, multilayer circuits, and large‐area conductive patterns with excellent transfer efficiency, facile fabrication process, and remarkable electrical stability, which is beneficial to quickly construct wearable electronics, 3D folding conductive structures, flexible actuators, soft robots, etc. Moreover, its advantages of self‐healing and recyclable ability make the strategy possible to prepare reconfigurable circuits and further reduce the cost of fabrication and environmental pollution. This study suggests an important way for future widespread practices of liquid metal soft functional electronics. A one‐step liquid metal transfer printing method for flexible electronics based on adhesion difference of EGaIn on allied substrates is developed. The EGaIn could be adhered to polymethacrylates glue, but not paper. This fabrication method has five advantages: (1) fast fabrication; (2) excellent electrical stability; (3) simple to fabricate multilayer and large‐area circuits; (4) self‐healing ability; (5) reversion of EGaIn. [ABSTRACT FROM AUTHOR]

In article number 1800265, Rui Gao, Jing Liu, Wei Rao, and co‐workers demonstrate a new kind of one‐step liquid metal transfer printing method with rather wide range substrate adaptability. As illustrated here, a classical Chinese ancient painting (Riverside Scene on Pure Brightness Festival) was thus printed on a picture scroll. With preprinted glue, liquid metal can be selectively patterned on target substrate such as paper to compose various flexible functional electronics. [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]

Gallium and many of its alloys remain in liquid phase across impressively wide temperature ranges. Here such liquid metals are proposed as reaction media for the carbonization of low thermal stability polymeric precursors at high temperatures. Plain and cross-linked polyvinyl alcohol are chosen as representatives of such polymers. We show that due to the immiscibility of organic carbons within the liquid metal phase, these polymers that would otherwise vaporize at elevated temperatures, can function as precursors for the formation of carbonaceous films. The thin polymeric films are placed in an intimate contact with the liquid metal surface before thermal processing and show amorphous to graphitic-like characteristics after carbonization. Graphitic-like properties were obtained when a high melting point graphitization catalyst, such as copper, was co-alloyed. The proposed work can be expanded to explore other metallic elements within the bulk of gallium-based alloys for the carbonization of polymeric precursors at large-scales. Image 1 • Polymers that do not carbonize otherwise can be 'capped' by liquid metals and thereby carbonized. • Gallium-based liquid metals exhibit selective miscibility to non-metallic elements. • Gallium, gallium-indium and gallium-copper were chosen as liquid metal reactors. • Carbon films with amorphous and graphitic characteristics were formed. • Inclusion of copper in liquid metal enhanced the graphitic characteristics of the films. [ABSTRACT FROM AUTHOR]

Functional zones are basic spatial units of urban management and urban planning and they provide the basic space where human activities occur. The concept of urban functional zones is closely related to two basic factors: urban landscape environment and human activities. The former provides the basic space for human activity and influences urban land use at a coarse scale, and the latter indicates the differentiation of functions in local urban areas. In previous studies, the hierarchical correspondence and interaction between urban landscape and human activities have not been given full consideration in the cognition of urban functional zones, which would influence the accuracy and interpretability of the results. Therefore, a hierarchical fusion method considering urban landscape and human activity patterns based on multi‐source data is proposed in this article. We introduce a new cognition framework. First, a land use classification based on urban landscape features from remote sensing images is extracted and used as the fundamental results of urban land uses. Then, fine‐grained functional semantics of local urban areas are recognized based on human activity patterns extracted from crowdsourced data (i.e., points of interest and taxi trajectories). Finally, the above results at different scales are fused with hierarchical constraints, which means that the information from both urban landscape and human activities can be fully utilized to obtain a refined and fine‐grained map of urban functional zones. Wuhan, China was chosen as an example to evaluate the performance of the proposed method. Results show that the overall accuracy of the proposed method is 82.51% (accuracies of the mixed functional zones and single functional zones are 77.93 and 87.96%, respectively) and compared with state‐of‐the‐art methods, the proposed method performed better for the recognition of mixed functional zones. [ABSTRACT FROM AUTHOR]

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park's test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications. [ABSTRACT FROM AUTHOR]

Abstract Massive spatio-temporal data have been collected from the earth observation systems for monitoring the changes of natural resources and environment. To find the interesting dynamic patterns embedded in spatio-temporal data, there is an urgent need for detecting spatio-temporal clusters formed by objects with similar attribute values occurring together across space and time. Among different clustering methods, the density-based methods are widely used to detect such spatio-temporal clusters because they are effective for finding arbitrarily shaped clusters and rely on less priori knowledge (e.g. the cluster number). However, a series of user-specified parameters is required to identify high-density objects and to determine cluster significance. In practice, it is difficult for users to determine the optimal clustering parameters; therefore, existing density-based clustering methods typically exhibit unstable performance. To overcome these limitations, a novel density-based spatio-temporal clustering method based on permutation tests is developed in this paper. High-density objects and cluster significance are determined based on statistical information on the dataset. First, the density of each object is defined based on the local variance and a fast permutation test is conducted to identify high-density objects. Then, a proposed two-stage grouping strategy is implemented to group high-density objects and their neighbors; hence, spatio-temporal clusters are formed by minimizing the inhomogeneity increase. Finally, another newly developed permutation test is conducted to evaluate the cluster significance based on the cluster member permutation. Experiments on both simulated and meteorological datasets show that the proposed method exhibits superior performance to two state-of-the-art clustering methods, i.e., ST-DBSCAN and ST-OPTICS. The proposed method can not only identify inherent cluster patterns in spatio-temporal datasets, but also greatly alleviates the difficulty in selecting appropriate clustering parameters. Highlights • Two permutation tests are proposed to determine high-density objects and cluster significance. • The proposed method greatly alleviates the difficulty in selecting appropriate clustering parameters. • The proposed method exhibits superior performance to two state-of-the-art clustering methods, i.e., ST-DBSCAN and ST-OPTICS. [ABSTRACT FROM AUTHOR]

The accurate large-scale mapping of cerebral microvascular blood flow velocity is crucial for a better understanding of cerebral blood flow (CBF) regulation. Although optical imaging techniques enable both high-resolution microvascular angiography and fast absolute CBF velocity measurements in the mouse cortex, they usually require different imaging techniques with independent system configurations to maximize their performances. Consequently, it is still a challenge to accurately combine functional and morphological measurements to co-register CBF speed distribution from hundreds of microvessels with high-resolution microvascular angiograms. We propose a data acquisition and processing framework to co-register a large set of microvascular blood flow velocity measurements from dynamic light scattering optical coherence tomography (DLS-OCT) with the corresponding microvascular angiogram obtained using two-photon microscopy (2PM). We used DLS-OCT to first rapidly acquire a large set of microvascular velocities through a sealed cranial window in mice and then to acquire high-resolution microvascular angiograms using 2PM. The acquired data were processed in three steps: (i) 2PM angiogram coregistration with the DLS-OCT angiogram, (ii) 2PM angiogram segmentation and graphing, and (iii) mapping of the CBF velocities to the graph representation of the 2PM angiogram. We implemented the developed framework on the three datasets acquired from the mice cortices to facilitate the coregistration of the large sets of DLS-OCT flow velocity measurements with 2PM angiograms. We retrieved the distributions of red blood cell velocities in arterioles, venules, and capillaries as a function of the branching order from precapillary arterioles and postcapillary venules from more than 1000 microvascular segments. The proposed framework may serve as a useful tool for quantitative analysis of large microvascular datasets obtained by OCT and 2PM in studies involving normal brain functioning, progression of various diseases, and numerical modeling of the oxygen advection and diffusion in the realistic microvascular networks. [ABSTRACT FROM AUTHOR]

Abstract: The clustering of spatio‐temporal events has become one of the most important research branches of spatio‐temporal data mining. However, the discovery of clusters of spatio‐temporal events with different shapes and densities remains a challenging problem because of the subjectivity in the choice of two critical parameters: the spatio‐temporal window for estimating the density around each event, and the density threshold for evaluating the significance of clusters. To make the clustering of spatio‐temporal events objective, in this study these two parameters were adaptively generated from statistical information about the dataset. More precisely, the density threshold was statistically modeled as an adjusted significance level controlled by the cardinality and support domain of the dataset, and the appropriate sizes of spatio‐temporal windows for clustering were determined by the spatio‐temporal classification entropy and stability analysis. Experiments on both simulated and earthquake datasets were conducted, and the results show that the proposed method can identify clusters of different shapes and densities. [ABSTRACT FROM AUTHOR]

Two photon microscopy and optical coherence tomography (OCT) are two standard methods for measuring flow speeds of red blood cells in microvessels, particularly in animal models. However, traditional two photon microscopy lacks the depth of field to adequately capture the full volumetric complexity of the cerebral microvasculature and OCT lacks the specificity offered by fluorescent labeling. In addition, the traditional raster scanning technique utilized in both modalities requires a balance of image frame rate and field of view, which severely limits the study of RBC velocities in the microvascular network. Here, we overcome this by using a custom two photon system with an axicon based Bessel beam to obtain volumetric images of the microvascular network with fluorescent specificity. We combine this with a novel scan pattern that generates pairs of frames with short time delay sufficient for tracking red blood cell flow in capillaries. We track RBC flow speeds in 10 or more capillaries simultaneously at 1 Hz in a 237 µm × 237 µm × 120 µm volume and quantified both their spatial and temporal variability in speed. We also demonstrate the ability to track flow speed changes around stalls in capillary flow and measure to 300 µm in depth. [ABSTRACT FROM AUTHOR]

Reasonable urban commercial planning must clarify the location and scope of urban commercial districts (UCDs). However, existing studies typically detect spurious UCDs owing to the bias in a single data source while ignoring the continuity and ambiguity of commercial district boundaries. Therefore, in this study, we designed a two-stage approach for detecting UCDs. First, points of interest and population heat data were fused through hotspot and overlay analyses to detect core commercial areas. The boundaries of the UCDs were then identified by considering adjacent blocks using adjusted cosine similarity and region-growing algorithms. Finally, an experiment was conducted in Xiamen, revealing concentrated businesses on Xiamen Island and sparse businesses outside Xiamen Island. An experimental comparison with other strategies confirmed the improved modeling ability of this approach for the edge ambiguity of UCDs. This framework provides tools for urban commercial planning and helps recognize urban commercial patterns in a timely manner. [ABSTRACT FROM AUTHOR]

Abstract Liquid metals (LM) such as eutectic gallium-indium and gallium-indium-tin are important functional liquid-state metal materials with many unique properties, which have attracted wide attentions especially from soft robot area. Recently the amoeba-like transformations of LM on the graphite surface are discovered, which present a promising future for the design and assemble of self-fueled actuators with dendritically deformable body. It appears that the surface tension of the LM can be significantly reduced when it contacts graphite surface in alkaline solution. Clearly, the specific surface should play a vital role in inducing these intriguing behaviors, which is valuable and inspiring in soft robot design. However, the information regarding varied materials functions underlying these behaviors remains unknown. To explore the generalized effects of surface materials in those intriguing behavior, several materials including glass, graphite, nickel and copper oxides (CuO) were comparatively investigated as substrate surfaces. Important results were obtained that only LM amoeba transformations were observed on graphite and CuO surfaces. In order to identify the proper surface condition for LM transformation, the intrinsic properties of substrate surfaces, such as the surface charge and roughness, as well as the specific interaction with LM like wetting behavior and mutual locomotion etc., were characterized. The integrated results revealed that LM droplet appears more likely to deform on surfaces with higher positive surface charge density, higher roughness and less bubble generation on them. In addition, another surface material, CuO x , is identified to own similar ability to graphite, which is valuable in achieving amoeba-like transformation. Moreover, this study offers a fundamental understanding of the surface properties in realizing LM amoeba transformations, which would shed light on packing and structure design of liquid metal-based soft device within multi-material system. [ABSTRACT FROM AUTHOR]

Plasmon‐induced chemical reaction is an emerging field but its development faces huge challenges because of low quantum efficiency. Herein, we report that the solar energy conversion efficiency of Au/TiO2 in plasmon‐induced water oxidation is greatly enhanced by intercalating Li+ into TiO2. An incident photon‐to‐current efficiency as high as 2.0 %@520 nm is achieved by Au/Li0.2TiO2 in photoelectrocatalytic water oxidation, realizing a 33‐fold enhancement in photocurrent density compared with Au/TiO2. The superior photoelectrocatalytic performance is mainly ascribed to the enhanced electric conductivity and higher catalytic activity of Li0.2TiO2. Furthermore, the ultrafast transient absorption spectroscopy suggests that lithium intercalation into TiO2 could change the dynamics of hot electron relaxation in Au nanoparticles. This work demonstrates that intercalation of alkaline ions into semiconductors can promote the charge separation efficiency of the plasmonic effect of Au/TiO2. [ABSTRACT FROM AUTHOR]

Plasmon‐induced chemical reaction is an emerging field but its development faces huge challenges because of low quantum efficiency. Herein, we report that the solar energy conversion efficiency of Au/TiO2 in plasmon‐induced water oxidation is greatly enhanced by intercalating Li+ into TiO2. An incident photon‐to‐current efficiency as high as 2.0 %@520 nm is achieved by Au/Li0.2TiO2 in photoelectrocatalytic water oxidation, realizing a 33‐fold enhancement in photocurrent density compared with Au/TiO2. The superior photoelectrocatalytic performance is mainly ascribed to the enhanced electric conductivity and higher catalytic activity of Li0.2TiO2. Furthermore, the ultrafast transient absorption spectroscopy suggests that lithium intercalation into TiO2 could change the dynamics of hot electron relaxation in Au nanoparticles. This work demonstrates that intercalation of alkaline ions into semiconductors can promote the charge separation efficiency of the plasmonic effect of Au/TiO2. [ABSTRACT FROM AUTHOR]

This paper proposes a fast and effective template-based visual tracking method based on a novel distance metric. First, a fragment-based correntropy induced metric (FCIM) is proposed, which exploit the strengths from both the correntropy method and the fragment scheme to handle observation noise (especially, gross errors caused by occlusion). Second, the proposed FCIM method is integrated into the Bayesian inference framework for solving the tracking problem. In addition, a simple on-line template update scheme is introduced to capture the appearance change of the object during the tracking processing. Experimental results and discussions demonstrate that the proposed method is better than other popular algorithms. [ABSTRACT FROM AUTHOR]

A regioselective deuteration at the β- and γ-position of pyridines is reported. Efficient deuteration occurred with a combination of KOtBu and DMSO-d6, replenishing the prevailing α-deuteration of the pyridine systems. Preliminary mechanistic studies suggested that the dimsyl carbanion acts as one of the key intermediates. [ABSTRACT FROM AUTHOR]

• Proposed a method to investigate the loading rate effects of elliptically shaped Ni 8 Ti 9 Cr 1 rings. • Found aspect ratio influences deformation behaviors, impacting ductility and failure modes. • Found the oscillation behavior of the test curves caused by the superimposition of stress waves. • Established the Johnson-Cook model of irregularly shaped Ni 8 Ti 9 Cr 1 ring for the first time. • Enriched understanding of nonuniform vibrations and necking phenomenon of irregularly shaped ring. Thin-walled tubes with elliptically shaped annular sections are widely applied in fuel ducts and exhaust pipes. However, the tensile mechanical properties of these structures have remained elusive due to the lack of accurate testing methods. This study aims to fill this gap by establishing circumferential tensile testing methods for static and dynamic conditions by employing a split Hopkinson tensile bar and an electronic universal tensile machine. The quasistatic and dynamic tensile characteristics of two distinct elliptically shaped Ni 8 Ti 9 Cr 1 rings with different aspect ratios were investigated at room temperature, revealing a significant loading rate effect. The tensile curves exhibited considerable fluctuations, which diminshed as aspect ratios decreased. These fluctuations were attributed to stress waves propagating back and forth between the specimen, the clamp, and the bars, causing superimposition. The Johnson‒Cook (JC) model was established based on the experimental results and then incorporated into the finite element model to gain deep insights into the mechanical responses of the Ni 8 Ti 9 Cr 1 rings under tension. Our findings reveal that dynamic tensile testing of elliptically shaped Ni 8 Ti 9 Cr 1 rings induces a complex stress state, which leads to nonuniform necking effects and structural vibrations, revealing complex behaviors previously unexplored. This investigation significantly advances the understanding of quasistatic and dynamic tensile responses in elliptically shaped ring structures, shedding light on their mechanical intricacies across varying conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

Liquid–liquid interfaces of liquid alloys with electrolytic solutions present fertile platforms for realizing exciting interfacial phenomena that can be devised to process alloys and produce nanomaterials. Here, such an interface is established by immersing a gallium–bismuth binary liquid alloy into aqueous electrolytes. It is shown that the application of a negative voltage to this interface results in a rapid and complete liberation of bismuth nanostructures from liquid gallium. The set of conditions that govern the chemistry of the interface can be adjusted to control the oxidation state, morphology, and crystal structure of the expelled bismuth. By changing the conditions; nanotubular, atomically thin plates, and sea‐urchin‐shaped bismuth oxide morphologies are obtained. The process can also control the crystal phase of bismuth oxide as monoclinic (α), tetragonal (β), or body‐centered cubic (γ). The addition of ascorbic acid to the electrolyte is observed to prevent the oxidation of the expelled entities, resulting in bismuth metal nanoparticles. Ab initio molecular dynamics and computational fluid dynamics simulations are performed to elucidate this rapid phase separation at the interface. This knowledge will potentially lead to new pathways for using alloys as reaction media to refine metals while simultaneously producing nanomaterials for various applications. [ABSTRACT FROM AUTHOR]

Liquid–liquid interfaces of liquid alloys with electrolytic solutions present fertile platforms for realizing exciting interfacial phenomena that can be devised to process alloys and produce nanomaterials. Here, such an interface is established by immersing a gallium–bismuth binary liquid alloy into aqueous electrolytes. It is shown that the application of a negative voltage to this interface results in a rapid and complete liberation of bismuth nanostructures from liquid gallium. The set of conditions that govern the chemistry of the interface can be adjusted to control the oxidation state, morphology, and crystal structure of the expelled bismuth. By changing the conditions; nanotubular, atomically thin plates, and sea‐urchin‐shaped bismuth oxide morphologies are obtained. The process can also control the crystal phase of bismuth oxide as monoclinic (α), tetragonal (β), or body‐centered cubic (γ). The addition of ascorbic acid to the electrolyte is observed to prevent the oxidation of the expelled entities, resulting in bismuth metal nanoparticles. Ab initio molecular dynamics and computational fluid dynamics simulations are performed to elucidate this rapid phase separation at the interface. This knowledge will potentially lead to new pathways for using alloys as reaction media to refine metals while simultaneously producing nanomaterials for various applications. [ABSTRACT FROM AUTHOR]

An anomalous geographical region refers to a collection of spatially aggregated objects whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighbors. The detection of anomalous regions plays an important role in spatial data mining. However, the requirement of user‐specified parameters for spatial neighborhood construction and anomalous region discovery will inevitably result in the omission or misjudgment of spatial anomalies; it is still challenging to detect arbitrarily shaped anomalous regions in an objective way. Inspired by the data field theory, this study models spatial anomaly degree by considering the distance decay effect and develops an approach for the objective detection of significantly anomalous regions from spatial sampling points. First, constrained Delaunay triangulation is employed to construct reasonable and stable spatial neighborhoods by quantifying the spatial distribution characteristics of sampling points. On this basis, a Gaussian function is adopted for the measurement of spatial anomaly degree considering both distance decay effect and non‐spatial attribute value differences, based upon which anomalous objects can be captured. Finally, treating each anomalous object as a seed, a multidirectional optimization method is developed to identify arbitrarily shaped anomalous regions, and a Monte Carlo simulation is employed to further test the statistical significance of anomalous regions. Experiments on both simulated and real‐world datasets demonstrate that the proposed approach outperforms existing methods in terms of both accuracy and sufficiency for anomalous region detection. [ABSTRACT FROM AUTHOR]