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The combination of continuous natural fiber and fused filament fabrication (FFF) 3D printing enables the manufacturing of low carbon emitting, environment friendly, lightweight, and high strength biomass composites with designated geometry characteristics. In current literature, reviews associated with continuous fiber 3D printing primarily cover synthetic fibers, such as carbon fiber, Kevlar, and glass fiber. Very few pieces of literature on the FFF printing of continuous natural fibers are available. Techniques/methodologies for incorporating continuous natural fiber reinforcements in FFF is an emerging field of research. A comprehensive review and discussion on current progress and the future prospects of continuous natural fiber 3D printing would be beneficial to its development. This article summarizes the current research status of continuous natural fiber 3D printing, including information on printing techniques, materials, and the influence of printing parameters on composite properties, so as to provide reference for the future development of FFF technology using continuous natural fiber and thermoplastic composites. [ABSTRACT FROM AUTHOR]

The relationships in multivariable data are intricate, and there are usually implicit associations between scalar values variables. However, existing association analysis methods lack spatial measurement of scalar values, and fail to collaboratively analyze the association between scalar values and variables. Thus association results may be one-sided. In this paper, we construct a scalar-value neighborhood graph to preserve the spatial information for scalar values and propose a graph neural network model composed of multiple graph convolutional layers and a self-attention mechanism for learning the low-dimensional vectors of scalar values and variables simultaneously. Several case studies show the scalability and flexibility of our method on analyzing the association between scalar values and variables. [ABSTRACT FROM AUTHOR]

The high-resolution scanning devices developed in recent decades provide biomedical volume datasets that support the study of molecular structure and drug design. Isosurface analysis is an important tool in these studies, and the key is to construct suitable description vectors to support subsequent tasks, such as classification and retrieval. Traditional methods based on handcrafted features are insufficient for dealing with complex structures, while deep learning-based approaches have high memory and computation costs when dealing directly with volume data. To address these problems, we propose IsoExplorer, an isosurface-driven framework for 3D shape analysis of biomedical volume data. We first extract isosurfaces from volume data and split them into individual 3D shapes according to their connectivity. Then, we utilize octree-based convolution to design a variational autoencoder model that learns the latent representations of the shape. Finally, these latent representations are used for low-dimensional isosurface representation and shape retrieval. We demonstrate the effectiveness and usefulness of IsoExplorer via isosurface similarity analysis, shape retrieval of real-world data, and comparison with existing methods. [ABSTRACT FROM AUTHOR]

For various reasons, such as new requirements, architecture refactoring, and bug fixing, software projects often evolve to yield better quality and performance. All changes produced during the development process are reflected in the source code, which provides an opportunity to explore software evolution. In this paper, we propose a visual analytics system to support evolution analysis based on topic modeling. We focus on three aspects: (1) when significant changes to source code occur, (2) how software features evolve, and (3) why software evolution occurs. Each source file is regarded as a document and represented by its topic vector. The files of each two successive versions are classified into four types to quantify version differences, and the number of topic-associated files is denoted as the topic assignment to characterize feature evolution. Finally, we inspect the causes of software evolution through the visual comparison between versions. Two case studies on JavaScript libraries demonstrate the usefulness and effectiveness of our system. [ABSTRACT FROM AUTHOR]

A large software system contains millions of lines of source code, and the development often involves many developers over a long period. How to understand and analyze its complex code dependencies is challenging but meaningful to developers for program comprehension. In this paper, we propose a novel visual analytics system to explore code dependencies between files for visually understanding software architecture and interactively analyzing bad dependencies. The dependencies between files are abstracted as a directed graph (i.e., the dependency graph) at different levels via the file hierarchy to show the modularization design of source code. Node embeddings of the dependency graph are learned to identify the files with specific dependencies and analyze the similarity between bad dependencies. Finally, we evaluate the usability of our system by two case studies in different JavaScript libraries as well as a user study on software architecture understanding and bad dependency analysis. [ABSTRACT FROM AUTHOR]

Neuron morphology reconstruction from high-resolution imaging data is essential for understanding the structure and function of the brain in neuroscience. However, previous methods cannot achieve both water-tight and high performance in surface mesh reconstruction of large-scale neurons. Thus, this paper proposes a novel neuronal surface mesh reconstruction algorithm based on isosurface extraction, virtual memory management, and parallel computation. The space of a neuron is firstly divided into blocks, and they are organized as a sparse octree to handle large-scale neurons with long projection. We then perform voxelization and isosurface extraction on valid blocks based on the skeleton model of the neuron to ensure the generated mesh that is water-tightness, and the quality and the density of the mesh are controllable. Since each block is processed independently, the reconstruction can be performed in parallel for high performance and partially for interactive modification during neuron proofreading. Experiments demonstrate that the proposed algorithm can generate water-tight neuronal surface meshes effectively and satisfy the needs of interactive visualization and correction. [ABSTRACT FROM AUTHOR]

The urban transportation system is the footstone of a city's infrastructure, and the booming bike-sharing system has become a vital part of urban transportation. Understanding the bike-sharing system and traditional taxi system as well as their similarities and differences are essential for bike-sharing rebalancing, taxi dispatching, and urban planning. However, due to the sparseness of record data and the difference in service regions, the relationship between them is indeed obscure, and previous solutions mostly focus only on a single system. In this paper, we propose a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. The service region for each bike station is created to fuse bike-sharing data and taxi data. We harness two three-order tensors to represent them in a unified framework to generate potential patterns by tensor decomposition. The visual analytics system integrates two spatiotemporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system. We demonstrate the effectiveness of our system through real-world case studies. The urban transportation system is the footstone of a city's infrastructure, and the booming bike-sharing system has become a vital part of urban transportation. Understanding the bike-sharing system and traditional taxi system as well as their similarities and differences are essential for bike-sharing rebalancing, taxi dispatching, and urban planning. However, due to the sparseness of record data and the difference in service regions, the relationship between them is indeed obscure, and previous solutions mostly focus only on a single system. In this paper, we propose a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. The service region for each bike station is created to fuse bike-sharing data and taxi data. We harness two three-order tensors to represent them in a unified framework to generate potential patterns by tensor decomposition. The visual analytics system integrates two spatiotemporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system. We demonstrate the effectiveness of our system through real-world case studies. [ABSTRACT FROM AUTHOR]

Time-varying volume data is often generated from scientific simulations in a variety of application domains, such as computational fluid dynamics, combustion science, and computational cosmology. Data visualization plays an important role in analyzing the dynamics and evolution of phenomena hidden in the data. Over the last two decades, a substantial amount of visualization techniques have been proposed in this research area. In this paper, we systematically review the recent literature on data visualization and visual analytics for time-varying scalar volume data. We first collect a corpus of relevant technical and application papers in visualization journals and conferences from 2008 to 2019. Based on this corpus, we classify these techniques into three aspects, including feature tracking, evolution visualization, and rendering, and then detaily describe relevant techniques in these three aspects. Finally, we conclude this survey with emerging trends and future challenges in time-varying volume visualization. [ABSTRACT FROM AUTHOR]

Scientific visualizations offer domain experts the ability to explore data visually and interactively to gain insights from data. Most visualization systems focus on functionality and scalability. However, we believe that with the advent of faster rendering techniques and higher-speed networks, accessibility for every device should also be a goal for scientific visualizations. In this paper, we propose a novel scientific visualization system, Voxer, which provides ubiquitous visualizations by decoupling user interfaces from system space. In Voxer, we encapsulate the data processing and rendering functionality as a web service and design a module-based user interface for domain experts to create and customize different visualization pipelines in response to their specific requirements. These configured visualizations can be shared with the public through embedding visualizations on the web and interactively rendering on the server. Use cases and benchmarks are used to demonstrate how our system can help domain experts easily create and customize visualizations and improve visualizations accessibility. [ABSTRACT FROM AUTHOR]

Multivariate spatial data play an important role in computational science and engineering simulations. The potential features and hidden relationships in multivariate data can assist scientists to gain an in-depth understanding of a scientific process, verify a hypothesis, and further discover a new physical or chemical law. In this paper, we present a comprehensive survey of the state-of-the-art techniques for multivariate spatial data visualization. We first introduce the basic concept and characteristics of multivariate spatial data, and describe three main tasks in multivariate data visualization: feature classification, fusion visualization, and correlation analysis. Finally, we prospect potential research topics for multivariate data visualization according to the current research. [ABSTRACT FROM AUTHOR]

Time-varying volume data generated from scientific simulations are generally temporal and contain dynamic and complex features. The evolution of these features is important to understand the phenomena hidden in the data. In this paper, we introduce FeatureFlow, which is a novel visualization technique revealing feature evolution based on a hierarchical river metaphor. FeatureFlow decomposes the entire feature evolution into multiple levels and exploits an evolution measure to quantify the changes of the features. FeatureFlow visually summarizes the hierarchical evolution, the evolution value, and associated attributes to intuitively display the complex 4D spatial-temporal feature evolution in 2D. In addition, FeatureFlow converts each river into a string based on the serial ordering of evolutionary events and supports evolutionary pattern-matching queries. Experiments on three time-varying volume data sets and feedback from two domain experts demonstrate the utility of FeatureFlow in effectively helping users understand and explore feature evolution in time-varying volume data. [ABSTRACT FROM AUTHOR]

Abstract Flammability of wood composites has always been of a concern. To investigate whether CaCO 3 particles are efficient fire-retardant fillers for wood composites, wood composites were prepared with Phenol Formaldehyde (PF) resin and pine flakes that were deposited with CaCO 3 by the treatments of Na 2 CO 3 and CaCl 2. The flammable performance, Limiting Oxygen Index (LOI), simultaneous Thermogravimetric Analysis and Differential Scanning Calorimetry (TGA–DSC), mechanical properties, and water resistance of wood-flake composites deposited with CaCO 3 were evaluated. Furthermore, the Scanning Electron Microscopy (SEM) was used to observe the surface morphology of the treated wood flakes, showing that CaCO 3 microparticle crystals grew onto the wood flake surfaces. The increase of reactant concentrations from 0.5 M to 1.0 M resulted in an increase in LOI and improvements of the flame-retardant performance of the composites. The TGA–DTG examination showed that the decomposition peak of the treated wood flakes shifted to a lower temperature range. The TGA also demonstrated that the treatment contributed to the accumulation of char layer, which benefited the flame retardancy of the composites. The growth of energy consumed during the sample pyrolysis could be attributed to the insulation of CaCO 3 and thicker char layers. After the treatment with reactants (concentration of 0.5 M), although the internal bonding (IB) slightly decreased by 8.3%, the modulus of elasticity (MOE) and the modulus of rupture (MOR) of the composites significantly increased by 182.9% and 63.5%, respectively. [ABSTRACT FROM AUTHOR]

Abstract Wood plastic composites (WPCs) have emerged as environmentally benign filaments for fused deposition modeling (FDM) printing. Using WPCs filaments to print light weight and strong cellular structures could create new application potentials for WPCs and provide sustainable feedstock for cellular materials. In this research, the compression performance of FDM printed circle, square, and voronoi WPCs cellular structures were investigated using finite element simulations and compression experiments. Simulated results did not agree with experimental results due to the presence of cavities in the prints. Cavities were formed in the cellular structures with arcs and curvature. The results showed that the circle cellular structure demonstrated great fluctuations between the simulations and experimental results. The cavity porosity was also found to increase with the increase of the print line width. Further, to improve the accuracy of simulations, the cavity porosity exists in the cellular structures was considered while revising the models. After modification, square cellular models compressed the least, followed by the circle and voronoi ones, which was consistent with the experimental results. Minimizing the print line width could reduce cavity porosity. Nonetheless, thinner print line width and smaller wood fiber size in filaments will likely increase manufacturing cost and difficulty. [ABSTRACT FROM AUTHOR]

Viewpoint estimation from 2D rendered images is helpful in understanding how users select viewpoints for volume visualization and guiding users to select better viewpoints based on previous visualizations. In this article, we propose a viewpoint estimation method based on Convolutional Neural Networks (CNNs) for volume visualization. We first design an overfit-resistant image rendering pipeline to generate the training images with accurate viewpoint annotations, and then train a category-specific viewpoint classification network to estimate the viewpoint for the given rendered image. Our method can achieve good performance on images rendered with different transfer functions and rendering parameters in several categories. We apply our model to recover the viewpoints of the rendered images in publications, and show how experts look at volumes. We also introduce a CNN feature-based image similarity measure for similarity voting based viewpoint selection, which can suggest semantically meaningful optimal viewpoints for different volumes and transfer functions. [ABSTRACT FROM AUTHOR]

Abstract Word embeddings are distributed representations for natural language words, and have been wildly used in many natural language processing tasks. The word embedding space contains local clusters with semantically similar words and meaningful directions, such as the analogy. However, there are different training algorithms and text corpora, which both have a different impact on the generated word embeddings. In this paper, we propose a visual analytics system to visually explore and compare word embeddings trained by different algorithms and corpora. The word embedding spaces are compared from three aspects, i.e., local clusters, semantic directions and diachronic changes, to understand the similarity and differences between word embeddings. [ABSTRACT FROM AUTHOR]

Abstract: Online review platforms offer customers the opportunities to express valuable feedback and personal views from various aspects on restaurants, products, works of art, or other items. A majority of previous studies on these user-generated reviews are devoted to controversy, bias, and opinion analysis. However, little work has been done to study urban characteristics via topic analysis from the city level in reviews. In this paper, we propose a visual analytics system, to visually explore spatiotemporal urban topics for cultural trend discovery, location mining, and decision making. Specifying a topic by users is supported due to the difference between review text and traditional text, such as news and books, and the diversity of topics and users. Sentiment analysis and statistical analysis are adopted to characterize the temporal trend and sentiment and topic geographical distributions of the user-specific topic. Our system allows the user to interactively explore the time-evolving frequency trend and characteristic geographical distributions of a topic in reviews. We evaluate the effectiveness and usefulness of our system using three case studies in different domains.Graphical Abstract: [ABSTRACT FROM AUTHOR]

Abstract: Bike-sharing systems have grown tremendously worldwide in the recent years. Understanding the user activities in urban areas is invaluable, especially for bike rebalance and urban planning. However, it is difficult to directly capture the user activity patterns from the bike-sharing data due to its sparse and discontinuous characteristics. In the recent years, many methods have been explored to visualize the user activity patterns. Many previous methods focused on visually presenting the temporal and spatial distribution directly. In this paper, we construct a tensor based on the spatial, temporal, and user information of the bike-sharing data, and employ tensor factorization to extract latent user activity patterns. To facilitate the users to analyze and understand these patterns, a visual analytics system is designed to interactively explore these patterns from the spatial, temporal, and user dimensions and compare these patterns in/between cities. We demonstrate the effectiveness of our system via case studies with real-word datasets.Graphical abstract: [ABSTRACT FROM AUTHOR]

Abstract: Multivariate volume data sets usually have complex interactions between fields, and features from different fields, such as segmented regions and isosurfaces, can be associated together to intuitively reveal the correlation and difference between fields. In this paper, we present a visual analytic approach for interactive feature exploration. A graph-based representation, called FeatureNet, is designed to provide a full picture of major features extracted from each field. FeatureNet visually summarizes both the nesting and association relationships of major features in each variable, and serves as a navigation tool to guide data exploration. Case studies with three simulation data sets demonstrate the effectiveness and usefulness of FeatureNet, and it can help users better understand and inspect the nesting and correlation relationships between fields.Graphical Abstract: [ABSTRACT FROM AUTHOR]

Photographic volumes have been increasingly used in medical and biological researches in recent years. The original colors kept in photographic volumes present great opportunities to capture a rich set of information within the dataset for a wide variety of data analysis and visualization applications. Despite years of research, an interactive and user-friendly transfer function is still lacking for photographic volume visualization. The difficulty lies in how to map colors to a space that is convenient and intuitive for users to interactively classify features, i.e., specifying opacities for voxels. In this paper, we propose a color-based transfer function for intuitive opacity specification of photographic volumes. The color-based transfer function intelligently maps the colors from 3D to 1D, resulting in 256 representative colors which preserve the original colors to the maximum extent. Users can directly classify voxels based on these representative colors similar to the conventional 1D transfer function. Experiments are performed to evaluate the effectiveness of the proposed method, and also demonstrate the intuitiveness and flexibility of the proposed method. Graphical Abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Many fields, such as medicine and biology, are producing an increasingly large volume using high-resolution digital imaging techniques, and this makes effective data analysis and visualization of these volumes more and more difficult. Volume reduction, decreasing the volume size, is one of the promising directions to solve this challenge for interactive volume visualization. In this paper, we present an automatic volume data reduction method called surface carving. It intelligently removes contextual voxels while preserving important features, and finally generates an optimal volume at the desired reduction size/rate. For large volume data sets, a multilevel banded method is introduced to gracefully overcome the memory limit and speed up volume reduction. We compare our technique with traditional cropping and scaling approaches and demonstrate the effectiveness and efficiency of our method with several volume data sets. [ABSTRACT FROM AUTHOR]

Lightweight rigid polyurethane foam (RPUF) composites with favorable mechanical properties are demanded by numerous engineering applications. The mechanical performance of foam composites could be customized and enhanced by embedding geometric skeletons. The advancement of 3D printing technology has enabled greater freedom in manufacturing geometrically complex products. In this paper, two types of lattice structures were 3D printed with fused filament fabrication (FFF). Structural composites were prepared by filling the lattices with RPUF through the free rising method. Experimental testing and FEA modeling demonstrated that the lattice structures had significant effects on the composites' stress dissipation and fracture forms. Compared to neat RPUF, the composites exhibited greater elastic limit, compression modulus, energy absorption capabilities, flexural strength, and flexural modulus. Lattices with more struts and greater density resulted in superior compression and flexural performance. This study shows that FFF lattices are suitable for constructing RPUF composites with enhanced flexural and compression properties. [ABSTRACT FROM AUTHOR]

Halos are generally used to enhance depth perception and display spatial relationships in illustrative visualization. In this paper, we present a simple and effective method to create volumetric halo illustration. At the preprocessing stage, we generate, on graphics hardware, a view-independent halo intensity volume, which contains all of the potential halos around the boundaries of features, based on the opacity volume. During halo rendering, the halo intensity volume is used to extract halos only around the contours of structures for the current viewpoint. The performance of our approach is significantly faster than previous halo illustration methods, which perform both halo generation and rendering during direct volume rendering. We further propose depth-dependent halo effects, including depth color fading and depth width fading. These halo effects adaptively modulate the visual properties of halos to provide more perceptual cues for depth interpretation. Experimental results demonstrate the efficiency of our proposed approach and the effectiveness of depth-dependent halos. [ABSTRACT FROM AUTHOR]

An ecofriendly approach was developed for preparing copper-doped carbon dots (CDs) with superior photocatalysis using chromium-copper-arsenic (CCA)-treated wood waste as a precursor. Original wood (W-CDs), CCA-treated wood (C-CDs), and bioremediation CCA wood (Y-CDs) were used as the precursors. The chemical composition and structural, morphological, and optical properties, as well as the photocatalytic ability of the synthesized CDs varied with wood type. The C-CDs and W-CDs had similar characteristics: quasispherical in shape and with a diameter of 2 to 4.5 nm. However, the Y-CDs particles were irregular and stacked together, with a size of 1.5–3 nm. The presence of nitrogen prevented the formation of an aromatic structure for those CDs fabricated from bioremediation CCA wood. The three synthesized CDs showed a broad absorption peak at 260 nm and a weak absorption peak at 320 nm. Proof of the model study for the fabrication of luminescent CDs from CCA wood waste for bioimaging was provided. The degradation rate of CD photocatalytic MB was 97.8% for 30 min. Copper doping gives the CDs electron acceptor properties, improving their photocatalytic efficiency. This study provides novel ways to prepare nanomaterials from decommissioned wood as a nontoxic and low-cost alternative to fluorescent dots. [ABSTRACT FROM AUTHOR]

Developing 3D printable waste paper based biocomposites is a potentially greener alternative to traditional waste paper recycling methods. In this paper, waste office paper (WOP) filled polylactide acid (PLA) biocomposites were prepared as filaments for Fused Filament Fabrication (FFF). The morphology, rheology, thermal properties, and physical properties of PLA-WOP composites were investigated. Results revealed that the addition of WOP improved the composites' melt flow abilities. The T g and T m of composites remained similar to the neat PLA. However, tensile strengths of the prints decreased with the increase of WOP content, which could be attributed to the heterogeneous dispersion and aggregation caused by the abundance of WOP in the system. When 5% WOP was used, the elongation at break increased in comparison with pure PLA specimens. Nonetheless, when the WOP content was increased to 15%, the elongation at break decreased. To improve the mechanical properties of the prints, a silane coupling agent (γ-methacryloxypropyltrimethoxylsilane, KH570) was used to modify WOP granules. The mechanical properties were clearly enhanced post KH570 treatment. Furthermore, FTIR analyses demonstrated that the hydrogen bonding improved interfacial adhesion between WOP fillers and the PLA matrix. Thermal decomposition showed that the PLA-WOP composites had virtually no degradation below 260 °C, indicating that the composite filaments are safe for indoor 3D printing. [ABSTRACT FROM AUTHOR]

Wood-based materials in current additive manufacturing (AM) feedstocks are primarily restricted to the micron scale. Utilizing large-scale wood in existing AM techniques remains a challenge. This paper proposes an AM method—laser-cut veneer lamination (LcVL)—for wood-based product fabrication. Inspired by laminated object manufacturing (LOM) and plywood technology, LcVL bonds wood veneers in a layer-upon-layer manner. As demonstrated by printed samples, LcVL was able to retain the advantageous qualities of AM, specifically, the ability to manufacture products with complex geometries which would otherwise be impossible using subtractive manufacturing techniques. Furthermore, LcVL-product structures designed through adjusting internal voids and wood-texture directionality could serve as material templates or matrices for functional wood-based materials. Numerical analyses established relations between the processing resolution of LcVL and proportional veneer thickness (layer height). LcVL could serve as a basis for the further development of large-scale wood usage in AM. [ABSTRACT FROM AUTHOR]

Feature classification has always been one of the research hotspots in scientific visualization. However, conventional interactive feature classification methods rely on prior knowledge and typically require trial and error, whereas feature classification based on data mining is generally based on local features; therefore, obtaining good results with traditional methods is difficult. In this paper, we first map a volume to the super-voxel graph using a 3D extension of the simple linear iterative clustering algorithm and then construct a graph convolutional neural network to implement node classification in a semi-supervised way, i.e., a small number of user-labeled super-voxels. We transform the feature classification of a volume into the classification task of nodes of a super-voxel graph, which is a novel approach and broadens the application scope of graph neural network to volumes. Experiments on different volumes have demonstrated the strong learning ability and reasoning ability of the proposed method. [ABSTRACT FROM AUTHOR]

Neuron tracing, also known as neuron reconstruction, is an essential step in investigating the morphology of neuronal circuits and mechanisms of the brain. Since the ultra-high throughput of optical microscopy (OM) imaging leads to images of multiple gigabytes or even terabytes, it takes tens of hours for the state-of-the-art methods to generate a neuron reconstruction from a whole mouse brain OM image. We introduce InstantTrace, a novel framework that utilizes parallel neuron tracing on GPUs, achieving a significant speed boost of more than 20 × compared to state-of-the-art methods with comparable reconstruction quality on the BigNeuron dataset. Our framework utilizes two methods to achieve this performance advance. Firstly, it takes advantage of the sparse feature and tree structure of the neuron image, which serial tracing methods cannot fully exploit. Secondly, all stages of the neuron tracing pipeline, including the initial reconstruction stage that have not been parallelized in the past, are executed on GPU using carefully designed parallel algorithms. Furthermore, to investigate the applicability and robustness of the InstantTrace framework, a test on a whole mouse brain OM Image is conducted, and a preliminary neuron reconstruction of the whole brain is finished within 1 h on a single GPU, an order of magnitude faster than the existing methods. Our framework has the potential to significantly improve the efficiency of the neuron tracing process, allowing neuron image experts to obtain a preliminary reconstruction result instantly before engaging in manual verification and refinement. [ABSTRACT FROM AUTHOR]

Abstract: Photographic volume visualization has been widely applied in various fields ranging from medicine to biology. Different from scalar volume data, photographic volume data are directly captured by means of the modern cryo-imaging systems. The voxels are recorded as RGB vectors, which makes it difficult to estimate accurate gradient for the shading and the design of transfer functions. In this paper, we propose a robust color gradient estimation method to produce accurate and robust gradient results for photographic volumes. First, a robust color morphological gradient (RCMG) operator is employed to estimate the gradient in a dominant direction and the low-pass filters are then applied to reduce the effects of noises. Then, an aggregation operator is applied to estimate the accurate gradient directions and the gradient magnitudes. Based on the obtained color gradients, the shading effects of internal materials are enhanced and the features can be better specified in a 2D transfer function space. At last, the effectiveness of the robust gradient estimation for photographic volume is demonstrated based on a large number of experimental rendering results, especially for those noisy photographic volume data sets.Graphical abstract: [ABSTRACT FROM AUTHOR]

Similar patient retrieval has become increasingly important with the explosive growth of electronic health records (EHRs). A similar patient cohort identified from all patients can provide data-driven insights for personalized healthcare. However, the high dimensionality and heterogeneity of EHRs increase the difficulty of measuring patient similarity. How to accurately and efficiently retrieve similar patients from a large number of EHRs remains challenging. In this paper, we propose a novel similar patient retrieval method based on interactive patient labeling and automatic model updating. Combined with the knowledge and experience of physicians, it can be adaptively modified for different patients and diseases. We also develop a visual analytics system to assist patient labeling through pairwise comparisons and support outcome analysis of similar patients. The case studies on two real-world datasets in collaboration with physicians demonstrate the effectiveness and usefulness of our method. [ABSTRACT FROM AUTHOR]

Zero liquid discharge (ZLD) is a technique for treating high-salinity brine to obtain freshwater and/or salt using a solar interface evaporator. However, salt accumulation on the surface of the evaporator is a big challenge to maintaining stable water evaporation. In this study, a simple and easy-to-manufacture evaporator, also called a crystallizer, was designed and fabricated by 3D printing. The photothermal layer printed with polylactic acid/carbon composites had acceptable light absorption (93%) within the wavelength zone of 250 nm–2500 nm. The micron-sized voids formed during 3D printing provided abundant water transportation channels inside the crystallizer. After surface hydrophilic modification, the crystallizer had an ultra-hydrophilic channel structure and gravity-assisted salt recovery function. The results revealed that the angles between the photothermal layers affected the efficacy of solar evaporation and the yield of solid salt. The crystallizer with the angle of 90° between two photothermal layers could collect more solid salt than the three other designs with angles of 30°, 60°, and 120°, respectively. The crystallizer has high evaporation and salt crystallization efficiency in a high-salinity brine environment, which is expected to have application potentials in the zero liquid discharge of wastewater and valuable salt recovery. [ABSTRACT FROM AUTHOR]

The flame retardancy of wood–polymer composites significantly affects their potential applications. Thus, multilayered wood flour/high-density polyethylene (HDPE)/polycarbonate (PC) composites were prepared via thermocompression to improve the fire retardancy of wood–polymer composites in this paper. Thermal degradation behavior, flame retardancy, and flexural strengths of the resulting composites were investigated using a thermogravimetric analysis, cone calorimetry, and mechanical testing machine, respectively. Results revealed that the boric acid treatment reduced the heat release rate and total heat release of the wood flour/HDPE composites and increased their mass of residues. However, boric acid reduced the flexural strength of the resulting composites. The combustion test indicated that PC cap layers suppressed the combustion of the resulting composites via the formation of carbon layers. Adding PC layers reduced heat release and increased the flexural strength of the resulting composites. Finally, the failure mode of the multilayered wood flour/HDPE/PC composites in the three-point flexural test was simulated by finite element analysis. [ABSTRACT FROM AUTHOR]

To tackle the increasingly severe freshwater resource scarcity problem, desalination using solar evaporators is potentially an effective approach. This article reviews the research progress of solar evaporators in recent years, including materials, structures, and performance evaluations. In terms of material research, this article introduces the mechanism of photothermal conversion of metallic, semiconductor, polymeric, and carbon-based materials and their applications in the research of solar evaporators. The structure design of solar evaporators that can improve the photothermal conversion efficiency and water transport efficiency are summarized in detail. Regarding the evaluation of the evaporator performance, common evaluation methods for steam efficiency and environmental benefits of solar evaporators were introduced. Finally, this article analyzes the current problems of evaporators and proposes a prospect for the development of new types of high-efficiency evaporators. [Display omitted] • The research progress and hotspot about solar evaporators are summarized. • The advantages of different materials and structures are compared. • By comparison, the development direction of solar evaporator in the future is prospected. [ABSTRACT FROM AUTHOR]

Foods and beverages with excessive tannins acid (TA) content taste astringent and bitter. The overconsumption of TA could result in nutritional and digestive problems. In this study, the cellulose nanocrystals (CNC)/fish swim bladder gelatin (FG) composite sponge was prepared with glutaraldehyde as a crosslinking agent. The TA adsorption performance of the sponge was discussed. The freeze-dried CNC/FG composite sponge had a porous network structure. CNC was combined into the FG matrix as a reinforcing phase. The mechanical strength, thermal stability, and swelling properties of the composite sponge were improved with the addition of an appropriate amount of CNC. Although CNC decreased the porosity of composite sponge, the increase in active adsorption sites resulted in an overall positive effect on its TA adsorption properties. Under the optimal adsorption conditions, the TA removal rate of 1.0 % CNC composites reached 80.4 %. Furthermore, the sponge retained a TA removal rate of 54 % after five cycles of adsorption and desorption using 50 % ethanol. The results demonstrated that CNC/FG composite sponge has application potential in the field of adsorption materials for TA. [Display omitted] • Porous fish gelatin sponge is capable of removing tannic acid. • Cellulose nanocrystals additives bonded with fish gelatin through hydrogen bonds. • Additives improved mechanical strength, thermal stability, and swelling property. • 1 % cellulose nanocrystals addition achieved best tannic acid removal (80.4 %). • Composite sponge's adsorption follows the pseudo-second-order kinetic model. [ABSTRACT FROM AUTHOR]

Accurate and reliable segmentation of colorectal tumors and surrounding colorectal tissues on 3D magnetic resonance images has critical importance in preoperative prediction, staging, and radiotherapy. Previous works simply combine multilevel features without aggregating representative semantic information and without compensating for the loss of spatial information caused by down-sampling. Therefore, they are vulnerable to noise from complex backgrounds and suffer from misclassification and target incompleteness-related failures. In this paper, we address these limitations with a novel adaptive lesion-aware attention network (ALA-Net) which explicitly integrates useful contextual information with spatial details and captures richer feature dependencies based on 3D attention mechanisms. The model comprises two parallel encoding paths. One of these is designed to explore global contextual features and enlarge the receptive field using a recurrent strategy. The other captures sharper object boundaries and the details of small objects that are lost in repeated down-sampling layers. Our lesion-aware attention module adaptively captures long-range semantic dependencies and highlights the most discriminative features, improving semantic consistency and completeness. Furthermore, we introduce a prediction aggregation module to combine multiscale feature maps and to further filter out irrelevant information for precise voxel-wise prediction. Experimental results show that ALA-Net outperforms state-of-the-art methods and inherently generalizes well to other 3D medical images segmentation tasks, providing multiple benefits in terms of target completeness, reduction of false positives, and accurate detection of ambiguous lesion regions. [ABSTRACT FROM AUTHOR]

Oriented strandboards were constructed from various combinations of mixed hardwood flakes. Fractal dimension of void size (FDVS) in the manufactured samples was studied by means of X-ray computer tomography images and a computer image processing technique. The FDVS value was high in the panel surface layers and decreased toward the panel center, and the FDVS decrement was linear to increased porosity. At the same overall porosity level, the FDVS varied as function of void distribution. FDVS could be a useful additional parameter for characterizing the internal void structure of strand-based wood composites. [ABSTRACT FROM AUTHOR]

A complete digital tooth model with both the dental crown and root is of great importance for computer-aided orthodontic treatment. This paper first proposes an automatic segmentation method for complete tooth models with both the crown and reconstructed root based on multimodal data. With the laser-scanned crown mesh and cone-beam computed tomography (CBCT) data of a patient, we propose an improved iterative closest point algorithm and convex hull selection method to obtain the initial contour and slice for segmentation. Based on the initialization, we propose an improved level set method with the shape prior, named LSS, to segment the root of the tooth slice by slice. After segmentation, we reconstruct the root model and replace the crown part with the scanned crown model to solve the occlusal problem. The experiments demonstrate that our method can obtain tooth models from CBCT automatically and accurately. [ABSTRACT FROM AUTHOR]

As a natural macromolecule-based biomaterial, fish gelatin is used in medical materials for its low pathogen infection risk. However, because of poor mechanical properties, its application has been limited. In this study, microcrystalline cellulose-reinforced fish gelatin (FG/MCC) composite films were prepared with a biological cross-linking agent (genipin) under ultrasonic treatment. SEM micrographs showed that the smooth microstructure of FG film became increasingly disordered with the addition of MCC. The infrared spectrum analysis (FTIR) demonstrated the existence of hydrogen bond interaction between MCC and FG. Compared with the pure FG film, the tensile strength (TS) and modulus of elasticity (MOE) of composite films with MCC were improved, and the elongation at break (EAB) and swelling ratios (SR) were decreased. Ultrasonic treatment could further improve TS, MOE, and SR. When the composite film was prepared with 15% MCC and treated with ultrasound, the TS and MOE increased by 115% and 227%, respectively, while the EAB decreased by 35% and the SR decreased by 4% in comparison with pure FG films. Thermo-gravimetric analysis (TGA) showed that the FG/MCC composite films were stable below 100 °C. The above results indicate that the FG/MCC films have optimistic application prospects in the biomedical field. [ABSTRACT FROM AUTHOR]

Abstract: Many prevalent visualization packages can be used to visualize the GDP data from different perspectives. However, it is difficult to integrate these visualizations and provide a comprehensive analysis to assist users get deeper insights into the various economic features of GDP data, due to its spatio-temporal and multidimensional attributes. In this paper, we propose a visualization tool for the analysis of spatio-temporal multidimensional GDP data, aiming at the combination of the extraction of economic clusters in a time period and the track of dynamic feature evolutions across time periods. MDS is first employed to reduce the multiple dimensions of GDP data, in which the attributes used to achieve similarity matrix are selected interactively by users, according to their requirements. The 2D coordinates obtained by MDS are further clustered based on a hierarchical clustering scheme, allowing the analysts to visually capture the economic features of interest in a time period. We also design a temporal visualization to visually present the dynamic changes of clusters, which largely helps users track the various evolutions of economic features. In addition, stability is defined to evaluate the disorder of clusters between adjacent time periods and used to map meaningful colors to different glyphs in the visualizations. A rich set of interactions are further provided to help users highlight and explore economic features of interest. We demonstrate the usefulness of our system in two case studies based on a real-world GDP data of China.Graphical abstract: [ABSTRACT FROM AUTHOR]

With the rapid development of industrial society, air pollution has become a major issue in the modern world. The development and widespread deployment of sensors has enabled the collection of air-quality datasets with detailed spatial and temporal scales. Analyses of these spatiotemporal air-quality datasets can help decision makers explore the major causes of air pollution and find efficient solutions. The authors designed a visual analytics system that uses multidimensional scaling (MDS) to transform the air-quality data from monitor stations into 2D plots and uses hierarchical clustering, Voronoi diagrams, and storyline visualizations to help experts explore various attributes and time scales in the data. [ABSTRACT FROM AUTHOR]

View path design is used to generate proper animations for time-varying volume datasets, and it is crucial to show the evolution of features in the animation. In this paper, we present a novel view path design method to display the evolution of features with their topology. Firstly, feature extraction and tracking methods are employed to capture the temporal feature evolution. Then, the viewpoint quality is estimated by combining the visual information based on the viewpoint mutual information with the topology information based on the skeletons of features. Temporal viewpoint coherence is further proposed to partition the time range, and the volume datasets in each time segment share a fixed viewpoint. At last, the viewpoints in adjacent time segments are linked with a smooth view path, by means of which the user is able to explore the complex feature evolution in the time-varying volume dataset. Experimental results demonstrate the utility of the proposed topology aware view path design method. Graphical Abstract: [ABSTRACT FROM AUTHOR]

Hierarchical porous carbon materials were successfully prepared and controlled through a multi-step carbonization and mild-activation route using soybean straw from agricultural and forestry waste as raw material. Developing the unique hierarchical structure of soybean straw as a template during pre-carbonization process, is beneficial for regulation of micro-mesoporous structure at mild-activation route. The specific surface area of the obtained sample is controlled by adjusting the pyrolysis temperature, and reaches 2266.19 m2 g−1 in the presence of a small amount of alkaline activator (mass ratio of soybean straw carbon: KOH=1:2). Moreover, the abundant porosity and specific chemical structure of nitrogen and oxygen in soybean straw-based carbon materials are favorable for an better electrochemical behavior of carbon-based supercapacitor devices. In the three-electrode system, the optimal sample (SSC-700) exhibits the capacitance as high as 380.5 F g−1 at a current density of 0.5 A g−1, and capacitance retention 73.97% after 10000 charge and discharge cycles, and produces a high energy density of 13.2 W h kg−1 at a power density of 52.03 W kg−1. In the two-electrode system, the SSC-700-based symmetric supercapacitor exhibits a high energy density of 8.95 W h kg−1 at a power density of 25 W kg−1 and is able to maintain 5 W h kg−1 at 2500 W kg−1. Furthermore, carbon-based symmetrical supercapacitors also show good cycle capacity, with a capacity loss rate of 0.5% in 5000 cycles. This work shows that soybean straw-based carbon materials have great application potential in high-performance energy storage devices Biomass carbon-based supercapacitors is efficiently constructed through coupling multi-step carbonization and mild-activation strategy from soybean straw. [Display omitted] • Biomass-based hierarchical porous carbon is prepared from soybean straw. • Multi-step carbonization and mild-activation improve the electrochemical performance. • Soybean straw waste can be used to produce highly efficient supercapacitors. [ABSTRACT FROM AUTHOR]

Visualizing multivariate volume data is useful when the user wants to inspect the correlational distributions of multiple variables in a spatial field. Existing solutions commonly rely on color blending or weaving techniques to show multiple variables on a sampling point, probably causing heavy visual confusion. This paper presents an alternative solution that employs a multi-class sampling technique to generate spatially separated sampling points for multiple variables and illustrates the sampling points of each variable individually. We combine this new sampling scheme with the conventional direct volume rendering mode, iso-surface mode, and the cutting plane mode to support interactive inspection of volumetric distributions of multiple variables. The effectiveness of our approach is demonstrated with the IEEE VIS Contest 2004 Hurricane dataset and a 3D nuclear fusion simulation dataset. [ABSTRACT FROM AUTHOR]

A simple and effective mixing carbonization-activation process was developed to prepare rice hull-derived porous Si–carbon materials. The morphologies and pore structures of the materials were controlled effectively without any loading or additions at various carbonization temperatures. The structures of the samples changed from large pores and thick walls after 800 °C carbonization to small pores and thin walls after 1000 °C carbonization. An additional alkali activation–carbonization process led to coral reef-like structures surrounded by squama in the sample that underwent 900 °C carbonization (Act-RH-900). This optimal material (Act-RH-900) had a large specific surface area (768 m2 g−1), relatively stable specific capacitance (150.8 F g−1), high energy density (31.9 Wh kg−1), and high-power density (309.2 w kg−1) at a current density of 0.5 A g−1 in 1 M KOH electrolyte, as well as a good rate performance and high stability (capacitance retention > 87.88% after 5000 cycles). The results indicated that Act-RH-900 is a promising candidate for capacitive applications. This work overcomes the restrictions imposed by the complex internal structure of biomass, implements a simple reaction environment, and broadens the potential applicability of biomass waste in the field of supercapacitors. [ABSTRACT FROM AUTHOR]

Constructing biomass hierarchical porous carbon composite electrodes with ultra-high specific surface area, reasonable distribution of pores, and fast redox atoms for super-capacitors (SCs), is still a constant pursuit goal of current researchers. Herein, biochar containing heteroatoms with micro-meso-macro porous were successfully prepared from soybean straw as the precursor. Remarkably, an exclusive synthesis strategy, via hydrothermal reaction, alkali activation, and acid etching dual chemical treatments at specific carbonization temperature, was applied for controlling pore structure in this work, affording three dimensions (3D) hierarchical porous and a certain disordered graphitized structure of carbon material: SSP-900. SSP-900 exhibits the property of ultra-high specific surface area and reasonable distribution (S BET =1283 m2 g−1, S micro /S BET = 65%, Pore Volume= 0.73 cm3 g−1). Furthermore, it also displays the key merits of electrode materials candidate for SCs, including the superb specific capacity of 357 F g−1 at 0.5 A g−1 in the three-electrode system. Additionally, single electrode of SSP-900//SSP-900 demonstrates a splendid specific capacitance of 320 F g−1 in current density of 0.15 A g−1, the high energy density (11.2 Wh kg−1) and power density (37.8 W kg−1), a great rate capability retention (42%), and outstanding cycling stability up to 10,000 times (only 10% loss of capacitance) in two-electrode system. Its splendid electrochemical performances originate from the synergistic contribution of the special pore structure and built-in redox atom (N and O). As such SSP-900 may be seen as a promising SCs electrode material. This research provides an effective method for controlling the pore structure of biochar to be of great application in the SCs field. • Soybean straw waste is used to prepare 3-D hierarchical porous carbon electrode material. • Carbon materials show micro-meso-macroporous and disordered graphitized structure. • Its performance has been improved by combined alkali activation with acid etching. [ABSTRACT FROM AUTHOR]

Blind motion deblurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. Although significant progress has been made on tackling this problem, existing methods, when applied to highly diverse natural images, are still far from stable. This paper focuses on the robustness of blind motion deblurring methods toward image diversity—a critical problem that has been previously neglected for years. We classify the existing methods into two schemes and analyze their robustness using an image set consisting of 1.2 million natural images. The first scheme is edge-specific, as it relies on the detection and prediction of large-scale step edges. This scheme is sensitive to the diversity of the image edges in natural images. The second scheme is nonedge-specific and explores various image statistics, such as the prior distributions. This scheme is sensitive to statistical variation over different images. Based on the analysis, we address the robustness by proposing a novel nonedge-specific adaptive scheme (NEAS), which features a new prior that is adaptive to the variety of textures in natural images. By comparing the performance of NEAS against the existing methods on a very large image set, we demonstrate its advance beyond the state-of-the-art. [ABSTRACT FROM AUTHOR]

Demand for high-performance biocomposites is increasing due to their ease of processing, low environmental impact, and in-service performance. This study investigated the effect of boric acid modification of wood flour on polycarbonate (PC) wood composites' thermal stability, fire retardancy, water absorption, and creep behavior. The composites' fire retardancy increased with increasing wood flour content, and their char residue increased by 102.3% compared to that of pure PC. However, the water absorption of the resulting composites increased due to the hydroxyl groups of the wood flour. Wood flour also improved the composites' anti-creep properties. The excellent fire retardancy and anti-creep properties of wood–PC composites expand their use in the construction sector. [ABSTRACT FROM AUTHOR]

In the online published article, electronic supplementary material has been published incorrectly. The correct supplementary material is uploaded. [ABSTRACT FROM AUTHOR]