A PAM hydrogel surface-coated hydroponic bamboo evaporator with efficient thermal utilization for solar evaporation.

Solar-driven interfacial water purification emerges as a sustainable technology for seawater desalination and wastewater treatment to address the challenge of water scarcity. Currently, the energy losses via radiation and convection to surrounding environment minimize its energy efficiency. Therefore, it is necessary to develop strategies to minimize the heat losses for efficient water purification. Here, a novel evaporator was developed through the in situ gelation of PAM hydrogel on the surface carbonized hydroponic bamboo (PSC) to promote energy efficiency. The inherent porous and layered network structures of bamboo, in synergy with the functional hydration capacity of PAM hydrogel, facilitated adequate water transportation, while reducing evaporation enthalpy. The PAM hydrogel firmly covered on the photothermal layer surface effectively minimized the radiation and convection heat losses, while further harvesting those thermal energy that would otherwise dissipate into the surrounding environment. The reduced thermal conductivity of PSC served as a thermal insulator as well, obstructing heat transfer to bulk water and thus diminishing conduction losses. Consequently, the rational designed PSC could efficiently convert solar energy to purified water, leading to the evaporation of 2.09 kg m-2 h-1, the energy efficiency of 87.6 % under one sun irradiation, and yielding 9.6 kg m-2 fresh water over 11 h outdoor operation. Moreover, the PSC also performs excellent salt rejection, and long-term stability at outdoor experiment. These results demonstrated high and stable solar evaporation performance could be achieved if turning heat losses into a way of extra energy extraction to further enhance the evaporation performance. This strategy appears to be a promising strategy for effective thermal energy management and practical application.

Mystique: Deconstructing SVG Charts for Layout Reuse.

To facilitate the reuse of existing charts, previous research has examined how to obtain a semantic understanding of a chart by deconstructing its visual representation into reusable components, such as encodings. However, existing deconstruction approaches primarily focus on chart styles, handling only basic layouts. In this paper, we investigate how to deconstruct chart layouts, focusing on rectangle-based ones, as they cover not only 17 chart types but also advanced layouts (e.g., small multiples, nested layouts). We develop an interactive tool, called Mystique, adopting a mixed-initiative approach to extract the axes and legend, and deconstruct a chart's layout into four semantic components: mark groups, spatial relationships, data encodings, and graphical constraints. Mystique employs a wizard interface that guides chart authors through a series of steps to specify how the deconstructed components map to their own data. On 150 rectangle-based SVG charts, Mystique achieves above 85% accuracy for axis and legend extraction and 96% accuracy for layout deconstruction. In a chart reproduction study, participants could easily reuse existing charts on new datasets. We discuss the current limitations of Mystique and future research directions.

Reducing Ambiguities in Line-Based Density Plots by Image-Space Colorization.

Line-based density plots are used to reduce visual clutter in line charts with a multitude of individual lines. However, these traditional density plots are often perceived ambiguously, which obstructs the user's identification of underlying trends in complex datasets. Thus, we propose a novel image space coloring method for line-based density plots that enhances their interpretability. Our method employs color not only to visually communicate data density but also to highlight similar regions in the plot, allowing users to identify and distinguish trends easily. We achieve this by performing hierarchical clustering based on the lines passing through each region and mapping the identified clusters to the hue circle using circular MDS. Additionally, we propose a heuristic approach to assign each line to the most probable cluster, enabling users to analyze density and individual lines. We motivate our method by conducting a small-scale user study, demonstrating the effectiveness of our method using synthetic and real-world datasets, and providing an interactive online tool for generating colored line-based density plots.

Balancing Accuracy and Speed in Gaze-Touch Grid Menu Selection in AR via Mapping Sub-Menus to a Hand-Held Device.

Eye gaze can be a potentially fast and ergonomic method for target selection in augmented reality (AR). However, the eye-tracking accuracy of current consumer-level AR systems is limited. While state-of-the-art AR target selection techniques based on eye gaze and touch (gaze-touch), which follow the "eye gaze pre-selects, touch refines and confirms" mechanism, can significantly enhance selection accuracy, their selection speeds are usually compromised. To balance accuracy and speed in gaze-touch grid menu selection in AR, we propose the Hand-Held Sub-Menu (HHSM) technique.tou HHSM divides a grid menu into several sub-menus and maps the sub-menu pointed to by eye gaze onto the touchscreen of a hand-held device. To select a target item, the user first selects the sub-menu containing it via eye gaze and then confirms the selection on the touchscreen via a single touch action. We derived the HHSM technique's design space and investigated it through a series of empirical studies. Through an empirical study involving 24 participants recruited from a local university, we found that HHSM can effectively balance accuracy and speed in gaze-touch grid menu selection in AR. The error rate was approximately 2%, and the completion time per selection was around 0.93 s when participants used two thumbs to interact with the touchscreen, and approximately 1.1 s when they used only one finger.

Quality of reporting of integrative Chinese and Western medicine intervention in randomized controlled trials of ulcerative colitis: a review.

BACKGROUND: Integrative Chinese and Western medicine (ICWM) is commonly used for the treatment of ulcerative colitis (UC) in clinical practice. However, it is unclear whether the details of ICWM interventions, such as selection rationale, implementation design, and potential interactions, were adequately reported. Therefore, this study aimed to assess the quality of reporting in the ICWM interventional randomized controlled trials (RCTs) of UC and to identify the common problems if any. METHODS: Through a search of 10 international electronic databases, we identified RCTs of UC with ICWM interventions published in English or Chinese from the inception date of each database up to 16 June 2023. Literature screening was strictly conducted based on the inclusion and exclusion criteria of the Population, Concept, and Context (PCC) framework. The general characteristics of the included studies were described. The quality of reporting was assessed according to three checklists, including the CONSORT (Consolidated Standards of Reporting Trials) with 36 items (except for one item 1b about abstract), the CONSORT for Abstracts (17 items), and a self-designed ICWM-related checklist (27 items covering design rationale, intervention details, outcome assessments, and analysis). The reporting scores of RCTs published before and after 2010 were compared. RESULTS: A total of 1458 eligible RCTs were included. For the reporting compliance, the median score (interquartile ranges) of the CONSORT (72 score in total), the CONSORT for Abstract (34 score), and ICWM-related (54 score) items was 21 (18-25), 13 (12-15), and 18 (15-21), respectively. Although the time period comparisons showed that reporting quality of included publications improved significantly after the CONSORT 2010 issued (P < 0.01), more than 50% of items were evaluated as poor quality (reporting rate < 65%) among each checklist, especially in the CONSORT for Abstract and ICWM-specific items. CONCLUSION: Although CONSORT appears to have enhanced the reporting of RCTs in UC, the quality of ICWM specifics is variable and in need of improvement. Reporting guidelines of the ICWM recommendations should be developed to improve their quality.

SmartSpring: A Low-Cost Wearable Haptic VR Display with Controllable Passive Feedback.

With the development of virtual reality, the practical requirements of the wearable haptic interface have been greatly emphasized. While passive haptic devices are commonly used in virtual reality, they lack generality and are difficult to precisely generate continuous force feedback to users. In this work, we present SmartSpring, a new solution for passive haptics, which is inexpensive, lightweight and capable of providing controllable force feedback in virtual reality. We propose a hybrid spring-linkage structure as the proxy and flexibly control the mechanism for adjustable system stiffness. By analyzing the structure and force model, we enable a smart transform of the structure for producing continuous force signals. We quantitatively examine the real-world performance of SmartSpring to verify our model. By asymmetrically moving or actively pressing the end-effector, we show that our design can further support rendering torque and stiffness. Finally, we demonstrate the SmartSpring in a series of scenarios with user studies and a just noticeable difference analysis. Experimental results show the potential of the developed haptic display in virtual reality.

Online view enhancement for exploration inside medical volumetric data using virtual reality.

BACKGROUND AND OBJECTIVE: Medical image visualization is an essential tool for conveying anatomical information. Ray-casting-based volume rendering is commonly used for generating visualizations of raw medical images. However, exposing a target area inside the skin often requires manual tuning of transfer functions or segmentation of original images, as preset parameters in volume rendering may not work well for arbitrary scanned data. This process is tedious and unnatural. To address this issue, we propose a volume visualization system that enhances the view inside the skin, enabling flexible exploration of medical volumetric data using virtual reality. METHODS: In our proposed system, we design a virtual reality interface that allows users to walk inside the data. We introduce a view-dependent occlusion weakening method based on geodesic distance transform to support this interaction. By combining these methods, we develop a virtual reality system with intuitive interactions, facilitating online view enhancement for medical data exploration and annotation inside the volume. RESULTS: Our rendering results demonstrate that the proposed occlusion weakening method effectively weakens obstacles while preserving the target area. Furthermore, comparative analysis with other alternative solutions highlights the advantages of our method in virtual reality. We conducted user studies to evaluate our system, including area annotation and line drawing tasks. The results showed that our method with enhanced views achieved 47.73% and 35.29% higher accuracy compared to the group with traditional volume rendering. Additionally, subjective feedback from medical experts further supported the effectiveness of the designed interactions in virtual reality. CONCLUSIONS: We successfully address the occlusion problems in the exploration of medical volumetric data within a virtual reality environment. Our system allows for flexible integration of scanned medical volumes without requiring extensive manual preprocessing. The results of our user studies demonstrate the feasibility and effectiveness of walk-in interaction for medical data exploration.

Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis.

We propose to use optimally ordered orthogonal neighbor-joining (O 3 NJ) trees as a new way to visually explore cluster structures and outliers in multi-dimensional data. Neighbor-joining (NJ) trees are widely used in biology, and their visual representation is similar to that of dendrograms. The core difference to dendrograms, however, is that NJ trees correctly encode distances between data points, resulting in trees with varying edge lengths. We optimize NJ trees for their use in visual analysis in two ways. First, we propose to use a novel leaf sorting algorithm that helps users to better interpret adjacencies and proximities within such a tree. Second, we provide a new method to visually distill the cluster tree from an ordered NJ tree. Numerical evaluation and three case studies illustrate the benefits of this approach for exploring multi-dimensional data in areas such as biology or image analysis.

Force-Directed Graph Layouts Revisited: A New Force Based on the T-Distribution.

In this paper, we propose the t-FDP model, a force-directed placement method based on a novel bounded short-range force (t-force) defined by Student's t-distribution. Our formulation is flexible, exerts limited repulsive forces for nearby nodes and can be adapted separately in its short- and long-range effects. Using such forces in force-directed graph layouts yields better neighborhood preservation than current methods, while maintaining low stress errors. Our efficient implementation using a Fast Fourier Transform is one order of magnitude faster than state-of-the-art methods and two orders faster on the GPU, enabling us to perform parameter tuning by globally and locally adjusting the t-force in real-time for complex graphs. We demonstrate the quality of our approach by numerical evaluation against state-of-the-art approaches and extensions for interactive exploration.

Target Netgrams: An Annulus-Constrained Stress Model for Radial Graph Visualization.

We present Target Netgrams as a visualization technique for radial layouts of graphs. Inspired by manually created target sociograms, we propose an annulus-constrained stress model that aims to position nodes onto the annuli between adjacent circles for indicating their radial hierarchy, while maintaining the network structure (clusters and neighborhoods) and improving readability as much as possible. This is achieved by having more space on the annuli than traditional layout techniques. By adapting stress majorization to this model, the layout is computed as a constrained least square optimization problem. Additional constraints (e.g., parent-child preservation, attribute-based clusters and structure-aware radii) are provided for exploring nodes, edges, and levels of interest. We demonstrate the effectiveness of our method through a comprehensive evaluation, a user study, and a case study.

Taurus: Towards a Unified Force Representation and Universal Solver for Graph Layout.

Over the past few decades, a large number of graph layout techniques have been proposed for visualizing graphs from various domains. In this paper, we present a general framework, Taurus, for unifying popular techniques such as the spring-electrical model, stress model, and maxent-stress model. It is based on a unified force representation, which formulates most existing techniques as a combination of quotient-based forces that combine power functions of graph-theoretical and Euclidean distances. This representation enables us to compare the strengths and weaknesses of existing techniques, while facilitating the development of new methods. Based on this, we propose a new balanced stress model (BSM) that is able to layout graphs in superior quality. In addition, we introduce a universal augmented stochastic gradient descent (SGD) optimizer that efficiently finds proper solutions for all layout techniques. To demonstrate the power of our framework, we conduct a comprehensive evaluation of existing techniques on a large number of synthetic and real graphs. We release an open-source package, which facilitates easy comparison of different graph layout methods for any graph input as well as effectively creating customized graph layout techniques.

SizePairs: Achieving Stable and Balanced Temporal Treemaps using Hierarchical Size-based Pairing.

We present SizePairs, a new technique to create stable and balanced treemap layouts that visualize values changing over time in hierarchical data. To achieve an overall high-quality result across all time steps in terms of stability and aspect ratio, SizePairs employs a new hierarchical size-based pairing algorithm that recursively pairs two nodes that complement their size changes over time and have similar sizes. SizePairs maximizes the visual quality and stability by optimizing the splitting orientation of each internal node and flipping leaf nodes, if necessary. We also present a comprehensive comparison of SizePairs against the state-of-the-art treemaps developed for visualizing time-dependent data. SizePairs outperforms existing techniques in both visual quality and stability, while being faster than the local moves technique.

Iron carbide nanoparticles supported on an N-doped carbon porous framework as a bifunctional material for electrocatalytic oxygen reduction and supercapacitors.

Highly active and durable bifunctional materials are of pivotal importance for energy conversion and storage devices, yet a comprehensive understanding of their geometric and electronic influence on electrochemical activity is urgently needed. Fe-N-C materials with physical and chemical structural merits are considered as one of the promising candidates for efficient oxygen reduction reaction electrocatalysts and supercapacitor electrodes. Herein, Fe3C nanoparticles supported on a porous N-doped carbon framework (denoted as Fe3C/PNCF) were readily prepared by one-step chemical vapor deposition under the assistance of a NaCl salt template. The experiment results revealed that the as-synthesized Fe3C/PNCF nanocomposites successfully displayed attractive electrocatalytic oxygen reduction reaction (ORR) activity comparable to that of the Pt/C catalyst (E1/2 of 0.84 V and 0.83 V, respectively), and a superior capacitance of 385.3 F g-1 under 1 A g-1 for a supercapacitor. It's proposed that the increased pyridinic and graphitic N coordination on the hydrophilic porous framework provides more electrochemical active surface area for the storage and transport of electrolyte ions. Additionally, an appropriate d-band center created by the optimized adsorption function endows Fe3C/PNCF with excellent electrochemical properties. The results confirmed that the integration strategy of porous heterogeneous structure and accessible active sites balanced the complex relationship between geometry, electronic structure, and electrochemical activity. Our research provides a facile approach for fabricating multi-functional nanomaterials applicable in both ORR and supercapacitors in the future.

Electrochemical deposition of flower-like nanostructured silver particles with a PVA modified carbon cloth cathode.

A novel electrochemical method for preparing flower-like nanostructured silver particles using polyvinyl alcohol (PVA) modified carbon cloth as a cathode is reported. The method does not involve the use of any morphological control agents in aqueous solution. The morphology of the silver nanoparticles obtained was studied using scanning electron microscopy (SEM) and X-ray diffractometry (XRD). The effects of the operating conditions on the deposited silver nanoparticles were investigated. It was found that PVA concentration for carbon cloth modification had a significant effect on the deposited silver morphology. With 1% PVA modification, current density of 10 µA cm-2 and silver nitrate concentration of 1 mM, a flower-like nanostructured silver with petal thickness of 100 nm can be prepared. With the reaction proceeding, silver nanocrystals nucleated on the cathode in a few seconds, then the nuclei grew and the rudimental flower-like silver started to form in 1 min. The perfect flower-like nanostructure of silver was formed in 20 min. However overlong reaction time led to micrometer sized blocks. The specific silver nanostructure growth might be attributed to the silver ion concentration gradient caused by reaction and diffusion rate and the effects of PVA.

Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study.

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniques would yield various patterns, which significantly affect the performance of visual cluster analysis tasks. We present the results of a user study that investigates the influence of different DR techniques on visual cluster analysis. Our study focuses on the most concerned property types, namely the linearity and locality, and evaluates twelve representative DR techniques that cover the concerned properties. Four controlled experiments were conducted to evaluate how the DR techniques facilitate the tasks of 1) cluster identification, 2) membership identification, 3) distance comparison, and 4) density comparison, respectively. We also evaluated users' subjective preference of the DR techniques regarding the quality of projected clusters. The results show that: 1) Non-linear and Local techniques are preferred in cluster identification and membership identification; 2) Linear techniques perform better than non-linear techniques in density comparison; 3) UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) perform the best in cluster identification and membership identification; 4) NMF (Nonnegative Matrix Factorization) has competitive performance in distance comparison; 5) t-SNLE (t-Distributed Stochastic Neighbor Linear Embedding) has competitive performance in density comparison.

F2-Bubbles: Faithful Bubble Set Construction and Flexible Editing.

In this paper, we propose F2-Bubbles, a set overlay visualization technique that addresses overlapping artifacts and supports interactive editing with intelligent suggestions. The core of our method is a new, efficient set overlay construction algorithm that approximates the optimal set overlay by considering set elements and their non-set neighbors. Thanks to the efficiency of the algorithm, interactive editing is achieved, and with intelligent suggestions, users can easily and flexibly edit visualizations through direct manipulations with local adaptations. A quantitative comparison with state-of-the-art set visualization techniques and case studies demonstrate the effectiveness of our method and suggests that F2-Bubbles is a helpful technique for set visualization.

Joint t-SNE for Comparable Projections of Multiple High-Dimensional Datasets.

We present Joint t-Stochastic Neighbor Embedding (Joint t-SNE), a technique to generate comparable projections of multiple high-dimensional datasets. Although t-SNE has been widely employed to visualize high-dimensional datasets from various domains, it is limited to projecting a single dataset. When a series of high-dimensional datasets, such as datasets changing over time, is projected independently using t-SNE, misaligned layouts are obtained. Even items with identical features across datasets are projected to different locations, making the technique unsuitable for comparison tasks. To tackle this problem, we introduce edge similarity, which captures the similarities between two adjacent time frames based on the Graphlet Frequency Distribution (GFD). We then integrate a novel loss term into the t-SNE loss function, which we call vector constraints, to preserve the vectors between projected points across the projections, allowing these points to serve as visual landmarks for direct comparisons between projections. Using synthetic datasets whose ground-truth structures are known, we show that Joint t-SNE outperforms existing techniques, including Dynamic t-SNE, in terms of local coherence error, Kullback-Leibler divergence, and neighborhood preservation. We also showcase a real-world use case to visualize and compare the activation of different layers of a neural network.

SPEULER: Semantics-preserving Euler Diagrams.

Creating comprehensible visualizations of highly overlapping set-typed data is a challenging task due to its complexity. To facilitate insights into set connectivity and to leverage semantic relations between intersections, we propose a fast two-step layout technique for Euler diagrams that are both well-matched and well-formed. Our method conforms to established form guidelines for Euler diagrams regarding semantics, aesthetics, and readability. First, we establish an initial ordering of the data, which we then use to incrementally create a planar, connected, and monotone dual graph representation. In the next step, the graph is transformed into a circular layout that maintains the semantics and yields simple Euler diagrams with smooth curves. When the data cannot be represented by simple diagrams, our algorithm always falls back to a solution that is not well-formed but still well-matched, whereas previous methods often fail to produce expected results. We show the usefulness of our method for visualizing set-typed data using examples from text analysis and infographics. Furthermore, we discuss the characteristics of our approach and evaluate our method against state-of-the-art methods.

KD-Box: Line-segment-based KD-tree for Interactive Exploration of Large-scale Time-Series Data.

Time-series data-usually presented in the form of lines-plays an important role in many domains such as finance, meteorology, health, and urban informatics. Yet, little has been done to support interactive exploration of large-scale time-series data, which requires a clutter-free visual representation with low-latency interactions. In this paper, we contribute a novel line-segment-based KD-tree method to enable interactive analysis of many time series. Our method enables not only fast queries over time series in selected regions of interest but also a line splatting method for efficient computation of the density field and selection of representative lines. Further, we develop KD-Box, an interactive system that provides rich interactions, e.g., timebox, attribute filtering, and coordinated multiple views. We demonstrate the effectiveness of KD-Box in supporting efficient line query and density field computation through a quantitative comparison and show its usefulness for interactive visual analysis on several real-world datasets.

Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization.

We present a pyramid-based scatterplot sampling technique to avoid overplotting and enable progressive and streaming visualization of large data. Our technique is based on a multiresolution pyramid-based decomposition of the underlying density map and makes use of the density values in the pyramid to guide the sampling at each scale for preserving the relative data densities and outliers. We show that our technique is competitive in quality with state-of-the-art methods and runs faster by about an order of magnitude. Also, we have adapted it to deliver progressive and streaming data visualization by processing the data in chunks and updating the scatterplot areas with visible changes in the density map. A quantitative evaluation shows that our approach generates stable and faithful progressive samples that are comparable to the state-of-the-art method in preserving relative densities and superior to it in keeping outliers and stability when switching frames. We present two case studies that demonstrate the effectiveness of our approach for exploring large data.