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As one of the effective means of representing geographic information, geographic visualization can directly improve the cognitive efficiency of users who are perceiving geospatial data. The existing geographic information visualization relies heavily on the background knowledge and visualization skills the data workers own. Therefore, the geographic visualization task is usually very time-consuming and challenging. To lower the barrier of visualization of geographical data, we propose a novel recommendation system of geographic information visualization called GeoVis. This system extracts the distribution characteristics with adaptive kernel density estimation and recommends the map type (scatter, bubble, hexbin and heatmap) that can best reflect the regularity of data distribution based on latent code. The key idea of how the data-driven recommendation works is to use latent code to express and decouple data features and then learn the mapping between data features and visual styles. At the same time, this system recommends design choices (e.g., map styles and color schemes). Users only need to browse the recommendation results to realize explorations and analyses of the dataset, which will greatly improve their work efficiency. We conduct a series of evaluation experiments on the proposed system, including a case study. The experiment results show that the system is practical and effective and can perform the task of recommending informative and esthetic geographical visualization results well. [ABSTRACT FROM AUTHOR]

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system. [ABSTRACT FROM AUTHOR]

Contamination by odor substances such as geosmin (GSM) and 2-methylisoborneol (2-MIB) was examined in the cultured water from aquaculture farming in the region of the Hongze Lake in 2022, and some factors influencing residual levels of them in the water were analyzed. Geographically, high concentrations of GSM were located mainly in the north and northeast culture areas of the lake, while those of 2-MIB were found in the northeast and southwest. Analysis of the water in the enclosure culture revealed significant differences in the concentrations of GSM and 2-MIB among the cultured species. The mean concentrations of GSM in culture water were ranked in the order: crab > the four major Chinese carps > silver and bighead carp, and silver and bighead carp > crab > the four major Chinese carps for 2-MIB. The concentration of GSM was significantly higher at 38.99 ± 18.93 ng/L in crab culture water compared to other fish culture water. Significant differences were observed in GSM concentrations between crab enclosure culture and pond culture, while 2-MIB levels were comparable. These findings suggest that cultural management practices significantly affect the generation of odor substances. The taste and odor (T&O) assessment revealed that the residual levels of GSM and 2-MIB in most samples were below the odor threshold concentrations (OTCs), although high levels of GSM and 2-MIB in all water bodies were at 30.9% and 27.5%, respectively. Compared with the corresponding data from other places and the regulation guidelines of Japan, USA, and China, the region in the Hongze Lake is generally classified as a slightly T&O area, capable of supporting the aquaculture production scale. [ABSTRACT FROM AUTHOR]

Spatio-temporal Scene Graphs Generation (STSGG) aims to extract a sequence of graph-based semantic representations for high-level visual tasks. Existing works often fail to exploit the strong temporal correlation and the details of local features, which leads to the inability to distinguish the action between dynamic relation (e.g., drinking) and static relation (e.g., holding). Furthermore, due to bad long-tailed bias, the prediction results are troubled by inaccurate tail predicates classifications. To address these issues, a slowfast local-aware attention (SFLA) Network is proposed for temporal modeling in STSGG. First, a two-branch network is used to extract static and dynamic relation features respectively. Second, a local relation-aware attention (LRA) module is proposed to attach higher importance to the crucial elements in the local relationship. Third, three novel self-supervision prediction tasks are proposed, that is, spatial location, human attention state, and distance variation. Such self-supervision tasks are trained simultaneously with the main model to alleviate the long-tailed bias problem and enhance feature discrimination. Systematic experiments show that our method achieves state-of-the-art performance in the recently proposed Action Genome (AG) dataset and the popular ImageNet Video dataset. [ABSTRACT FROM AUTHOR]

The fashion clothing and items classification is challenging to incorporate category/sub-category classification and attributes prediction for numerous fashion items into a compact multitask learning infrastructure. The main motive of this research is to improve the fashion items categorization and their attributes prediction from extracted visual features. We proposed a novel fashion sub-categories and attributes prediction (FS C AP) model using deep learning techniques. In this proposed model, YOLO and DeepSORT architectures are used for person detection and tracking, Faster-RCNN architecture is used for sub-categories classification, and Custom-EfficientNet-B3 architecture is designed for attributes prediction. Twenty-four distinct modules are designed to increase the attributes classification accuracy for detected fashion items again each sub-category. The performance of the proposed model is evaluated on a customized fully annotated FashionItem dataset. The experimental results clearly show that the proposed model outperforms the recent baseline methods in fashion sub-categories and attributes prediction. [ABSTRACT FROM AUTHOR]

Categorical data charts composed of adjacent color blocks, such as pie charts and stack bar charts, are widely used in daily life and scientific research. Color is a significant channel in such charts. In general, colors are used to distinguish the categories of these data. In some scenes of low color accuracy, the indistinguishability of colors will make these charts challenging to read. Similar colors and poor viewing conditions make it difficult for people to read charts, and people spend more time distinguishing the boundaries of color blocks. There have been previous works by modifying the palette to achieve the distinction between colors. However, in many scenarios, people need to use a palette that meets a particular style and does not allow modification. There is also a method to optimize the color assignment with a given palette, but the method is only designed for scatter plots. In this paper, we propose an automatic coloring method for optimizing the distinguishability of blocks with a given palette based on graph theory and color science. We consider the adjacency of blocks in visual charts as a graph structure and take into account the color difference, block size, and color harmony. To demonstrate the method's effectiveness, we compared our results with those of another color assignment method. We also use a class visibility measurement method and an aesthetic evaluation method based on deep learning to evaluate each method's results. The results show that our method can guarantee the distinguishability of the color blocks and produce a sufficiently harmonious visualization. [ABSTRACT FROM AUTHOR]

Peer-to-peer accommodation is developing rapidly in the era of sharing economy, and the visual recommendation of accommodation is also an urgent problem to be solved. Meanwhile, user-generated content is critical in P2P accommodations, because they contain a wealth of information about the opinions and experiences of users, which helps understand consumer decisions and improve products and services better. However, the huge volume of reviews makes it difficult for potential customers to gain useful insights and for managers to track customer opinions. In this paper, we propose a complete pipeline for recommending personalized accommodations for consumers, while also providing insights for managers. First, we use topic modeling techniques to mining opinions from review. Second, we build a deep learning network for review sentiment analysis. Third, we perform sentiment analysis of the reviews at the aspect level to obtain the sentiment vector representation of the accommodation. Finally, we propose a personalized accommodation recommendation method based on the above work. Moreover, we design a visual analytic system with a user-friendly interface to facilitate interactive analysis. Evaluation including user and case studies demonstrates the usefulness and effectiveness of our method and system. [ABSTRACT FROM AUTHOR]

With the growth of hierarchical data in public opinion analysis, new visualization methods that can intuitively present this kind of data are urgently needed. In this paper, we propose VineMap, a new visualization method with a vine metaphor form. Different from other public opinion visualizations, we devote more attention to visualize both the hierarchical structure of texts and the semantic orientation in content. First, we extract a hierarchical topic model from text data. Then we design a visualization based on a vine metaphor form to enable users to understand public opinion in hierarchical form. At the same time, we propose heuristic optimized strategies for the visualization layout. VineMap is applied both on unstructured text data and structured data to demonstrate its applicability. The evaluations not only show users' perceptions to our method but also prove its good performance with respect to generation time, space utilization and visual effect. [ABSTRACT FROM AUTHOR]

The optimization of terminal airspace can provide more airport capacity to meet the growing aviation demand. Improving flight procedures is an important prerequisite for optimizing airspace. The existing air-route network visualization cannot fully meet the assessment needs of the terminal flight procedure. In this research, we introduce an analysis tool, VEFP, that provides multiple visualizations based on unlabeled flight trajectory data. The system can help domain experts to evaluate the terminal flight procedure from multiple perspectives, respectively. First, we provide a time series-based statistical information view to help determine the usage status of the flight procedure per unit time. Second, we evaluated the controller's use of space for flight procedures based on the location information in the data. Third, combined with the visualization method after data processing, the visual complexity is reduced and necessary details are displayed. Then, the users can directly observe the actual flight procedure. We evaluate flight procedures in actual use through cases and experiments and then discuss with users about the observations provided by the system. These results confirm that our system can help domain experts evaluate flight procedures. [ABSTRACT FROM AUTHOR]

A Chinese calligraphy copybook usually has a limited number of Chinese characters, far from a whole set of characters needed for typesetting. Therefore, there is a need to develop complete sets of Chinese calligraphy libraries for well-known calligrapher styles. This paper proposes an end-to-end network for character generation based on specific calligraphy styles. Specifically, a style transfer network is designed to transfer the style of characters, and a content supplement network is designed to capture the details of stylish strokes. Our model can generate high-quality calligraphy images without manually annotating data. To verify the generated calligraphy styles, a new dataset is constructed for experimental comparison between our method and two other baseline methods. Moreover, a user study is conducted to evaluate our generated calligraphy from a visual perspective. When the experiment participants are asked to distinguish the real calligraphy from generated samples, the correct rate was 53.5%. The results show that the calligraphy styles generated by our model are almost indistinguishable from the original works. [ABSTRACT FROM AUTHOR]

An appropriate location of the retailing store is vital for achieving business success. However, a massive amount of complex information needs to be considered in location selection, such as customer flow, the business environment, and current business performance. Unlike the traditional location recommendation method of statistical sampling, we establish a model of business-district attractiveness based on customer flow. Besides, we build an interactive visual analysis system with a user-friendly interface for an interactive visual query about complex business and environment information. Our system can help users select retailing store locations, support interactive visual queries and display rich information to facilitate managers in decision-making. [ABSTRACT FROM AUTHOR]

Hydroxyapatite (HA) is an ideal bone insert material due to its biocompatibility and osteoconductivity, but the poor mechanical properties limit its wide application in clinical practice. The aim of this work is to fabricate a novel composite that with similar porous structure to cortical bone and high mechanical properties by the addition of boron nitride (BN) and sintered in air at 1250 °C for 40 min. In mechanical properties, the maximum flexural strength, elastic modulus and fracture toughness are 94.04 MPa, 69.46 GPa and 1.16 MPa m1/2, respectively, which are improved by 20.56%, 12.87% and 8.41%, respectively. In the microscopy of surface and fracture surface, the porous structure similar to cortical bone is observed. There are microscale and nanoscale pores, and the pores present an interconnected structure. In vitro biocompatibility assessment, the addition of BN and its reaction product have no adverse effect on osteoblast viability, which even promotes osteogenic differentiation and mineralization. [ABSTRACT FROM AUTHOR]

The extensive distribution of portable digital devices brings demands of adapting media to restricted display spaces or various aspect ratios. Most existing content-aware visualization resizing approaches introduce nonlinear deformation to enhance or preserve salient areas while squeezing non-salient ones. Their deformation on image-based geographic context could cause confusion and make it harder to interpret by the general mobile user. In this paper, we describe a deformation-free approach to resize the geovisualization using density map, resulting in a collage layout of sub-maps, called map collage. We show our technique of constructing a collage layout for one frame of geospatial data and a strategy of extending it to the temporal dimension. We demonstrate the results on two sets of geotemporal data and conducted a survey comparing our method, the seam carving, and the uniform scaling, which shows that ours is more preferred than the other two. [ABSTRACT FROM AUTHOR]

Reconstructing a 3D fluid simulation (re-simulation) from video has practical significance. In this study, we address the realistic problem of fluid re-simulation in an inverse project. State-of-the-art studies on the inverse problem of fluid simulation have mainly focused on dimensionality reduction for acquiring better time performance, but the realistic aspect has rarely been investigated. This paper presents an extracting-mapping scheme to tightly couple fluid re-simulation with enhanced physically driven data for realistic high-quality dynamic detail. We make a full use of the details extracted from recovered physically driven data to improve the coarse and unrealistic re-simulation from fluid auto-advection. Two schemes are discussed. The first scheme is the density block method (DBM). In this method, a density block database is constructed from the prepartitioned and sorted density blocks, and then, some selected density blocks with dynamic details are selected from the preconstructed database and coupled coherently into the physically driven data to enhance the detail in every auto-advection cycle. The second method is the density spectrum block method (DSBM) in the frequency domain. Using the DSBM and DBM, realistic effects are achieved by extensive quantitative and qualitative evaluation via re-simulation tests driven by the recovered physical data from ground truth fluid video. Both approaches outperform previous auto-advection re-simulation schemes in terms of the rich detail under several challenging scenarios and low-level hardware conditions. [ABSTRACT FROM AUTHOR]

Modeling large-scale vegetation on terrain is an important and challenging task in computer games, movie production and other digital entertainment applications. In this work, we propose a novel example-based method for rapid generation of vegetation in outdoor natural environments. Its central idea is to learn the vegetation distribution on terrain via deep convolution neural networks. We first use a pre-trained deep neural network to extract rich local information from the terrain pertinent to vegetation distribution. Second, we produce the initial features of the target vegetation distribution based on patch matching and further introduce a network that generates a vegetation density map based on the initial features. Third, during the synthesis stage, we propose a procedural method to generate the vegetation distribution data corresponding to the terrain data. Our research work confirms that the image features extracted by the pre-trained deep neural network could be utilized to explore the connection between vegetation and terrain. We validate our new method over various outdoor scenes, including procedural generated scenes and scenes with manual control on tree patterns. The experimental results demonstrate that our method can rapidly produce new realistic scenes for outdoor natural environments, which relies on the mechanism of learning correlationship between vegetation distribution and terrain data. [ABSTRACT FROM AUTHOR]

Abstract : Visualizations of machine learning models have developed rapidly during these days, attracting great interests of industry and researchers. However, a pipeline that visualizations are created from logged data is a time-consuming process. In this work, we adopt progressive visual analytics to propose a new pipeline to facilitate the visual analysis progress of gradient boosting regression tree (GBRT). Visualizations such as tree view, instances view, and cluster view are created according to different types of data in real time. Users can explore GBRT with different visualization components interactively through GBRTVis. Case studies demonstrate that our pipeline can improve the efficiency of the training process and understanding. Furthermore, we propose a mixed structure of GBRT to improve itself. Two tests on different datasets show the effectiveness of the improvement.Graphical Abstract: [ABSTRACT FROM AUTHOR]

Abstract: It is a feasible way to investigate the mental health status of college students through University Personality Inventory (UPI). However, the analysis of original UPI data is a difficult and tedious task, because the traditional software always requires the users to select and reload slices of data by hand. Also, the limited constant functions provided by the software cannot meet the various requirements of domain experts. In this paper, we propose VisUPI, a specialized visualization framework for UPI datasets, aiming at an in-depth analysis of the mental health status of college students. A circular view is firstly designed to layout the questionnaires and visualize the answers of individuals. Then, a decision tree model is employed to classify the investigated students, and a radial hierarchy chart is designed to present the relationship of different groups of students. According to the prior knowledge of UPI, we restructure the network relationship of specified questionnaires, and use the force-directed model to layout the questionnaires, enabling users to better perceive the answers of those students with serious mental health problems. Furthermore, multi-dimensional scaling is used to visualize the dissimilarity between different questionnaires, and the statistical answers are presented through bar charts in detail. Finally, the effectiveness and scalability of VisUPI are demonstrated through case studies with the real-world datasets and the domain-expert interviews.Graphical Abstract: [ABSTRACT FROM AUTHOR]

Conventional methods to create fluid animation primarily resort to physically based simulation via numerical integration, whose performance is dominantly hindered by large amount of numerical calculation and low efficiency. Alternatively, video-based methods could easily reconstruct fluid surfaces from videos, yet they are not able to realize two-way dynamic interaction with their surrounding environment in a physically correct manner. In this paper, we propose a hybrid method that combines video-based fluid surface reconstruction and popular fluid animation models to compute and re-animate fluid surface. First, the fluid surface's height field corresponding to each video frame is estimated by using the shape-from-shading method. After denoising, hole-filling, and smoothing operations, the height field is utilized to calculate the velocity field, where the shallow water model is adopted. Then we treat the height field and velocity field as real data to drive the simulation. Still, only one layer of surface particles is not capable of driving the smoothed particle hydrodynamics (SPH) system. The surface particles (including 3D position and its velocity) are then employed to guide the spatial sampling of the entire volume underneath. Second, the volume particles corresponding to each video frame are imported into the SPH system to couple with other possible types of particles (used to define interacting objects), whose movement is dictated by the direct forcing method, and fluid particles' geometry information is then corrected by both physical models and real video data. The resulting animation approximates the reconstruction surface from the input video, and new physically based coupling behaviors are also appended. We document our system's detailed implementation and showcase visual performance across a wide range of scenes. [ABSTRACT FROM AUTHOR]

Seasonal variations in the biochemical composition and the reproductive cycle of pen shell Atrina pectinata in Bohai Sea were investigated from May 2013 to April 2014. Histological analysis indicated that the reproductive cycle of A. pectinata can be divided into two phases, inactive stage and gametogenesis, which were equally and significantly influenced by seawater temperature and food availability. Gametogenesis began in late autumn (October), and completed in June and July. Spawning took place in August, coinciding with the highest water temperature and the richest phytoplankton. The significantly high glycogen content in adductor muscles sustained throughout the late active and ripeness stages, but plummeted during spawning and inactive stages. The protein content in female gonads exhibited a synchronous increase along with oocyte diameter and lipid content, suggesting that the female gonads could accumulate protein and lipid for vitelline in A. pectinata. Furthermore, the RNA/DNA ratio was found to be a useful index to indicate the level of gonad maturation in both males and females. The findings of the present study provided a foundation for the fishery resource administration and the aquaculture development of this species. [ABSTRACT FROM AUTHOR]

Customizing a desired naturalistic fluid simulation result from video to obtain similar artistic effect is significant in practice. But art-directed customizing is challengeable due to the chaotic nature of the physics contained in it, and this still remains to be a difficult task in spite of rapid advancements of computer graphics during the last two decades. This paper focuses on the problem of physically based fluid re-simulation which is foundational to customize desired naturalistic simulation result from video example. In the previous achievements, conventional algorithms primarily recover 3D geometry of fluid surface or obtain the velocity of fluid particles in video. However, to launch new derivative results, just geometry and velocity are not enough, and physically based driven models are promising. We present a novel method that is capable of efficiently recovering physically driven model from an existing video. We advocate a new approach to calculate the velocity and non-normalized surface geometry under the constraints of the appearance and dynamic behavior of the example fluid in multi-scale framework, and use the calculated physical properties quickly to recover the fluid-driven model. In particular, to calculate the surface geometry more accurately, we introduce a scale factor between normalized geometry and non-normalized geometry to acquire a more accurate result. We propose a novel recovery algorithm, in which the particle densities of lattice Boltzmann method can be recovered more accurately from matching fluid advection geometry with the calculated non-normalized geometry. Fluid re-simulations with different types of density can be achieved, including constrained particle density, auto-advection density, and enhanced particle density. We demonstrate our results in several challenging scenarios and provide qualitative evaluation to our method. Some applications based on our approach are also demonstrated in the implementation. [ABSTRACT FROM AUTHOR]

Micro-blog, a new social networking service is being widely used by the public to share ideas, disseminate information, and communicate with each other. Due to the large volume of information involved, it is a challenge to understand the retweet and diffusion process and find different pattern or characteristic on micro-blogs. How do we track the process of micro-blog diffusion and analyze its characteristic from different view? How to track micro-changes (structure pattern) and detect abnormal user behaviors (detail tracking)? Aiming to solve above questions, this paper presents an information diffusion function (IDF) model for analyzing the mechanism of micro-blog retweet and combine visualization to show the dynamic process of information diffusion and track user behaviors. We use the IDF to quantify the process of micro-blog diffusion which is defined by three influential factors: micro-blogs information quantity, micro-blog sentiment and user influence. Then we combine IDF with visualization to analyze the process of micro-blog diffusion and detect abnormal users. Experiments on several real topics have shown that visualization based on our model is useful in detecting interesting micro-blog characteristics and trends. Graphical Abstract: [ABSTRACT FROM AUTHOR]

In this paper, we articulate a novel particle-centric method to simulate the dynamics of brittle fracture for anisotropic materials. The key motivation of this paper is to develop a new hybrid, particle-based simulation that inherits advantages from both powerful finite element methods and popular mesh-free methods, while overcoming certain disadvantages of both types of methods. Our method stems from two novel aspects: (1) a physical model built upon an improved mechanical framework and the adaptive smoothed particle hydrodynamics (SPH), an improved variant of traditional SPH, which can handle complicated anisotropic elastic behaviors with little extra cost; and (2) a hybrid, adaptive particle system that serves for more accurate fracture modeling with richer details. At the physical level, in order to facilitate better control during the formation of fracture and improve its time performance, we develop a physical framework based on contact mechanics and adopt the stress and energy analysis on the anisotropic SPH numerical integration to pinpoint fracture generation and propagation. At the geometric level, in order to reduce time consumption and enhance accuracy in rigid dynamics and fracture generation, we employ hybrid, fully adaptive particles in the vicinity of fracture regions via geometric subdivision. Our novel approach can facilitate the user to control the generation of cracks with low computational cost and retain high-fidelity crack details during animation. Our comprehensive experiments demonstrate the controllability, effectiveness, and accuracy of our method when simulating various brittle fracture patterns for anisotropic materials. [ABSTRACT FROM AUTHOR]

Purpose: Small hydropower (SHP) in China has experienced soring development in the past two decades and has been assigned ambitious development goals recently, while its environmental performance remains unclear. This study is intended to provide a comprehensive assessment of the environmental impacts of SHP plants in China, to compare the results with its counterparts in other countries, and to identify the key factors in the mitigation of negative consequences. Methods: A life cycle assessment of a SHP plant in Guizhou Province of China was conducted in a cradle-to-grave manner following the ISO 14040 guidelines. The functional unit is defined as 1 MWh of net electricity produced by the plant. The CML 2001 method was applied to characterize the environmental impacts. The environmental impact categories considered in this study included global warming (GWP), abiotic depletion (ADP), acidification (AP), freshwater aquatic ecotoxicity (FAETP), human toxicity (HTP), and photochemical ozone creation (POCP). Further contribution analyses and sensitivity analysis was performed to identify the key contributors to each impact category during the life cycle of the plant. Results and discussion: For the case plant, the considered impacts are caused primarily by the construction stage. As for the materials and energy inputs, cement, steel, and electricity are the three dominating ones for the overall environmental impacts. Compared with SHP plants in other countries, the plant performs similar to the MW scale plants in Thailand and Japan but worse than the plant in Switzerland. Further comparison of life cycle inventories (LCIs) revealed that the quality of hydro-energy resources and acquisition of indigenous equipment technology is essential to their environmental performance. The results of the sensitivity analysis suggested that the amount of construction materials and energy consumption as well as the plant output influences its environmental performance significantly. Conclusions and recommendations: The construction stage of the SHP plant is the most important source of environmental impacts. To minimize the impacts of this stage, optimization of the structural design and application of new construction materials and good construction practices is recommended. In addition, determining suitable installed capacity and advancing equipment technologies to ensure the optimal output is also crucial to improve the environmental performance of SHP plants in China, regarding the current serious problem of unstable operation. [ABSTRACT FROM AUTHOR]

Various methods on the display of dynamic information diffusion for social media have been proposed. Most of them use data mining approaches to explore the behaviors and interactions between users. Such approaches are unable to reveal the complex mechanism and the process of information diffusion. Lattice Boltzmann Method (LBM) models fluid behaviors at the microscopic scale, similar to the information diffusion in social media that is determined by the collective behavior of many personal retweeting of topics. We propose an information diffusion model inspired by the fundamental idea of LBM to analyze and simulate users' communicating behaviors and processes in Micro-blogging. The micro-blog space is regarded as an artificial physical system with social phenomena such as micro-blog bursting. The macroscopic properties of the information diffusion model are explored to simulate and predict the trend of information diffusion for any specific topic. A novel visualization style mimicking fluid dynamics is proposed to help understand the scale of information diffusion and the popularity of a topic. The flow visualization based on the speed of information diffusion is useful in discovering typical information diffusion patterns for different types of topics in social networks. Comparing with other approaches, our approach provides more effective yet intuitive simulation. Graphical abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Assessing the extent to which all bio-fuels that are claimed to be renewable are in fact renewable is essential because producing such renewable fuels itself requires some amount of non-renewable energy (NE) and materials. Using hybrid life cycle analysis (LCA)-from raw material collection to delivery of pellets to end users-the energy cost of wood pellet production in China was estimated at 1.35 J/J, of which only 0.09 J was derived from NE, indicating that only 0.09 J of NE is required to deliver 1 J of renewable energy into society and showing that the process is truly renewable. Most of the NE was consumed during the conversion process (46.21%) and delivery of pellets to end users (40.69%), during which electricity and diesel are the two major forms of NE used, respectively. Sensitivity analysis showed that the distance over which the pellets are transported affects the cost of NE significantly. Therefore the location of the terminal market and the site where wood resources are available are crucial to saving diesel. [ABSTRACT FROM AUTHOR]

Physically based fluid simulation in recent years has been successful for small-scale fluids such as liquid in a cubic cavity. However, for boundless free-surface flow of large scale and irregular area, there is a critical trade-off between simulation efficiency and accuracy because of the restriction of the traditional regular computational grids. This paper introduces boundless computational grids based on hierarchical runlength encoding to simulate large-scale free-surface flow. We first modeled the free-surface flow with a lattice Boltzmann method, and calculated the surface curvature in the update process. We then introduced an effective method with which to calculate the surface curvature according to the surface tension so that the surface detail was enhanced, and obtained the curvature of each surface effectively as it was extracted. Furthermore, we dynamically compressed and indexed the computational cells with the improved hierarchical run-length encoding algorithm, so that the grid expands dynamically according to the fluid flow and the computational resources used were proportional to the volume of the fluid. Finally fluids in different situations were simulated realistically. The proposed method makes the best of the computational resources to perform the simulation with high resolution, and dynamically allocates resources so that the fluid can expand in random directions without boundaries, which is suited to the simulation of large-scale visual scenes. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to generate the dynamic infrared outer space scene in real time. In the paper, we first model the dynamic infrared background of star sky based on the infrared star catalogue and temporal and spacial coordinate transformations. We also present a new concept called infrared star magnitude for given infrared receiving wavebands to display the infrared star sky effectively. Then we put forward an infrared image synthesis model for outer space targets based on the method of divided elements and energy equilibrium. To generate the dynamic infrared outer space scene, several simplification and accelerating techniques such as Billboard, Levels of Details and Occlusion Culling are hired to accelerate the generating speed of the scene. Finally infrared images of targets, background of outer space scene at different infrared receiving wavebands are drawn and the dynamic infrared outer space scene is successfully carried out in real time. [ABSTRACT FROM AUTHOR]