Visualization of Tomato Growth Based on Dry Matter Flow

The visualization of tomato growth can be used in 3D computer games and virtual gardens. Based on the growth theory involving the respiration theory, the photosynthesis, and dry matter partition, a visual system is developed. The tomato growth visual simulation system is light-and-temperature-dependent and shows plausible visual effects in consideration of the continuous growth, texture map, gravity influence, and collision detection. In addition, the virtual tomato plant information, such as the plant height, leaf area index, fruit weight, and dry matter, can be updated and output in real time

Tuning the Magnetism in Ultrathin CrxTey Films by Lattice Dimensionality

Two-dimensional (2D) magnetic transition metal compounds with atomic thickness exhibit intriguing physics in fundamental research and great potential for device applications. Understanding the correlations between their macrosopic magnetic properties and the dimensionality of microscopic magnetic exchange interactions are valuable for the designing and applications of 2D magnetic crystals. Here, using spin-polarized scanning tunneling microscopy, magnetization and magneto-transport measurements, we identify the zigzag-antiferromagnetism in monolayer CrTe2, incipient ferromagnetism in bilayer CrTe2, and robust ferromagnetism in bilayer Cr3Te4 films. Our density functional theory calculations unravel that the magnetic ordering in ultrathin CrTe2 is sensitive to the lattice parameters, while robust ferromagnetism with large perpendicular magnetic anisotropy in Cr3Te4 is stabilized through its anisotropic 3D magnetic exchange interactions

Efficient photocatalytic hydrogen evolution over carbon supported antiperovskite cobalt zinc nitride

Photocatalytic solar to chemical energy conversion is an important energy conversion process but suffer from low efficiency. Thus, development of efficient photocatalytic system using earth-abundant elements with low costs is highly desirable. Here, antiperovskite cobalt zinc nitride has been synthesized and coupled with carbon black (Co3ZnN/C) for visible light driven hydrogen production in an Eosin Y-sensitized system. Replacement of cobalt atom by zinc atom leads to an improved charge transfer kinetics and catalytic properties compared with Co4N. Density functional theory (DFT) calculations further reveal the adjusted electronic structure of Co3ZnN by zinc atom introducing. The lower antibonding energy states of Co3ZnN are beneficial for the hydrogen desorption. Moreover, carbon black as support effectively reduces the particle size of Co3ZnN and benefits to the electron storage and adsorption capabilities. The optimal Co3ZnN/C catalysts exhibit the H-2 evolution rate of 15.4 mu mol mg(-1) h(-1),which is over 6 times higher than that of monometallic Co4N. It is even greater than those of most of Eosin Y-sensitized systems

Near infrared spectroscopy coupled with radial basis function neural network for at-line monitoring of Lactococcus lactis subsp. fermentation

AbstractIn our previous work, partial least squares (PLSs) were employed to develop the near infrared spectroscopy (NIRs) models for at-line (fast off-line) monitoring key parameters of Lactococcus lactis subsp. fermentation. In this study, radial basis function neural network (RBFNN) as a non-linear modeling method was investigated to develop NIRs models instead of PLS. A method named moving window radial basis function neural network (MWRBFNN) was applied to select the characteristic wavelength variables by using the degree approximation (Da) as criterion. Next, the RBFNN models with selected wavelength variables were optimized by selecting a suitable constant spread. Finally, the effective spectra pretreatment methods were selected by comparing the robustness of the optimum RBFNN models developed with pretreated spectra. The results demonstrated that the robustness of the optimal RBFNN models were better than the PLS models for at-line monitoring of glucose and pH of L. lactis subsp. fermentation

Bridging Nano and Micro-scale X-ray Tomography for Battery Research by Leveraging Artificial Intelligence

X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials' absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models.Comment: 33 pages, 5 figure

sketch-based design for green geometry and image deformation

User interfaces have traditionally followed the WIMP (window, icon, menu, pointer) paradigm. Though functional and powerful, they are usually cumbersome for a novice user to design a complex model, requiring considerable expertise and effort. This paper presents a system for designing geometric models and image deformation with sketching curves, with the use of Green coordinates. In 3D modeling, the user first creates a 3D model by using a sketching interface, where a given 2D curve is interpreted as the projection of the 3D curve. The user can add, remove, and deform these control curves easily, as if working with a 2D line drawing. For a given set of curves, the system automatically identifies the topology and face embedding by applying graph rotation system. Green coordinates are then used to deform the generated models with detail-preserving property. Also, we have developed a sketch-based image-editing interface to deform image regions using Green coordinates. Hardware-assisted schemes are provided for both control shape deformation and the subsequent surface optimization, the experimental results demonstrate that 3D/2D deformations can be achieved in realtime. © 2011 Springer Science+Business Media, LLC

Sketch-based design for green geometry and image deformation

User interfaces have traditionally followed the WIMP (window, icon, menu, pointer) paradigm. Though functional and powerful, they are usually cumbersome for a novice user to design a complex model, requiring considerable expertise and effort. This paper presents a system for designing geometric models and image deformation with sketching curves, with the use of Green coordinates. In 3D modeling, the user first creates a 3D model by using a sketching interface, where a given 2D curve is interpreted as the projection of the 3D curve. The user can add, remove, and deform these control curves easily, as if working with a 2D line drawing. For a given set of curves, the system automatically identifies the topology and face embedding by applying graph rotation system. Green coordinates are then used to deform the generated models with detail-preserving property. Also, we have developed a sketch-based image-editing interface to deform image regions using Green coordinates. Hardware-assisted schemes are provided for both control shape deformation and the subsequent surface optimization, the experimental results demonstrate that 3D/2D deformations can be achieved in realtime.User interfaces have traditionally followed the WIMP (window, icon, menu, pointer) paradigm. Though functional and powerful, they are usually cumbersome for a novice user to design a complex model, requiring considerable expertise and effort. This paper presents a system for designing geometric models and image deformation with sketching curves, with the use of Green coordinates. In 3D modeling, the user first creates a 3D model by using a sketching interface, where a given 2D curve is interpreted as the projection of the 3D curve. The user can add, remove, and deform these control curves easily, as if working with a 2D line drawing. For a given set of curves, the system automatically identifies the topology and face embedding by applying graph rotation system. Green coordinates are then used to deform the generated models with detail-preserving property. Also, we have developed a sketch-based image-editing interface to deform image regions using Green coordinates. Hardware-assisted schemes are provided for both control shape deformation and the subsequent surface optimization, the experimental results demonstrate that 3D/2D deformations can be achieved in realtime