Beads-on-String Model for Virtual Rectum Surgery Simulation

A beads-on-string model is proposed to handle the deformation and collision of the rectum in virtual surgery simulation. The idea is firstly inspired by the observation of the similarity in shape shared by a rectum with regular bulges and a string of beads. It is beneficial to introduce an additional layer of beads, which provides an interface to map the deformation of centreline to the associated mesh in an elegant manner and a bounding volume approximation in collision handling. Our approach is carefully crafted to achieve high computational efficiency and retain its physical basis. It can be implemented for real time surgery simulation application

Novel Phases of Semi-Conducting Silicon Nitride Bilayer: A First-Principle Study

In this paper, we have predicted the stabilities of several two-dimensional phases of silicon nitride, which we name as \alpha-phase, \beta-phase, and \gamma-phase, respectively. Both \alpha- and \beta-phases has formula Si$_{2}$N$_{2}$, and are consisted of two similar layer of buckled SiN sheet. Similarly, \gamma-phase is consisted of two puckered SiN sheets. For these phases, the two layers are connected with Si-Si covalent bonds. Transformation between \alpha- and \beta-phases is difficult because of the high energy barrier. Phonon spectra of both \alpha- and \beta-phase suggest their thermodynamic stabilities, because no phonon mode with imaginary frequency is present. By Contrast, \gamma-phase is unstable because phonon modes with imaginary frequencies are found along \Gamma-Y path in the Brilliouin zone. Both \alpha- and \beta-phase are semiconductor with narrow fundamental indirect band gap of 1.7eV and 1.9eV, respectively. As expected, only s and p orbitals in the outermost shells contribute the band structures. The p$_{z}$ orbitals have greater contribution near the Fermi level. These materials can easily exfoliate to form 2D structures, and may have potential electronic applications.Comment: 9 pages, 6 figure

Heterologous expression and characterization of a malathion-hydrolyzing carboxylesterase from a thermophilic bacterium, Alicyclobacillus tengchongensis

A carboxylesterase gene from thermophilic bacterium, Alicyclobacillus tengchongensis, was cloned and expressed in Escherichia coli BL21 (DE3). The gene coded for a 513 amino acid protein with a calculated molecular mass of 57.82 kDa. The deduced amino acid sequence had structural features highly conserved among serine hydrolases, including Ser204, Glu325, and His415 as a catalytic triad, as well as type-B carboxylesterase serine active site (FGGDPENITIGGQSAG) and type-B carboxylesterase signature 2 (EDCLYLNIWTP). The purified enzyme exhibited optimum activity with β-naphthyl acetate at 60 °C and pH 7 as well as stability at 25 °C and pH 7. One unit of the enzyme hydrolyzed 5 mg malathion l(−1) by 50 % within 25 min and 89 % within 100 min. The enzyme strongly degraded malathion and has a potential use for the detoxification of malathion residues

R&D Subsidies and Economic Growth: The Case of the ICT Manufacturing Sectors in China

早稲田大学博士(学術)早大学位記番号:新9661doctoral thesi

A Guided Ant Colony Optimization Algorithm for Conflict-free Routing Scheduling of AGVs Considering Waiting Time

Efficient conflict-free routing scheduling of automated guided vehicles (AGVs) in automated logistic systems can improve delivery time, prevent delays, and decrease handling cost. Once potential conflicts present themselves on their road ahead, AGVs may wait for a while until the potential conflicts disappear besides altering their routes. Therefore, AGV conflict-free routing scheduling involves making routing and waiting time decisions simultaneously. This work constructs a conflict-free routing scheduling model for AGVs with consideration of waiting time. The process of the model is based on calculation of the travel time and conflict analysis at the links and nodes. A guided ant colony optimization (GACO) algorithm, in which ants are guided to avoid conflicts by adding a guidance factor to the state transition rule, is developed to solve the model. Simulations are conducted to validate the effectiveness of the model and the solution method

MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training

In this research, we introduce MaeFuse, a novel autoencoder model designed for infrared and visible image fusion (IVIF). The existing approaches for image fusion often rely on training combined with downstream tasks to obtain high-level visual information, which is effective in emphasizing target objects and delivering impressive results in visual quality and task-specific applications. MaeFuse, however, deviates from the norm. Instead of being driven by downstream tasks, our model utilizes a pretrained encoder from Masked Autoencoders (MAE), which facilities the omni features extraction for low-level reconstruction and high-level vision tasks, to obtain perception friendly features with a low cost. In order to eliminate the domain gap of different modal features and the block effect caused by the MAE encoder, we further develop a guided training strategy. This strategy is meticulously crafted to ensure that the fusion layer seamlessly adjusts to the feature space of the encoder, gradually enhancing the fusion effect. It facilitates the comprehensive integration of feature vectors from both infrared and visible modalities, preserving the rich details inherent in each. MaeFuse not only introduces a novel perspective in the realm of fusion techniques but also stands out with impressive performance across various public datasets