大腸炎と結腸癌におけるDCIRとClec12Aの機能的役割

東京理科大学202

The Application of Fluoride in Dental Caries

The most efficient way to prevent caries is by using fluoridated dental products. Fluoride can reduce enamel demineralization and promote enamel remineralization. In terms of prevention, the topical application of fluoride is accessible, which includes fluoride toothpaste, fluoride varnish, fluoride gel, and mouth rinse. Besides, the application of fluoride is systematical. In some countries, fluoride is added into water, salt, or milk. Fluoride is also used for the medical treatment of early dental caries. However, fluoride is a double-edged sword. Excessive fluoride intake will cause toxic reactions, and dental fluorosis is caused by a high intake of fluorides during tooth development

A Temporal-Rule Based Verification System for Business Collaboration Reliability

Based on the temporal rules defined for the business processe participating in a business collaboration, we present an implementation for a system called TiCoBTS to verify the reliability of the business collaboration

Nanoporous Oxides and Nanoporous Composites

Nanoporous oxides, such as cupric oxide (CuO), nickelous oxide (NiO), titanium dioxide (TiO2), cobaltosic oxide (Co3O4), and cerium oxide (CeO2), and noble-metal-based nanoporous composites, such as silver (Ag) ligaments loaded with CeO2, TiO2, zirconium dioxide (ZrO2) or NiO and palladium (Pd) ligaments loaded with TiO2 or ZrO2, are described in the chapter. Oxide-based nanoporous composites, such as Au loaded on CuO and CeO2 or platinum (Pt) loaded on TiO2, are also summarized. The structures, microstructures, and microstructure parameters of these materials are reviewed. The performance of the noble-based nanoporous composites is presented, including the catalytic oxidation of methanol and ethanol. Environmental protection applications, such as catalytic oxidation of carbon monoxide (CO) for the oxide-based nanoporous composites, have also been developed. Applications of rare earth elements in nanoporous materials are also reviewed

Equal Incremental Cost-Based Optimization Method to Enhance Efficiency for IPOP-Type Converters

Systematic optimization over a wide power range is often achieved through the combination of modules of different power levels. This paper addresses the issue of enhancing the efficiency of a multiple module system connected in parallel during operation and proposes an algorithm based on equal incremental cost for dynamic load allocation. Initially, a polynomial fitting technique is employed to fit efficiency test points for individual modules. Subsequently, the equal incremental cost-based optimization is utilized to formulate an efficiency optimization and allocation scheme for the multi-module system. A simulated annealing algorithm is applied to determine the optimal power output strategy for each module at given total power flow requirement. Finally, a dual active bridge (DAB) experimental prototype with two input-parallel-output-parallel (IPOP) configurations is constructed to validate the effectiveness of the proposed strategy. Experimental results demonstrate that under the 800W operating condition, the approach in this paper achieves an efficiency improvement of over 0.74\% by comparison with equal power sharing between both modules

Rapid flocculation-sedimentation of microalgae with organosilane-functionalized halloysite

Microalgae is a promising feedstock of biofuel for alternating fossil fuels. The major challenge of microalgal biofuels for commercial applications is in designing an efficient harvesting method with high economic feasibility. In this study, a rapid flocculation-sedimentation harvesting method induced by organosilane-functionalized halloysite flocculant was achieved for Scenedesmus dimorphus harvest. The harvesting efficiency was significantly influenced by the pH of microalgal dispersion and the dosage of flocculant. The optimized harvesting condition was pH 3.0 with flocculant dosage of 1.0 g.g(-1) cell dry mass. Under the optimized harvesting condition, microalgae rapidly reached 93% harvesting efficiency within 0.5 min of settling time, and reached 98% harvesting efficiency within 2 min of settling time. The rapid flocculation was attributed to the charge neutralization of the negatively-charged microalgae cells by the positively-charged organosilane-functionalized halloysite flocculant and to the sweep flocculation by organosilane-functionalized halloysite flocculant. The organosilane-functionalized halloysite flocculant did not affect the lipid extraction of microalgae, and not contaminate the extracted residuals. The organosilane-functionalized halloysite flocculant is of high efficient, cost-effective, and eco-friendly, makes it be of promising application for commercial microalgae harvesting.</p

Dynamic characteristics and optimal design of the manipulator for automatic tool changer

In order to improve the reliability of changing tool for ATC (automatic tool changer), a horizontal tool changer of machining center is chosen as the example to study the dynamic characteristics in the condition of changing a heavy tool. This paper analyzes the structure and properties of the tool changer by simulation and experiment, and the space trajectory equations of the manipulator and tool are derived. The maximum force is calculated in the processing of changing tool. A virtual platform for the automatic tool changer is built to simulate and verify the dynamic performance of the tool changer; the simulation results show an obvious vibration in the process of changing tool, which increases the probability of failure for changing tool. Moreover, in order to find out the device's vibration reasons, a professional experiment platform is built to test the dynamic characteristics. Based on the testing results for a horizontal tool changer, it is known that the unstable vibration is mainly caused by the collision of the tool. Finally, an optimization method for the manipulator is proposed to reduce this vibration and improve the reliability of the tool changer. The final simulation and experiment results show that the optimized manipulator can grasp the heavy tool stably, and the vibration amplitude is significantly reduced in the process of changing tool

Improving law enforcement daily deployment through machine learning-informed optimization under uncertainty

National Research Foundation (NRF) Singapore under Corp Lab @ University scheme; Fujitsu Lt

SVFAP: Self-supervised Video Facial Affect Perceiver

Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction. Previous studies mainly focus on developing various deep learning architectures and training them in a fully supervised manner. Although significant progress has been achieved by these supervised methods, the longstanding lack of large-scale high-quality labeled data severely hinders their further improvements. Motivated by the recent success of self-supervised learning in computer vision, this paper introduces a self-supervised approach, termed Self-supervised Video Facial Affect Perceiver (SVFAP), to address the dilemma faced by supervised methods. Specifically, SVFAP leverages masked facial video autoencoding to perform self-supervised pre-training on massive unlabeled facial videos. Considering that large spatiotemporal redundancy exists in facial videos, we propose a novel temporal pyramid and spatial bottleneck Transformer as the encoder of SVFAP, which not only enjoys low computational cost but also achieves excellent performance. To verify the effectiveness of our method, we conduct experiments on nine datasets spanning three downstream tasks, including dynamic facial expression recognition, dimensional emotion recognition, and personality recognition. Comprehensive results demonstrate that SVFAP can learn powerful affect-related representations via large-scale self-supervised pre-training and it significantly outperforms previous state-of-the-art methods on all datasets. Codes will be available at https://github.com/sunlicai/SVFAP.Comment: Submitted to IEEE Trans. on Affective Computing (February 8, 2023

Automated Large-Scale Multi-Language Dynamic Program Analysis in the Wild (Artifact)

This artifact provides a preliminary release of NAB, a distributed infrastructure for executing large-scale dynamic program analyses (DPAs). The artifact consists of ready-to-use Docker containers that allow one to run different DPA tools (Deep-Promise, JITProf, and tgp) on Node.js, Java, and Scala projects hosted on GitHub. The artifact enables the reproduction of the figures and tables of the related paper "Automated Large-scale Multi-language Dynamic Program Analysis in the Wild" with pre-collected data (several GBs) and the execution of DPAs on specific sets of GitHub projects