The Human Resource Management of Japanese Company in China - Be Compared with Chinese Company -

修士論文要

Quadruple bonding between iron and boron in the BFe(CO)3- complex.

While main group elements have four valence orbitals accessible for bonding, quadruple bonding to main group elements is extremely rare. Here we report that main group element boron is able to form quadruple bonding interactions with iron in the BFe(CO)3- anion complex, which has been revealed by quantum chemical investigation and identified by mass-selected infrared photodissociation spectroscopy in the gas phase. The complex is characterized to have a B-Fe(CO)3- structure of C3v symmetry and features a B-Fe bond distance that is much shorter than that expected for a triple bond. Various chemical bonding analyses indicate that the complex involves unprecedented B≣Fe quadruple bonding interactions. Besides the common one electron-sharing σ bond and two Fe→B dative π bonds, there is an additional weak B→Fe dative σ bonding interaction. This finding of the new quadruple bonding indicates that there might exist a wide range of boron-metal complexes that contain such high multiplicity of chemical bonds

Preparation and Application of Green Sustainable Solvent Cyrene

The bio-based solvent dihydrolevoglucosenone (Cyrene) is a green and sustainable alternative to petroleum-based dipolar aprotic solvents. Cyrene can be prepared from cellulose in a simple two-step process and can be produced in a variety of yields. Cyrene is compatible with a large number of reactions in the chemical industry and can be applied in organic chemistry, biocatalysis, materials chemistry, graphene and lignin processing, etc. It is also green, non-mutagenic and non-toxic, which makes it very promising for applications. In this paper, we have also screened all articles related to Cyrene on the Web of Science and visualised them through Cite Space

Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things

The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission

Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things

The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission

Measurement and simulation of carbon nanotube’s piezoresistance property by a micro/nano combined structure

282-286In this paper, the present status of carbon nanotube’s electromechanical properties was reviewed. The relationships among the carbon nanotube’s resistance, gauge factor and the rates of change of the band gaps with strain (dEℊ/dƐ) were analyzed and simulated. Then, a micro/nano combined device and method for measuring the piezoresistance property of carbon nanotube were proposed. The device is consisted of a silicon chip and a printed circuit board which is used for loading and leading wire. The microelectrodes were fabricated on the silicon chip by FIB and a CVD-growth single-wall carbon nanotube was connected with the microelectrodes. The voltage-current characteristic of the carbon nanotube was measured using the proposed device. The relationship between the current and the voltage is basically linear, which demonstrates that the carbon nanotube is metallic. The experimental results show that the micro/nano combined device can be used for measuring the piezoresistance of carbon nanotube in our future work

Type identification and engineering geology zoning of the unstable slope in Tongkuangling

The subgrade, tunnel and bridge engineering of highways or railways may be affected by some geological disasters, especially by unknown disaster types, which directly affects the overall treatment decision.This article takes the Tongkuangling unstable slope on the Yiba Expressway as an example andidentifies the types of the Tongkuangling unstable slope by using field geological surveys, engineering geological analysis and monitoring data analysis. The field investigation shows that this unstable slope is made up of the eluvial deluvial deposit of the upper part and siltstone with an inverted layer of the lower part. Combined with the displacement monitoring data, it is determined that there are multiple shear slip surfaces in the loose deposits. The unstable slope is comprehensively determined to be a deep creep deformation body. Last, according to the characteristics of deformation, the Tongkuangling deformation body is finally divided into two zones, of which zone Ⅱ can be subdivided into two small zones. The Ⅱ2 zone is the key control part. This provides the geological basis for the subsequent treatment design of the deep deformation body.It can be seen that the combination of traditional engineering geological surveys and monitoring data analysis is an effective means to identify the type of geological disasters and determine the boundary range

A Unified Framework for Depth Prediction from a Single Image and Binocular Stereo Matching

Depth information has long been an important issue in computer vision. The methods for this can be categorized into (1) depth prediction from a single image and (2) binocular stereo matching. However, these two methods are generally regarded as separate tasks, which are accomplished in different network architectures when using deep learning-based methods. This study argues that these two tasks can be achieved using only one network with the same weights. We modify existing networks for stereo matching to perform the two tasks. We first enable the network capable of accepting both a single image and an image pair by duplicating the left image when the right image is absent. Then, we introduce a training procedure that alternatively selects training samples of depth prediction from a single image and binocular stereo matching. In this manner, the trained network can perform both tasks and single-image depth prediction even benefits from stereo matching to achieve better performance. Experimental results on KITTI raw dataset show that our model achieves state-of-the-art performances for accomplishing depth prediction from a single image and binocular stereo matching in the same architecture