Microstructure and mechanical properties of aluminum-steel dissimilar metal welded using arc and friction stir hybrid welding

In this study, arc and friction stir hybrid welding (AFSHW) was proposed to weld aluminum-steel dissimilar metals in attempt to realize high quality joining. Firstly, an interlayer was produced on galvanized steel by using bypass current-metal inert gas welding (BC-MIG), and then an aluminium plate was jointed via Friction stir lap welding (FSLW). The effects of tool pin length and FSLW times on the microstructure and mechanical properties of dissimilar joints were fully investigated by means of Optical Microscopy (OM), Scanning Electron Microscope (SEM), Electron Backscatter Diffraction (EBSD), and mechanical testing. The results show that as pin length increased, joint strength tended to increase and then decrease, and the tensile failure partially occurred at aluminium base metal. However, with additional number of FSLW, joint strength would be reduced, which was attributed to attenuated dislocation density and strain concertation in dissimilar joint. The research outcomes will provide a new welding method to obtain sound Al-Fe dissimilar metal joint, and benefit to a better understanding of Al-Fe joining mechanism

Preparation and Characterization of Silkworm Pupa SourcePeptide-zinc Nanoparticles

Silkworm pupa peptide (SCP) was prepared by enzymatic hydrolysis and then chelated with soluble zinc ions to obtain silkworm pupa peptide-zinc chelates (SCP-Zn), so as to develop safe and easily absorbable zinc supplements and improve the utilization value of silkworm pupa. Taking the zinc chelating capacity as an index, the optimum preparation process of SCP-Zn was determined, and the structure of both SCP and SCP-Zn were characterized by ultraviolet spectrum, fluorescence spectra, scanning electron microscopy, elemental analysis, particle size analysis and Fourier transform infrared spectrum. The results showed that the chelation rate of silkworm chrysalis peptide was 58.05% under the conditions of 1% alkaline protease plus enzyme, pH8.0, temperature 50 ℃ and enzymatic hydrolysis time 6 h. The optimum preparation conditions for preparation of SCP-Zn nanoparticles were as follows: Mass ratio of zinc peptide 1:0.5, pH6.5, 55 ℃, time 20 min, and the chelation rate of zinc reached 72.63%. The results of ultraviolet spectrum and fluorescence spectrum showed that zinc ions successfully combined with SCP. The obtained chrysalis SCP-Zn belongs to nanoparticles with an average particle size of 71.99 nm, with uniform granular structure on the surface, and the relative content of zinc reached 37.46%. The -COOH, -NH2 and -C=O in the peptide chain were the main binding sites of Zn2+ and SCP. The results indicated that silkworm pupa was a good raw material for preparation of zinc chelates. The study provides a theoretical basis for enriching organic zinc supplement resources and the high value utilization of silkworm pupa

Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework

In recent years, rice seedling raising factories have gradually been promoted in China. The seedlings bred in the factory need to be selected manually and then transplanted to the field. Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was applied to estimate the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed model outperformed compared random forest (RF) and regression CNN models (RCNN). The model achieved R2 values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%, respectively. The hybrid CNNs method can learn the relationship between digital images and seedling growth traits, promising to provide a convenient and flexible estimation tool for the non-destructive monitoring of seedling growth in controlled environments

The Impact of the US - China Trade War on International Manufacturing and Supply Chains

This thesis aims to analyze how the trade war between China and the USA influences international manufacturers and supply chains. This thesis mainly focuses on analyst the impact on Vietnam. The first chapter introduces the trade history and the reason for the caused trade war between China and the USA. The Second chapter is to illustrate the character of manufacturing and supply chains. The third chapter analyst influences productivity, Innovation, export and import, exchange rate, and FDI of China and the USA from the trade war. The last chapter analyst the trade war consequence on southeast countries.This thesis aims to analyze how the trade war between China and the USA influences international manufacturers and supply chains. This thesis mainly focuses on analyst the impact on Vietnam. The first chapter introduces the trade history and the reason for the caused trade war between China and the USA. The Second chapter is to illustrate the character of manufacturing and supply chains. The third chapter analyst influences productivity, Innovation, export and import, exchange rate, and FDI of China and the USA from the trade war. The last chapter analyst the trade war consequence on southeast countries

Surface texture measurement on complex geometry using dual-scan positioning strategy

In this paper, a surface measurement method based on dual-scan positioning strategy is presented to address the challenges of irregular surface patterns and complex geometries. A confocal sensor with an internal scanning mechanism was used in this study. By synchronizing the local scan, enabled by the internal actuator in the confocal sensor, and the global scans, enabled by external positioners, the developed system was able to perform noncontact line scan and area scan. Thus, this system was able to measure both surface roughness and surface uniformity. Unlike laboratory surface measurement equipment, the proposed system is reconfigurable for in situ measurement and able to scan free-form surfaces with a proper stand-off distance and approaching angle. For long-travel line scan, which is needed for rough surfaces, a surface form tracing algorithm was developed to ensure that the data were always captured within the sensing range of the confocal sensor. It was experimentally verified that in a scanning length of 100 mm, where the surface fluctuation in vertical direction is around 10 mm, the system was able to perform accurate surface measurement. For area scan, XY coordinates provided by the lateral positioning system and the Z coordinate captured by the confocal sensor were plotted into one coordinate system for 3D reconstruction. A coherence scanning interferometer and a confocal microscope were employed as the reference measurement systems to verify the performance of the proposed system in a scanning area of 1 mm by 1 mm. Experimental data showed that the proposed system was able to achieve comparable accuracy with laboratory systems. The measurement deviation was within 0.1 µm. Because line scan mechanisms are widely used in sensor design, the presented work can be generalized to expand the applications of line scan sensors.Published versio

Event-triggered fault detection of nonlinear networked systems

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is propose d to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results

Enzymology of standalone elongating ketosynthases.

The β-ketoacyl-acyl carrier protein synthase, or ketosynthase (KS), catalyses carbon-carbon bond formation in fatty acid and polyketide biosynthesis via a decarboxylative Claisen-like condensation. In prokaryotes, standalone elongating KSs interact with the acyl carrier protein (ACP) which shuttles substrates to each partner enzyme in the elongation cycle for catalysis. Despite ongoing research for more than 50 years since KS was first identified in E. coli, the complex mechanism of KSs continues to be unravelled, including recent understanding of gating motifs, KS-ACP interactions, substrate recognition and delivery, and roles in unsaturated fatty acid biosynthesis. In this review, we summarize the latest studies, primarily conducted through structural biology and molecular probe design, that shed light on the emerging enzymology of standalone elongating KSs