THE INTERFACIAL BEHAVIORS OF ALUMINUM MATRIX COMPOSITE IN DIFFERENT WELDING METHODS

Non-interlayer liquid phase diffusion welding (China Patent) and laser welding methods for aluminum matrix composite are mainly described in this paper. In the non-interlayer liquid phase diffusion welding, the key processing parameters affecting the strength of joint is welding temperature. When temperature rises beyond solidus temperature, the bonded line vanishes. The strength of joint reaches the maximum and becomes constant when welding temperature is close to liquid phase temperature. Oxide film in the interface is no longer detected by SEM in the welded joint. With this kind of technique, particle reinforced aluminum matrix composite Al2 O3p/6061Al is welded successfully, and the joint strength is about 80% of the strength of composite (as-casted). In the laser welding, results indicate that because of the huge specific surface area of the reinforcement, the interfacial reaction between the matrix and the reinforcement is restrained intensively at certain laser power and pulsed laser beam. The laser pulse frequency directly affects the reinforcement segregation and the reinforcement distribution in the weld, so that the weldability of the composite could be improved by increasing the laser pulse frequency. The maximum strength of the weld can reach 70% of the strength of the parent.

Applications of Chapman-Richards model to geotechnical engineering

In this paper,the Chapman-Richards model is adapted to best fit three types of nonlinear curves that are generally encountered in geotechnical engineering practices,i.e.the degree of consolidation versus time factor curves in one-dimensional consolidation and plane strain consolidation under strip loading,the compressibility and permeability curves of soft clay,and the geometry parameters of geosynthetic tube versus pumping pressure curves.The methods of determining unknown parameters using the Chapman-Richards model are fully demonstrated.It is found that the Chapman-Richards model has its range of applications in geotechnical engineering and may provide unique insights into the complexity of geotechnical problems.

乌龙茶的醇类香味形成机理

在我们对乌龙茶（水仙与毛蟹──是茶树的两种名品种）的香味形成机理作研究的过程中，我们分离并鉴定出了香叶醇、芳樟醇、2-苯乙醇、苄醇、芳樟醇氧化物Ⅰ与Ⅱ（反式-与顺式-芳樟醇3，6-氧化物）的主要醇类香味前驱体（1-7），并与β-樱草糖苷一样的水杨酸甲酯（6-O-β-D-吡喃木糖基-β-D-吡喃糖苷），以酶法水解（用Yabukita栽培种制得的丙酮粉末）得出，随后采用气相色谱与气相色谱-质谱联用分析法。芳樟醇氧化物Ⅳ（顺式-芳樟醇3，7-氧化物）与（Z）-3-己烯醇的香味前驱体（8与9）罕见地分别以6-O-β-D-芹菜呋喃糖基-β-D-吡喃葡糖与β-D-吡喃葡糖的形式被分离出。作为一个初始实验，我们亦认日本绿茶生产用的Yabukita栽培种的新鲜茶叶作提纯并对其特性描述为一种新的糖苷酶（β-樱草糖苷）。这一糖苷酶经证实为与茶叶中醇类香味形成有关系的主要糖苷酶之一。这是迄今为止提纯出的第二个β-樱草糖苷的标样。从新鲜乌龙茶叶（水仙栽培种）中作的糖苷酶的提纯亦完成了一项发现，即樱草糖苷的活性很近似于Yabukita栽培种。

On Boundary Factors and Traces of Subgroups of Finite Groups

A subgroup E of a finite group G is called hypercyclically embedded in G if every chief factor of G below E is cyclic.Let A be a subgroup of a group G.Then we call any chief factor H/AG of G a G-boundary factor of A.For any G-boundary factor H/AG of A,we call the subgroup(A∩H)/AG of G/AG a G-trace of A.On the basis of these notions,we give some new characterizations of hypercyclically embedded subgroups.

Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model:Impact of the Spatial Resolution

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices.The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput,accuracy,and access to plant localization.However,high-resolution images are required to detect the small plants present at the early stages.This study explores the impact of image ground sampling distance(GSD)on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm.Data collected at high resolution(GSD≈0:3 cm)over six contrasted sites were used for model training.Two additional sites with images acquired both at high and low(GSD≈0:6 cm)resolutions were used to evaluate the model performances.Results show that Faster-RCNN achieved very good plant detection and counting(rRMSE=0:08)performances when native high-resolution images are used both for training and validation.Similarly,good performances were observed(rRMSE=0:11)when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images.Conversely,poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution.Training on a mix of high-and low-resolution images allows to get very good performances on the native high-resolution(rRMSE=0:06)and synthetic low-resolution(rRMSE=0:10)images.However,very low performances are still observed over the native low-resolution images(rRMSE=0:48),mainly due to the poor quality of the native low-resolution images.Finally,an advanced super resolution method based on GAN(generative adversarial network)that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images.Results show some significant improvement(rRMSE=0:22)compared to bicubic upsampling approach,while still far below the performances achieved over the native high-resolution images.