An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

Effects of sintering in a high magnetic field on properties of vitrified bond and vitrified CBN composites

Vitrified bond CBN grinding wheels are being widely used due to their superior performance. Also, advantages of vitrified grinding wheels are high elastic modulus, stable chemical property, and low thermal expansion coefficient. Brittleness and low strength are key factors restricting the development of vitrified bond CBN grinding wheels. In this paper, the sintering in a high magnetic field was innovatively introduced into the manufacturing of vitrified bond CBN grinding wheels, and the effects of sintering in a high magnetic field on properties on vitrified bond and vitrified CBN composites were systematically investigated. Vitrified bond was characterized using three-point bending, scanning electron microscopy, X-ray diffraction. It was observed that microstructure of vitrified bond could be changed, grain orientation could be controlled and average grain size could be decreased in a high magnetic field, while vitrified bond strength could be simultaneously improved. High quality vitrified bond could be obtained by appropriately adjusting the strength and direction of high magnetic field. Results demonstrated that vitrified bond properties were improved when the magnetic field strength was 6?T. In order to highlight the high magnetic field effect on the vitrified CBN composites, the ordinary CBN abrasives and nickel plated CBN abrasives were used respectively. Microstructures, bending strengths of vitrified CBN composites were compared in different high magnetic fields. When the magnetic field strength was appropriate (less than 6?T), the binding characteristic of vitrified bond CBN composites with nickel plated CBN abrasives was greatly improved. The highest bending strength value of vitrified CBN composites was 79.5?MPa in 6?T high magnetic field.     

不同煤阶煤孔隙特征及其吸附能力响应

我国是煤炭资源大国，煤层气储量规模相当可观，但煤储层又具有低孔、低渗的不足，照搬国外或常规石油天然气的开采程序和方法已被证实是走不通的。应结合实际，根据不同地质条件、不同煤阶的开采程序，进行孔隙特征研究。煤孔隙特征、连通性和吸附能力对煤层气开采影响尤为重要，为研究煤孔隙结构特征随煤变质程度的变化关系及其吸附能力的响应特点，采取不同地区不同变质程度煤样，进行压汞测试和等温吸附实验。实验结果表明，煤孔隙度和平均孔径均随变质程度增加呈现降低—升高—降低的趋势；煤中孔隙连通性随煤变质程度增加逐渐变差；随煤变质程度增加，其最大吸附能力也呈现降低—升高—降低的总体趋势。     

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

     

In-situ crystal growth and photoluminescence properties of YBO3: Tb3+ microstructures

Three-dimensional (3D) YBO₃:Tb³⁺ flower-like and dense flower-like hierarchitecture constituted of nanoflakes are solvothermally synthesized in the presence of polyborate precursors in the mixture of ethanol and water. The growth process of the YBO₃:Tb³⁺ flowers and dense flowers was explored based on the time-dependent experiment and the results showed that the growth mechanism follows an in situ growth rather than self-assembly process as reported previously. YBO₃:Tb³⁺ morphologies composed of nanoflakes are achieved by controlling the concentration of ethanol and dependence of photoluminescence on morphology was studied. Remarkable photoluminescence enhancement was observed for YBO₃:Tb³⁺ with flower-like morphology demonstrating the potential of the microstructure in future applications as a green phosphor. Such a synthetic method and growth mechanism may be applied to fabricate complex 3D architectures of other materials.     

Functionalized graphene oxide nanosheets with unique three-inone properties for efficient and tunable antibacterial applications

Developing antibiotics-independent antibacterial agents is of great importance since antibiotic therapy faces great challenges from drug resistance.Graphene oxide(GO)is a promising agent due to its natural antibacterial mechanisms,such as sharp edgemediated cutting effect.However,the antibacterial activity of GO is limited by its negative charge and low photothermal effect.Herein,the amino-functionalized GO nanosheets(AGO)with unique three-in-one properties were synthesized.Three essential properties(positive charge,strong photothermal effect,and natural cutting effect)were integrated into AGO.The positive charge(30 mV)rendered AGO a strong interaction force with model pathogen Streptococcus mutans(330 nN).The natural cutting effect of 100 ng·mL^-1AGO caused 27%loss of bacterial viability after incubation for 30 min.Most importantly,upon the near-infrared irradiation for just 5 min,the three-in-one properties of AGO caused 98%viability loss.In conclusion,the short irradiation period and the tunable antibacterial activity confer the three-in-one AGO a great potential for clinical use.