Both chemodynamic therapy and photodynamic therapy, based on the production of reactive oxygen (ROS), have excellent potential in cancer therapy. However, the abnormal redox homeostasis in tumor cells, especially the overexpressed glutathione (GSH) could scavenge ROS and reduce the anti-tumor efficiency. Therefore, it is essential to develop a simple and effective tumor-specific drug delivery system for modulating the tumor microenvironment (TME) and achieving synergistic therapy at the tumor site. In this study, self-assembled nanoparticles (named CDZP NPs) were developed using copper ion (Cu
The effects of thermal treatment on Cu-O/Al2O3, Fe-O/Al2O3, Cu-Fe-O/Al2O3 and La-Cu-Fe-O/Al2O3 at 750~1050℃ are studied. The changes in BET surface, average particle size in relation to the interaction between Al2O3 and active components are elucidated. It is found that both phase transition of γ-Al2O3 to α-Al2O3 and sintering of active components cccurred on two samples containing ferric oxides at 850℃. Such a phase transition occurred only until 950℃ for Cu-O/Al2O3. The effects on the reduction of phase transition temperature of γ-Al2O3 follow Cu-Fe>Fe>Cu. La2O3 raises phase transition temperature of γ-Al2O3 to α-Al2O3 and resistence sintering of active components. As detected by XRD the thermal stable LaAlO3 is formed at 850℃. LaAlO3 may highly disperse into the active components retarding migration and agglomeration of active components at high temperature.
To achieve the quantitative assessment of unmanned ground vehicles, it is necessary to quantitative analysis of the test environment. As an important part of the environmental factors, road has a major impact on test and evaluation for unmanned ground vehicles. However, previous studies on road are mainly based on the concept of road roughness. Because of the unicity of road feature indicators, road complexity can only be reflected to a certain extent. In order to show the complexity of road more comprehensively, this paper proposes a multi-feature-based road complexity calculation model in off-road environment. First, a multi-sensor-based data acquisition mobile platform is established to obtain more complete road data. Then, based on the analysis of road feature, road indicators like road roughness, average slope and adhesion characteristics of travelable area are obtained. According to the analysis methods of road roughness, the principle of analytic hierarchy process and the data collected from off-road environment, the calculation model of road complexity is determined. Finally, by calculating complexities of cross-country roads, the feasibility of this model is verified. The result shows that this model can quantitatively analyze road complexity in off-road environment and provide a theoretical support for the scientific calculation of different road complexities.
The highly contagious novel Coronavirus Disease 2019 (COVID-19) broke out at the end of 2019 and has lasted for nearly one year, and the pandemic is still rampant around the world. The diagnosis of COVID-19 is on the basis of the combination of epidemiological history, clinical symptoms, and laboratory and imaging examinations. Among them, imaging examination is of importance in the diagnosis of patients with suspected clinical cases, the investigation of asymptomatic infections and family clustering, the judgment of patient recovery, rediagnosis after disease recurrence, and prognosis prediction. This article reviews the research progress of CT imaging examination in the COVID-19 pandemic.
The effect of electropulsing treatment (EPT) on quasi-static compression behavior and anisotropy of AZ31 Mg alloy were investigated based on quasi-static compression test. The results show that the orientation of the sample has a significant effect on the deformation mechanism of metal and this mechanism can be changed by pre-deformation and EPT. However, the strain rate sensitivity of the material is not affected by pre-deformation. Compared with the as-received plates, the anisotropy of AZ31 Mg alloy increased in ND-RD plane after pre-compression along the transverse direction, the ∆YS of specimens increased from 87 MPa to 98 MPa. After EPT, the anisotropy of AZ31 Mg alloy gradually decreased with the change of EPT temperature and EPT time, the ∆YS of the sample reached 14.2 MPa when EPT conducted at 200 ℃ for 15 min, which decreased 72.8 MPa compared with the as-received plates.
Quality control is of vital importance during electronics production. As the methods of producing electronic circuits improve, there is an increasing chance of solder defects during assembling the printed circuit board (PCB). Many technologies have been incorporated for inspecting failed soldering, such as X-ray imaging, optical imaging, and thermal imaging. With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images. However, current algorithms sometimes are not accurate enough to meet the quality control. Specialists are needed to do a follow-up checking. For automated X-ray inspection, joint of interest on the X-ray image is located by region of interest (ROI) and inspected by some algorithms. Some incorrect ROIs deteriorate the inspection algorithm. The high dimension of X-ray images and the varying sizes of image dimensions also challenge the inspection algorithms. On the other hand, recent advances on deep learning shed light on image-based tasks and are competitive to human levels. In this paper, deep learning is incorporated in X-ray imaging based quality control during PCB quality inspection. Two artificial intelligence (AI) based models are proposed and compared for joint defect detection. The noised ROI problem and the varying sizes of imaging dimension problem are addressed. The efficacy of the proposed methods are verified through experimenting on a real-world 3D X-ray dataset. By incorporating the proposed methods, specialist inspection workload is largely saved.
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