Modern Photodetector IR-Modules

Journal of Communications Technology and Electronics - In recent years, there has been a rapid improvement in photonics products due to the use of multilayer heterostructures grown on the basis of...     

Special Issue on Artificial Intelligence and Technology: Trend,Challenges, and Opportunities

Mobile Networks and Applications -     

Fabrication of high-efficiency Yb:Y2O3 laser ceramics without photodarkening

High-efficiency Yb:Y₂O₃ laser ceramics were fabricated using the vacuum-sintering plus hot isostatic pressing (HIP) without sintering additives. High-purity well-dispersed nanocrystalline Yb:Y₂O₃ powder was synthesized using a modified co-precipitation method in-house. The green bodies were first vacuum sintered at a temperature as low as 1430°C and then HIPed at 1450°C. Finally, the samples were air annealed at 800°C for 10 h. Although no sintering aids were used, full density of the samples with excellent optical homogeneity and an inline transmission of 80% at 400 nm could be obtained. Moreover, photodarkening phenomenon was not detected in the ceramics. Preliminary laser experiment with the fabricated ceramics in a two-mirror cavity has demonstrated 32 W continuous-wave (CW) output at ∼1077 nm with an optical-to-optical conversion efficiency of 58.2%. To the best of our knowledge, this is so far the highest CW output power and optical-to-optical conversion efficiency achieved with the Yb³⁺-doped sesquioxide ceramics in a simple two-mirror cavity.     

Improved thermal stability of the near-infrared Al-modulated Zn3Ga2GeO8: Cr3+ phosphors for plant growth applications

The exploration of the high thermal stability near-infrared (NIR) phosphors is significantly crucial for the development of plant lighting. However, NIR phosphors suffer from the poor chemical and thermal stability, which severely limits their long-term operation. Here, the successful improvement of luminous intensity (149.5%) and thermal stability at 423 K of Zn₃Ga₂GeO₈ (ZGGO): Cr³⁺ phosphors is achieved for the introduction of Al³⁺ ions into the host. The release of carriers in deep traps inhibits the emission loss for the thermal disturbance. Furthermore, an NIR light emitting diodes (LEDs) lamp is explored by combining the optimized Zn₃Ga_1.1675Al_0.8GeO₈: 0.0325Cr³⁺ phosphors with a commercial 460 nm blue chip, and the emission band can match well with the absorption bands of photosynthetic pigments and the phytochrome (P_R and P_FR) of plants. The explored LEDs lamp further determines the growth and the pheromone content of the involved plants for the participation of the NIR emission originated from Cr³⁺ ions. Our work provides a promising NIR lamp as plant light with improved thermal stability for long-term operation.     

GM-CSF及CBS在肝损伤中的研究进展

肝损伤(liver injury)是指基因遗传或外部原因诱发的肝脏功能异常性疾病.临床表现为食欲不振、乏力、恶心、黄疸、疲乏等,严重者将出现肝功能衰竭,致死风险极高.在交通肇事、暴力犯罪及意外事件中,肝由于体积大、位置固定、质地脆弱、血运丰富等原因,往往是最容易发生损伤的器官之一,肝损伤后的死亡率可高达10.5％～25％[1].随着时间的变化,肝损伤的程度、病理改变、GM-CSF等相关细胞因子及CBS等相关酶的表达水平也会进一步变化.肝损伤后,由于肝内部环境稳态的破坏,器官的状态、结构、各种蛋白的转录水平、蛋白及细胞因子的表达变化水平也会发生一定程度的改变.因此定性定量地分析这些变化,找到时间相关性强的特异性指标,对于指导法医学的实践具有重要的作用.因此在法医学鉴定中,通过肝脏损伤及病变程度的改变,明确肝损伤病变与死亡时间的关系有利于判断事件或案件性质,并有助于进一步对责任方的认定.     

Optimizing the Structure of a Production System for Activated Carbon

Russian Engineering Research - A system for the production of activated carbon is considered. The thermal deformation of its components in operation is of interest. A frame design is developed,...     

NeuCrowd: neural sampling network for representation learning with crowdsourced labels

Knowledge and Information Systems - Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart...     

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students’ behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.