A Spatial Point Pattern Analysis to Recognize Fail Bit Patterns in Semiconductor Manufacturing

The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns

Trends in Nursing Research on Infections: Semantic Network Analysis and Topic Modeling

Background: Many countries around the world are currently threatened by the COVID-19 pandemic, and nurses are facing increasing responsibilities and work demands related to infection control. To establish a developmental strategy for infection control, it is important to analyze, understand, or visualize the accumulated data gathered from research in the field of nursing. Methods: A total of 4854 articles published between 1978 and 2017 were retrieved from the Web of Science. Abstracts from these articles were extracted, and network analysis was conducted using the semantic network module. Results: ‘wound’, ‘injury’, ‘breast’, “dressing”, ‘temperature’, ‘drainage’, ‘diabetes’, ‘abscess’, and ‘cleaning’ were identified as the keywords with high values of degree centrality, betweenness centrality, and closeness centrality; hence, they were determined to be influential in the network. The major topics were ‘PLWH’ (people living with HIV), ‘pregnancy’, and ‘STI’ (sexually transmitted infection). Conclusions: Diverse infection research has been conducted on the topics of blood-borne infections, sexually transmitted infections, respiratory infections, urinary tract infections, and bacterial infections. STIs (including HIV), pregnancy, and bacterial infections have been the focus of particularly intense research by nursing researchers. More research on viral infections, urinary tract infections, immune topic, and hospital-acquired infections will be needed

Ratio of Autoantibodies of Tumor Suppressor AIMP2 and Its Oncogenic Variant Is Associated with Clinical Outcome in Lung Cancer

Aminoacyl-tRNA synthetase-interacting multi-functional protein 2 (AIMP2) works as potent tumor suppressor, while its splicing variant lacking exon 2 (AIMP2-DX2) competes with AIMP2 for binding to target proteins and compromises its anti-tumor activity. Assuming that AIMP2 and its variant AIMP2-DX2 could be released out to human sera in pathological condition, we investigated the diagnostic and prognostic usefulness of autoantibodies against AIMP2 and AIMP2-DX2 by measuring their serum levels in 80 normal and lung cancer samples that were matched in age, gender and smoking status. The area under the curve of AIMP2-DX2, AIMP2, and AIMP2-DX2/AIMP2 autoantibody ratio was low (0.416, 0.579, and 0.357, respectively), suggesting limited diagnostic value. A total of 165 lung cancer patients were classified into low and high AIMP2-DX2, AIMP2, and AIMP2-DX2/AIMP2 based on the median expression of each parameter. The high AIMP2-DX2 group was older and had larger tumors (>3 cm) than the low AIMP2-DX2 group. The high AIMP2-DX2/AIMP2 group had higher CYFRA-21 levels and significantly shorter overall survival than the low AIMP2-DX2/AIMP2 group (18.6 vs. 48.9 months, P = 0.021, Log Rank Test). Taken together, autoantibodies against AIMP2-DX2 and AIMP2 are detectable in the human blood and the increased ratio of AIMP2-DX2/AIMP2 is related to poor clinical outcome of lung cancer