Development of a compact DFB laser interferometer for high-speed inline displacement measurement

A compact fiber laser interferometer, capable of precise displacement measurements, was devised using a 1550 nm DFB laser for frequency modulation up to 300 kHz. The system comprises a fiber laser, a fiber-optic interferometer, and a wavelength modulation system. The presented method offers nanometer-scale precision and incorporates a hydrogen cyanide (HCN) gas cell for monitoring the laser's central wavelength. A technique to compensate for modulation depth has been developed to maintain a consistent modulation depth over extensive measurement ranges. Preliminary trials reveal a measurement bias of less than 40 nm achieved over a 300 mm range at a speed of 100 mm/s compared to a calibrated interferometer. This approach proves beneficial for high-precision measurements for inline semiconductor manufacturing processes

Students’ persistence intention in MOOCs in the psychomotor domain: An extended 3P model of the teaching and learning perspective

This study proposed and tested a model adapted from Biggs’ 3P model that quantifies the behaviors of students who completed MOOCs (Massive Open Online Courses) in order to design intervention measures for low retention rates. Psychomotor domain data from 300 MOOC learners was analyzed in a covariance-based structural equation model (CB-SEM) to analyze the direct and indirect effects of various factors. Results show the basic psychological needs theory (BPNT) in the presage stage significantly positively correlated with engagement in the process stage. Meanwhile, the process stage exhibited a significantly positive correlation with the product stage, representing persistence intention (PI). Furthermore, a full mediation effect was observed among the presage, process, and product stages. The mediating effect demonstrates that higher student engagement leads to more positive exertion on BNPT and PI to complete the course. Moreover, results show bolstering students’ behavioral, emotional, and cognitive engagement strengthens their PIs

Credit Card Risk Assessment Using Artificial Neural Networks

In recent years, the supply and demand of the plastic currency market has been rapidly increasing, especially, for the growth rate in the credit card market. It has increased about 16 times in the last 10 years. Many banks devoted to make a large investment in credit card marketing for the sake of getting the maximum profits in the worldwide market. However, most banks are trying to reduce the requirements for credit card application in order to increase the motivation of the customers for applying their credit cards. As a result, many banks somehow ignore the risk management of credit card approval which leads to the increases of bad debt in the credit card market. When this scenario happens year by year, those banks will not get profits from the credit card market but a great loss. In this study, a total of 113,048 entries were used which included fundamental customer data, credit card data, and customer history data from Joint Credit Information Center (JCIC) of Taiwan. We used the characteristics of artificial neural networks and grey theory to find out the potential factors of the bad credit and finally used the correlation method to find out the higher (important) relative variables (parameters) of bad credit. 80,000 entries were randomly selected as training data and the remaining 33,084 entries were used as testing data. The experimental results shown that the accuracy of forecasting rate for the proposed early warning system was an overall of 92.7%. These results suggested that the once the collections of the new customer data were available, the proposed approach could be used as an early warning system which can be used to decrease the risk of credit card approval

Reconstruction of phyletic trees by global alignment of multiple metabolic networks

Background: In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information. Results: We explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis. Conclusions: We demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.National Institutes of Health (U.S.) (Grant GM081871

From insulator to quantum Hall liquid at low magnetic fields

We have performed low-temperature transport measurements on a GaAs two-dimensional electron system at low magnetic fields. Multiple temperature-independent points and accompanying oscillations are observed in the longitudinal resistivity between the low-field insulator and the quantum Hall (QH) liquid. Our results support the existence of an intermediate regime, where the amplitudes of magneto-oscillations can be well described by conventional Shubnikov-de Haas theory, between the low-field insulator and QH liquid.Comment: Magneto-oscillations governed by Shubnikov-de Haas theory are observed between the low-field insulator and quantum Hall liqui