Review of Literature and Curricula in Smart Supply Chain & Transportation

This study provides a review of existing smart supply chain management (SCM) literature and current course offerings in order to identify unexplored implications of smart SCM. Specifically, the study focuses on curricula within the state of California to derive potential opportunities for the relevant practitioners in the Bay Area. In addition, the study further extends curriculum review to other well-recognized SCM programs around the U.S. By exploring current relevant course offerings from different academic institutions for higher education (i.e., universities), this research aims to deliver general ideas useful to knowledge practitioners in fields concerning SCM. Finally, the research illustrates a conceptual framework aimed at fostering familiarity with the necessary research topics for the evolving smart SCM

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states

Identification of SNPs in TG and EDG1 genes and their relationships with carcass traits in Korean cattle (Hanwoo)

Thyroglobulin (TG) gene was known to be regulated fat cell growth and differentiation and the endothelial differentiation sphingolipid G-protein-coupled receptor 1 (EDG1) gene involves blood vessel formation and known to be affecting carcass traits in beef cattle. The aim of this study was to identify the single nucleotide polymorphisms (SNPs) in both TG and EDG1 genes and to analyze the association with carcass traits in Korean cattle (Hanwoo). The T354C SNP in TG gene located at the 3’ flanking region and c.-312A>G SNP located at 3’-UTR of EDG1 gene were used for genotyping the animals using PCR-RFLP method. Three genotypes were identified in T354C SNP in TG gene and only two AA and AG genotypes were observed for the c.-312A>G SNP in EDG1 gene. The results indicated that T354C SNP in TG gene was not significantly associated with carcass traits. However, the c.-312A>G SNP in EDG1 gene had significant effects on backfat thickness (BF) and yield index (YI). These results may provide valuable information for further candidate gene studies affecting carcass traits in Korean cattle and may use as marker assisted selection for improving the quality of meat in Hanwoo. Key words : TG, EDG1, Carcass traits, Hanwo

A backward procedure for change-point detection with applications to copy number variation detection

Change-point detection regains much attention recently for analyzing array or sequencing data for copy number variation (CNV) detection. In such applications, the true signals are typically very short and buried in the long data sequence, which makes it challenging to identify the variations efficiently and accurately. In this article, we propose a new change-point detection method, a backward procedure, which is not only fast and simple enough to exploit high-dimensional data but also performs very well for detecting short signals. Although motivated by CNV detection, the backward procedure is generally applicable to assorted change-point problems that arise in a variety of scientific applications. It is illustrated by both simulated and real CNV data that the backward detection has clear advantages over other competing methods especially when the true signal is short