Fu Jen Catholic University

About: Fu Jen Catholic University is a education organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Population & Hazard ratio. The organization has 6842 authors who have published 9512 publications receiving 171005 citations. The organization is also known as: FJU & Fu Jen.

Indices of novelty for emerging topic detection

Abstract: Emerging topic detection is a vital research area for researchers and scholars interested in searching for and tracking new research trends and topics. The current methods of text mining and data mining used for this purpose focus only on the frequency of which subjects are mentioned, and ignore the novelty of the subject which is also critical, but beyond the scope of a frequency study. This work tackles this inadequacy to propose a new set of indices for emerging topic detection. They are the novelty index (NI) and the published volume index (PVI). This new set of indices is created based on time, volume, frequency and represents a resolution to provide a more precise set of prediction indices. They are then utilized to determine the detection point (DP) of new emerging topics. Following the detection point, the intersection decides the worth of a new topic. The algorithms presented in this paper can be used to decide the novelty and life span of an emerging topic in a specific field. The entire comprehensive collection of the ACM Digital Library is examined in the experiments. The application of the NI and PVI gives a promising indication of emerging topics in conferences and journals.

New assay for old markers-plasma beta amyloid of mild cognitive impairment and Alzheimer's disease.

Abstract: Although there is a consensus on the reduced levels of Aβ1-42 in the CSF of patients with AD, studies of plasma Aβ levels were inconsistent and have limited clinical value. We developed an immunomagnetic reduction assay (IMR) to determine the plasma levels of Aβ. We surveyed patients with varying AD severity (CDR = 0.5, n=16; CDR ≥ 1, n=18) and controls (n=26). Significant group differences were apparent in the levels of Aβ1-42 (F = 5.54, p = 0.002) and the Aβ1-42/Aβ1-40 ratio (F = 24.198, p < 0.001). Post-hoc analyses showed significant differences in the Aβ1-42 levels of controls and AD patients (p = 0.001) and in the Aβ1-42/Aβ1-40 ratio of control, MCI and AD subjects (all p ≤ 0.001). Regression analysis of Aβ1-42/Aβ1-40 ratios on dementia severity showed an adjusted R2 of 0.553 (p = 0.001). We identified a cut-off of 16.1 pg/ml for Aβ1-42 to differentiate control subjects from patients (both AD and MCI) with 85.3% sensitivity and 88.5% specificity. We also obtained a cut-off value of 0.303 for Aβ1-42/Aβ1-40 ratios with 85.3% sensitivity and 96.2% specificity. APOE e4 carriers had significantly higher Aβ1-42/Aβ1-40 ratios than the non-carriers (F = 4.839, p = 0.015). An independent group of case-control subjects validated both cut-off values for Aβ1-42/Aβ1-40 (100% sensitivity and 83.3% specificity) and for Aβ1-42 (100% sensitivity and 75.3% specificity). In a subgroup of longitudinal follow- up study, we found that the plasma Aβ was relatively stable with an interval of approximately 3 months. In conclusion, we found that the plasma Aβ1-42 is a useful biomarker for AD. The Aβ1-42/Aβ1-40 ratio improves the diagnostic power of the plasma Aβ biomarkers. The iron nanoparticles and IMR provides a novel method to measure plasma Aβ and could serve as an important clinical tool for the diagnosis of neurodegenerative diseases.

Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming

Abstract: Hybrid flow shop scheduling problems are encountered in many real-world manufacturing operations such as computer assembly, TFT-LCD module assembly, and solar cell manufacturing. Most research considers the scheduling problem in regard to time requirements and the steps needed to improve production efficiency. However, the increasing amount of carbon emissions worldwide is contributing to the worsening global warming problem. Many countries and international organizations have started to pay attention to this problem, even creating mechanisms to reduce carbon emissions. Furthermore, manufacturing enterprises are showing growing interest in realizing energy savings. Thus, the present research study focuses on reducing energy costs and completion time at the manufacturing-system level. This paper proposed a multi-objective mixed-integer programming for energy-efficient hybrid flow shop scheduling with lot streaming in order to minimize both the production makespan and electric power consumption. Due to a trade-off between these objectives and the computational complexity of the proposed multi-objective mixed-integer program, this study adopts the genetic algorithm (GA) to obtain approximate Pareto solutions more efficiently. In addition, a multi-objective energy efficiency scheduling algorithm is also developed to calculate the fitness values of each chromosome in GA.