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 & Medicine. The organization has 6842 authors who have published 9512 publications receiving 171005 citations. The organization is also known as: FJU & Fu Jen.

Remote power On/Off control and current measurement for home electric outlets based on a low-power embedded board and ZigBee communication

Abstract: In this paper we have designed a remote power on/off control and a current measurement for electric outlets, based on both an embedded board and on ZigBee communication. This design consists of two parts: the ZigBee control module and the server module. The ZigBee control module contains several controllable outlets, a current measurement circuit, the ZigBee receiving and transmission circuit and a micro control unit. The measurement circuit senses the current and sends back a signal to the server module through the ZigBee. The measurement data of the current and voltage detection can be stored in the embedded board, and they can be designed to become aware of any overload and to send out a message to the circuit breaker for safety. We use Visual Basic as the interface software for the design of the graphic user interface to provide a user-friendly operation of a typical homepsilas electric outlets.

Morphological and Molecular Defects in Human Three-Dimensional Retinal Organoid Model of X-Linked Juvenile Retinoschisis

Abstract: Summary X-linked juvenile retinoschisis (XLRS), linked to mutations in the RS1 gene, is a degenerative retinopathy with a retinal splitting phenotype. We generated human induced pluripotent stem cells (hiPSCs) from patients to study XLRS in a 3D retinal organoid in vitro differentiation system. This model recapitulates key features of XLRS including retinal splitting, defective retinoschisin production, outer-segment defects, abnormal paxillin turnover, and impaired ER-Golgi transportation. RS1 mutation also affects the development of photoreceptor sensory cilia and results in altered expression of other retinopathy-associated genes. CRISPR/Cas9 correction of the disease-associated C625T mutation normalizes the splitting phenotype, outer-segment defects, paxillin dynamics, ciliary marker expression, and transcriptome profiles. Likewise, mutating RS1 in control hiPSCs produces the disease-associated phenotypes. Finally, we show that the C625T mutation can be repaired precisely and efficiently using a base-editing approach. Taken together, our data establish 3D organoids as a valid disease model.

Resource Allocation for 5G Heterogeneous Cloud Radio Access Networks With D2D Communication: A Matching and Coalition Approach

Abstract: Device-to-Device (D2D) communication is a promising technology toward the fifth generation mobile communication. However, when D2D communication is incorporated into heterogeneous cloud radio access network (H-CRAN), the interference management between the D2D users and current users is a challenge. In this paper, we study how to assign the sub-channels of different bandwidth to multiple D2D pairs and the remote radio head users. In such a way, the sub-channels that have been pre-allocated to macro-cell users can be reused, the system performance can be maximized while the quality of service of all users can be guaranteed. Such a resource allocation problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To obtain the solution, the proposed problem is reformulated into a many-to-one matching sub-game with externality followed by a coalition sub-game. Then, a constrained deferred acceptance algorithm and a coalition formation algorithm are proposed to find solutions to these two sequential sub-games, respectively. Finally, we prove theoretically that both the proposed algorithms can convergent with a low computational complexity. Numerical results demonstrate that compared with existing resource allocation schemes, our proposed scheme can significantly improve the system performance in terms of overall throughput, the total number of admitted users and fairness.

Systematic review with meta-analysis: the efficacy of levofloxacin triple therapy as the first- or second-line treatments of Helicobacter pylori infection.

Abstract: SummaryBackground Levofloxacin triple therapy has been used for the first-line and second-line treatment of Helicobacter pylori infection for more than 10 years. Aims To systematically review the efficacy of levofloxacin triple therapy in the first- and second-line treatment, and to assess the time trend and factors that might affect its efficacy. Methods Prospective trials reporting the efficacy of levofloxacin triple therapy in either the first-line or second-line treatment of H. pylori infection in adults were searched from the PubMed and Cochrane database from January 2000 to September 2015. Meta-analysis was performed to calculate the cumulative eradication rate and the efficacies in subgroups. Results Of the 322 articles identified, a total of 4574 patients from 41 trials, including 16 trials in the first-line treatment and 25 trials in the second-line treatment were eligible for analysis. The cumulative eradication rate was 77.3% (95% confidence intervals, CI: 74.7–79.6) and was 80.7% (95% CI 77.1–83.7) in the first-line treatment and 74.5% (95% CI: 70.9–77.8) in the second-line treatment. The efficacies of levofloxacin triple therapy before 2008, between 2009 and 2011, and after 2012 were 77.4%, 79.6% and 74.8% respectively. The eradication rate was higher when levofloxacin was given once daily (80.6%, 95% CI: 77.1–83.7) than twice daily (73.6%, 95% CI: 69.7–77.2). The efficacy was significantly higher in levofloxacin-susceptible strains than resistant strains (81.1% vs. 36.3%, risk ratio 2.18, 95% CI: 1.6–3, P < 0.001). Conclusion The efficacy of levofloxacin triple therapy has been lower than 80% in many countries and it is not recommended when the levofloxacin resistance is higher than 5–10%.

Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm

Abstract: In this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. Fuzzy-neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, genetic algorithms are adopted because of their capabilities of directed random search for global optimization. It is well known, however, that the searching speed of the conventional genetic algorithms is not desirable. Such conventional genetic algorithms are inherently incapable of dealing with a vast number (over 100) of adjustable parameters in the fuzzy-neural networks. In this paper, the RGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed, serving as a single gene crossover operation. Chromosomes consisting of both, the control points of BMFs and the weightings of the fuzzy-neural network are coded as an adjustable vector with real number components that are searched by the RGA. Simulation results have shown that faster convergence of the evolution process searching for an optimal fuzzy-neural network can be achieved. Examples of nonlinear functions approximated by using the fuzzy-neural network via the RGA are demonstrated to illustrate the effectiveness of the proposed method.