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.

Post-recurrence survival in completely resected stage I non-small cell lung cancer with local recurrence

Abstract: Objective: Resection is the best treatment for patients with stage I non-small cell lung cancer (NSCLC). Patterns of disease recurrence after complete resection in stage I NSCLC have not been well demonstrated. The aim of this study was to evaluate the prognostic predictors of post-recurrence survival in patients with resected stage I NSCLC with local recurrence. Methods: The clinicopathological characteristics of 123 patients with local recurrence after complete resection of stage I NSCLC in Taipei Veterans General Hospital between 1980 and 2000 were retrospectively reviewed. Post-recurrence survival and their predictors were analysed. Results: The patterns of local recurrence included local only in 74 (60.2%) and both local and distant in 49 (39.8%) patients. The 1 and 2 year post-recurrence survival rates for the 74 patients with local only recurrence were 48.7% and 17.6%, respectively. Tumour size (p = 0.033) and treatment for initial recurrence (p Conclusions: Treatment for initial recurrence is a prognostic predictor for post-recurrence survival in resected stage I NSCLC with local recurrence. Complete surgical resection should be considered in selected candidates with resectable local recurrent disease.

Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems

Abstract: In this paper, a soft computing technique for estimating battery state-of-charge of individual batteries in a battery string is proposed. The soft computing approach uses a fusion of a fuzzy neural network (FNN) with B-spline membership functions (BMFs) and a reduced-form genetic algorithm (RGA). The algorithm is employed to tune both control points of the BMFs and the weights of the FNNs. The traditional multiple-input multiple-output FNN (MIMOFNN) cannot directly be used in this paper. The main reason is that there are too many free parameters in the MIMOFNN to be trained if many inputs are required. In this paper, a merged multiple-input single-output (MISO) FNN is proposed and can be trained by the RGA optimization approach. The merged MISO FNN with RGA (FNNRGA) can achieve faster convergence and lower estimation error than neural networks with the back propagation method. From experimental results, the proposed merged MISO FNNRGA is superior, more robust than the traditional method, and the overfitting suppression features are significantly improved.

Evidence for a new resonance and search for the Y(4140) in the γγ→φJ/ψ process

Abstract: The process gammagamma-->phiJ/psi is measured using a data sample of 825 fb{-1} collected with the Belle detector. A narrow peak of 8.8{-3.2}{+4.2} events, with a significance of 3.2 standard deviations including systematic uncertainty, is observed. The mass and natural width of the structure [named X(4350)] are measured to be [4350.6{-5.1}{+4.6}(stat)+/-0.7(syst)] MeV/c{2} and [13{-9}{+18}(stat)+/-4(syst)] MeV, respectively. The product of its two-photon decay width and branching fraction to phiJ/psi is [6.7{-2.4}{+3.2}(stat)+/-1.1(syst)] eV for J{P}=0{+}, or [1.5{-0.6}{+0.7}(stat)+/-0.3(syst)] eV for J{P}=2{+}. No signal for the Y(4140)-->phiJ/psi structure reported by the CDF Collaboration in B-->K{+}phiJ/psi decays is observed, and limits of Gamma_{gammagamma}(Y(4140))B(Y(4140)-->phiJ/psi)<41 eV for J{P}=0;{+} or <6.0 eV for J{P}=2{+} are determined at the 90% C.L. This disfavors the scenario in which the Y(4140) is a D{s}{*+}D{s}{*-} molecule.