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.

Measurement of the Near-Threshold e+e-D(*)±D(*) Cross Section using Initial-State Radiation

Abstract: We report a measurement of the exclusive e{sup +}e{sup -}{yields}D{sup (*){+-}}D*{sup {+-}} cross section as a function of center-of-mass energy near the D{sup (*)}{+-}D*{sup {+-}} threshold with initial-state radiation. A partial reconstruction technique is used to increase the efficiency and to suppress background. The analysis is based on a data sample collected with the Belle detector with an integrated luminosity of 547.8 fb{sup -1}.

An Efficient User Authentication and User Anonymity Scheme with Provably Security for IoT-Based Medical Care System.

Abstract: In recent years, with the increase in degenerative diseases and the aging population in advanced countries, demands for medical care of older or solitary people have increased continually in hospitals and healthcare institutions. Applying wireless sensor networks for the IoT-based telemedicine system enables doctors, caregivers or families to monitor patients’ physiological conditions at anytime and anyplace according to the acquired information. However, transmitting physiological data through the Internet concerns the personal privacy of patients. Therefore, before users can access medical care services in IoT-based medical care system, they must be authenticated. Typically, user authentication and data encryption are most critical for securing network communications over a public channel between two or more participants. In 2016, Liu and Chung proposed a bilinear pairing-based password authentication scheme for wireless healthcare sensor networks. They claimed their authentication scheme cannot only secure sensor data transmission, but also resist various well-known security attacks. In this paper, we demonstrate that Liu–Chung’s scheme has some security weaknesses, and we further present an improved secure authentication and data encryption scheme for the IoT-based medical care system, which can provide user anonymity and prevent the security threats of replay and password/sensed data disclosure attacks. Moreover, we modify the authentication process to reduce redundancy in protocol design, and the proposed scheme is more efficient in performance compared with previous related schemes. Finally, the proposed scheme is provably secure in the random oracle model under ECDHP.

Development and evaluation of emulsion-liposome blends for resveratrol delivery.

Abstract: Nano- and submicron-sized vesicles are beneficial for the controlled delivery of drugs Resveratrol, the main active polyphenol in red wine, was incorporated into various combinations of emulsions and liposomes to examine its physicochemical characteristics and cardiovascular protection The blends of emulsion-liposome were composed of coconut oil, soybean lecithin, glycerol formal, and non-ionic surfactants Multiple systems were assessed by evaluating the droplet size, surface charge, drug encapsulation, release rate, and stability The vesicle diameter of the systems ranged from 114 to 195 nm The liposomal vesicles in the systems had smaller diameters (of 43 approximately 56 nm) (F6 and F7) Drug encapsulation of approximately 70% were achieved by the vesicles The inclusion of resveratrol in these systems retarded the drug release in both the presence and absence of plasma in vitro The emulsion-liposome blends which incorporated Brij 98 (F5) exhibited the slowest release at zero-order for resveratrol delivery Treatment using resveratrol in the blended formulations dramatically inhibited vascular intimal thickening, which was tested in an experimental model in which endothelial injury was produced in normal rat carotid arteries Intraperitoneal injection of the multiple systems was associated with no or negligible liver and kidney toxicity We concluded that encapsulation by the emulsion-liposome blends is a potent way to enhance the preventative and therapeutic benefits of resveratrol

Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network

Abstract: Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called “Deep Retina.” Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.