Exploring the Key Determinants of Bicycle Share Program Use in a Leisure Context

Over the past two decades, bicycle share programs (BSPs) have developed rapidly around the world, with studies finding that people use such service not only for commuting but also for leisure. However, compared to utilitarian BSP users, limited research has focused on the factors influencing BSP use for leisure experiences. To begin this limitation in the current cycling literature, this dissertation explores the key determinants of leisure BSP use. The extended unified theory of acceptance and use of technology proposed by Venkatesh, Thong, and Xu (2012) and the dual-attitudes model conceptualized by Wilson, Lindsey, and Schooler (2000) provided the theoretical framework guiding this research. First, this dissertation developed the Unified Measurement of Bicycle Share Program Use (UMBSPU), an encompassing scale for further investigation of factors influencing an individual\u27s leisure BSP use. The results of the measurement invariance testing and method effect examination indicated that this scale, which includes eight constructs and thirty-three measurement items, is a reliable, valid measurement. Second, this dissertation applied the UMBSPU to examine the influences of performance expectancy, effort expectancy, facilitating conditions, social influence, price value, hedonic motivation, and habit on Taipei citizens\u27 intentions to use BSP and their actual use in leisure time. Among all factors examined, habit demonstrated the strongest predict validity of use intention. Furthermore, behavioral intention outperformed habit and facilitating conditions in explaining the variance of actual use. Finally, this dissertation used two Single Target Implicit Association Tests (ST-IATs) to explore BSP users\u27 implicit attitudes toward leisure cycling and leisure cyclists. Explicit attitudes toward leisure cycling and social identity with leisure cyclists were also measured and compared with implicit attitudes, the results indicating that implicit attitudes did not significantly predict leisure BSP use. However, social identity exhibited a strong predictability of an individual\u27s public bicycle riding frequency. Future research is needed to cross-validate the UMBSPU in different contexts and to compare the results from the leisure cycling and cyclists ST-IAT across different types of cyclist groups

Freeform Fabrication of Biological Scaffolds by Projection Photopolymerization

This article presents a micro-manufacturing method for direct, projection printing of 3- dimensional (3D) scaffolds for applications in the field of tissue engineering by using a digital micro-mirror-array device (DMD) in a layer-by-layer process. Multi-layered scaffolds are microfabricated using curable materials through an ultraviolet (UV) photopolymerization process. The pre-patterned UV light is projected onto the photocurable polymer solution by creating the “photomask” design with graphic software. Poly (ethylene glycol) diacrylate (PEGDA), is mixed with a small amount of dye (0.3 wt %) to enhance the fabrication resolution of the scaffold. The DMD fabrication system is equipped with a purging mechanism to prevent the accumulation of oligomer, which could interfere with the feature resolution of previously polymerized layers. The surfaces of the pre-designed, multi-layered scaffold are covalently conjugated with fibronectin for efficient cellular attachment. Our results show that murine marrow-derived progenitor cells successfully attached to fibronectin-modified scaffolds.Mechanical Engineerin

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table

Are systematic reviews up-to-date at the time of publication?

BACKGROUND: Systematic reviews provide a synthesis of evidence for practitioners, for clinical practice guideline developers, and for those designing and justifying primary research. Having an up-to-date and comprehensive review is therefore important. Our main objective was to determine the recency of systematic reviews at the time of their publication, as measured by the time from last search date to publication. We also wanted to study the time from search date to acceptance, and from acceptance to publication, and measure the proportion of systematic reviews with recorded information on search dates and information sources in the abstract and full text of the review. METHODS: A descriptive analysis of published systematic reviews indexed in Medline in 2009, 2010 and 2011 by three reviewers, independently extracting data. RESULTS: Of the 300 systematic reviews included, 271 (90%) provided the date of search in the full-text article, but only 141 (47%) stated this in the abstract. The median (standard error; minimum to maximum) survival time from last search to acceptance was 5.1 (0.58; 0 to 43.8) months (95% confidence interval = 3.9 to 6.2) and from last search to first publication time was 8.0 (0.35; 0 to 46.7) months (95% confidence interval = 7.3 to 8.7), respectively. Of the 300 reviews, 295 (98%) stated which databases had been searched, but only 181 (60%) stated the databases in the abstract. Most researchers searched three (35%) or four (21%) databases. The top-three most used databases were MEDLINE (79%), Cochrane library (76%), and EMBASE (64%). CONCLUSIONS: Being able to identify comprehensive, up-to-date reviews is important to clinicians, guideline groups, and those designing clinical trials. This study demonstrates that some reviews have a considerable delay between search and publication, but only 47% of systematic review abstracts stated the last search date and 60% stated the databases that had been searched. Improvements in the quality of abstracts of systematic reviews and ways to shorten the review and revision processes to make review publication more rapid are needed