Beyond usability -- affect in web browsing

This research concentrates on the visual aesthetics of a website, investigating the web user's affective/emotional reactions to different designs of web homepage aesthetics and their influence on subsequent behaviors of web users. Drawing on the existing theories and empirical findings in environmental psychology, human-computer interaction, aesthetics, and marketing research literature, a research model is developed to explore the relationships between the visual aesthetic qualities of a website homepage - webpage visual complexity and order, induced emotional states in users, and users' approach behaviors toward the website. The model predicts that the visual aesthetics of a web homepage elicit specific emotional responses by provoking intrinsic feelings of pleasantness / unpleasantness, arousal, as well as motivational pleasantness / unpleasantness in web users. These elicited emotional responses, which mediate the effect of homepage aesthetic features, in turn affect web users' subsequent behaviors toward the website, such as further approaching/exploring or avoiding the website. A set of pilot studies and a main laboratory experiment were conducted to test the model and its associated hypotheses. Based on the results of pilot studies, 12 versions of a Gift website's homepage, which varied at four levels of complexity and three levels of order, were selected the stimuli materials for the main experiment. A total of 467 undergraduate students participated in the main study. During the main study, we instructed the participants to browse the homepage stimuli for a goal-oriented web search activity or an excitement/enjoyment-seeking web browsing activity, measured how they felt about the homepage and their degree of approach/avoidance tendencies toward the entire website. The results of the study generally confirmed the belief that a web user's initial emotional responses (i.e., pleasantness and arousal) evoked by the aesthetic qualities of a website's homepage he/she first encounters will have carry-over effects on his/her subsequent approach behaviors toward the website

Effects of astragaloside IV on inflammation and immunity in rats with experimental periodontitis

Abstract: This study aimed to investigate the effects of astragaloside IV (AsIV) on inflammation and immunity in rats with experimental periodontitis. Periodontitis was established in 48 Wistar rats, which were then randomly divided into model and 10, 20 and 40 mg/kg AsIV groups, with 12 rats in each group. The latter 3 groups were treated with AsIV at doses of 10, 20 and 40 mg/kg, respectively. The control group (12 rats, without periodontitis) and model group were given the same amount of 5% sodium carboxymethyl cellulose. The treatment was performed once per day for 8 weeks. Before and after treatment, the tooth mobility scores of the rats were determined. After treatment, the salivary occult blood index (SOBI), plaque index (PLI), peripheral blood T lymphocyte subsets, and serum inflammatory factor and immunoglobulin levels were determined. The results showed that, after treatment, compared with that in model group, in 40 mg/kg AsIV group, the general state of rats was improved, while the tooth mobility score, SOBI and PLI were significantly decreased (p < 0.05); the peripheral blood CD4+ T cell percentage and CD4+/CD8+ ratio were significantly increased (p < 0.05), while the CD8+ T cell percentage was significantly decreased (p < 0.05); the serum tumor necrosis factor-α, interleukin-1β and interleukin-2 levels were significantly decreased (p < 0.05); the serum immunoglobulin A and immunoglobulin G levels were significantly decreased (p < 0.05). In conclusion, AsIV can alleviate inflammation and enhance immunity in rats with experimental periodontitis

Learning through Telemedicine Networks

Telemedicine is advocated for its potential to improve the accessibility and quality of health care delivery while lowering costs [1]. Although the potential benefits of telemedicine have long been a subject of research and intense discussion, the results of actual implementations have been far from conclusive. Most current research, which views telemedicine as a substitute for travel and a basis for economies of scale, is rather limited in exploring the full potential of telemedicine. In this paper, we develop a new framework in which organizational learning is the theoretical basis for explaining the development and potential benefits of telemedicine

Experimental Stimuli

We conducted two pilot studies to select the appropriate e-commerce website type and contents for the homepage stimuli. The purpose of Pilot Study 1 was to select a website category with which subjects are not familiar, for which they show neither liking nor disliking, but have some interests in browsing. Unfamiliarity with the website was required because familiarity with a certain category of website may influence perceived complexity of (Radocy and Boyle 1988) and liking for the webpage stimuli (Bornstein 1989; Zajonc 2000). We needed a website for which subjects showed neither liking nor disliking so that the manipulation of webpage stimuli in the experiment could be assumed to be the major influence on their reported emotional responses and approach tendencies. To have some degree of interest in browsing the website is necessary for subjects to engage in experiential web-browsing activities with the webpage stimuli. Based on the results of Pilot Study 1, we selected the gifts website as the context for the experimental stimuli. Then, we conducted Pilot Study 2 to identify appropriate gift items to be included in the webpage stimuli. Thirteen gift items, which were shown to elicit neutral affect in the subjects and to be of some interest to the subjects for browsing or purchase, were selected for the website. Utilizing Geissler et al.’s (2001) findings regarding the influence of amount of text, number of links, and number of graphics on user’s perceived complexity of webpage, we designed four levels of complexity (complexity increases from level 1 to level 4) into the experimental stimuli by manipulating the number of links, number of graphics, and amount of text (see Table A1). We also manipulated webpage order at three levels (order increases from level 1 to level 3) by arranging the layout of webpage elements. According to our definition of order, webpage order is related to the logical organization, coherence, and clarity of webpage content. We use

Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images

Ship detection is an important but challenging task in the field of computer vision, partially due to the minuscule ship objects in optical remote sensing images and the interference of clouds occlusion and strong waves. Most of the current ship detection methods focus on boosting detection accuracy while they may ignore the detection speed. However, it is also indispensable to increase ship detection speed because it can provide timely ocean rescue and maritime surveillance. To solve the above problems, we propose an improved YOLOv3 (ImYOLOv3) based on attention mechanism, aiming to achieve the best trade-off between detection accuracy and speed. First, to realize high-efficiency ship detection, we adopt the off-the-shelf YOLOv3 as our basic detection framework due to its fast speed. Second, to boost the performance of original YOLOv3 for small ships, we design a novel and lightweight dilated attention module (DAM) to extract discriminative features for ship targets, which can be easily embedded into the basic YOLOv3. The integrated attention mechanism can help our model learn to suppress irrelevant regions while highlighting salient features useful for ship detection task. Furthermore, we introduce a multi-class ship dataset (MSD) and explicitly set supervised subclass according to the scales and moving states of ships. Extensive experiments verify the effectiveness and robustness of ImYOLOv3, and show that our method can accurately detect ships with different scales in different backgrounds, while at a real-time speed

A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods