Although artificial intelligence (AI) is currently a prominent technology that has received increasing attention, the factors affecting users’ AI device adoption for healthcare were less discussed. Drawing on affordance-actualization theory and literature on AI, uniqueness theory, and healthcare, we developed a research model to explain users’ adoption intention of AI devices in the healthcare context. Data collected from 307 potential users in China were used to test the proposed model. Our findings indicated that data capture, classification, and social affordances impacted self-monitoring significantly, while data capture and social affordances were the predators of uniqueness enhancement. The self-monitoring, on the other hand, had a positive influence on uniqueness enhancement. Finally, our results revealed that self-monitoring and uniqueness enhancement have significant effects on AI adoption. From the findings, theoretical and practical implications are discussed.

From a national security viewpoint, a higher degree of cyber autonomy is crucial to reduce the reliance on external, oftentimes untrustworthy entities, in order to achieve better resilience against adversaries. To probe into the concept of government cyber autonomy, this study examines the external dependency of public-facing government websites across the world's major industrialized, Group of Seven (G7) countries. Over a two-year period, we measured HTTPS adoption rates, the autonomy status of CAs, and the autonomy status of CPs on G7 government websites. We find that approximately 85% of web resources loaded by G7 government sites originate from the United States. By reviewing policy documents and surveying technicians who maintain government websites, we identify four significant forces that can influence the degree of a government's autonomy, including government mandates on HTTPS adoption, website development outsourcing, the citizens' fear of large-scale surveillance, and user confusion. Because a government websites are considered critical information infrastructures, we expect this study to raise awareness of their complex dependency, thereby reducing the risk of blindly trusting external entities when using critical government services.

By integrating the perspectives of social presence, interactivity, and peer motivation, this study developed a theoretical model to examine the factors affecting members' purchase intention in the context of social media brand community. Data collected from members of a fan page brand community on Facebook in Taiwan was used to test the model. The results also show that peer extrinsic motivation and peer intrinsic motivation have positive influences on purchase intention. The results also reveal that human-message interaction exerts significant influence on peer extrinsic motivation and peer intrinsic motivation, while human-human interaction has a positive effect on human-message interaction. Finally, the results report that awareness impacts human-message interaction significantly, whereas awareness, affective social presence, and cognitive social presence influence human-human interaction significantly.

Stents are meshed tubes used to treat narrowed or weakened arteries. It is observed that the geometrical change created by a deployed stent may influence regional characteristics and blood flow. Restenosis can occur after the stent deployment due to neointimal proliferation. In this paper a new stent design based on a regular stent was proposed to modify the wall shear stress (WSS) of existing stent design. The fluid flow inside the stent region was then compensated to approach a regular level without disturbance of a stent. Simulation results indicated the possibility to reduce the restenosis of current stent designs.

We propose the very first non-intrusive measurement methodology for quantifying the performance of commodity Virtual Reality (VR) systems. Our methodology considers the VR system under test as a black-box and works with any VR applications. Multiple performance metrics on timing and positioning accuracy are considered, and detailed testbed setup and measurement steps are presented. We also apply our methodology to several VR systems in the market, and carefully analyze the experiment results. We make several observations: (i) 3D scene complexity affects the timing accuracy the most, (ii) most VR systems implement the dead reckoning algorithm, which incurs a non-trivial correction latency after incorrect predictions, and (iii) there exists an inherent trade-off between two positioning accuracy metrics: precision and sensitivity.

In order to understand the factors affecting users’ well-being perception derived from social networking sites usage, this study integrates the perspectives of social presence theory and social capital theory to develop a research model. Data were collected from 305 users of Facebook in Taiwan to test the model. The findings reveal that structural capital, relational capital, and cognitive capital exert significant influences on subjective well-being. The results also reveal that structural capital and cognitive capital have positive influence on relational capital, while structural capital influences cognitive capital significantly. The results, on the other hand, show that awareness and affective social presence are the antecedents of structural capital, whereas cognitive social presence and affective social presence influence cognitive capital significantly. Finally, affective social presence is associated with relational capital positively. Theoretical and practical implications are discussed.

Group buying is emerging as a new online buying model, in which an initiator takes the initiative and other users participate through a virtual community. A group of people who have the same need buy products online together, taking advantage of numbers to get bargains. Website stickiness, the website's ability to retain online customers and prolong the duration of each stay, is one of the key factors to e-commerce success. How to maintain members' stickiness becomes a critical issue in Information Systems (IS) study. Therefore, we aim to explore members' stickiness intention by linking justice theory and trust. The research model is based on Social cognition theory (SCT) and trust related literature. This study posits trust in marketplace, trust in initiator, and conformity as environmental influences, while collective efficacy is viewed as personal influences. We validate that member perceived distributive justice, procedure justice, and interactional justice have impact on their trust in marketplace and trust in initiator. Besides, member's perceived trust, conformity, and collective efficacy have influence on their stickiness intention.

Although perceived value has long been recognised as an influential means of affecting continuance intention in information systems (IS), little attention has been devoted to explore its antecedents, which constitutes an important research issue. This study, building on prior literature on continued usage in IS as well as the resource-based view, proposed a model to address this gap. Our model suggests that perceived value, a major driving force for members’ satisfaction and continuance intention, is affected by four kinds of resources embedded in virtual communities, which are relationship resources, technology infrastructure, knowledge resources and human resources. To provide additional insights on the pivotal role of perceived value, we also postulate that experience moderates the link between perceived value and its antecedents. Data collected from 235 members of a professional virtual community provide strong support for the research model. It was found that perceived value exerts a great effect on both satisfaction and continuance intention. In addition, when different levels of experience are taken into consideration, relationship resources, knowledge resources and human resources were found to be more salient to high-experience members, whereas technology infrastructure was found to be more salient to low-experience members. Finally, this study discusses the implications of these findings and offers direction for future research.

A framework to adjust sitting postures based on musculoskeletal models is presented in this paper. After the analysis of muscles and strength, some "comfortable" postures are specified as candidate postures. An optimization algorithm is then applied to choose from these candidates, according to current conditions of muscles and the durations one needs, and form a sequence of ordered postures for the sitters. The sitters shift the postures dynamically before they feel uncomfortable, therefore they can maintain a seated posture for extended durations. The ergonomists can also benefit from the information of intermittent postures and use it as a guideline for a new chair/desk design.

Although previous research has established that interpersonal trust and system trust are critical in shaping individual behaviour in virtual settings, the two perspectives have not been examined by IS researchers in virtual communities (VCs) simultaneously. Drawing from prior literature on trust and VCs, a research model for understanding the importance of trust in members and trust in system and their antecedents in VCs is presented. Six trust-building factors were identified using three trust-building bases (calculative base, relationship base and technology base). Data were collected from 324 members of a technical VC to test the model. The study shows that trust in members and trust in system have significant influences on knowledge sharing intention. The study also indicates that knowledge growth, perceived responsiveness and shared vision affect trust in members positively, while knowledge quality influences trust in system significantly. The study discusses the theoretical and managerial implications of this study and proposes several future research directions.

None of the recently reported voltage-mode versatile filters is digitally programmable like the application of easy addition of current-mode filtering signals to do the synthesis. And none of the recently reported analytical synthesis methods is used for the realization of a multifunction filter. Both the above two induced motivations of researches have been merged, developed, and solved in this brief on the voltage-mode OTA-C universal biquad filter which still achieves many advantages like the employments of all single-ended-input OTAs, all grounded capacitors, and the least number of active transistor and passive component counts in addition to have an controllable gain, a cascadable input terminal, and no need to impose component matchings.

A current-mode "even"-nth-order OTA-C elliptic filter with the minimum components is presented in this paper. The filter, designed by the analytical synthesis method, is the counterpart of the recently reported current-mode "odd"-nth-order filter and features both current-mode signals and the important criteria of single ended input OTAs and grounded capacitors. Only (3n)/2 single-value and single-ended-input OTAs and n grounded capacitors, the minimum active and passive components, are used for the realization of this even-nth-order elliptic filter which is able to realize both highpass and lowpass transfer functions. The odd-nth-order filter is not applicable to highpass filtering. An equal-capacitance-type structure is designed taking account of the difficulty of precisely fabricating capacitances in integrated circuits. For the structure realized in this paper, simultaneously satisfying three criteria reduces the total inner parasitics to the minimum and produces more precise output response. H-spice highpass and lowpass simulations with 0.35/spl mu/m process are shown for the proof of the theory.

Mammograms depict most of the significant changes in breast disease. The primary radiographic signs of cancer are related to tumor mass, density, size, borders, and shape, and local distribution of calcifications. We show that each of these features can be well described by coherence and orientation measures and provide visual cues for radiologists to identify possible lesions more easily without increasing false positives. In this paper, an artifact-free enhancement algorithm based on overcomplete multiscale representations is presented. First, an image was decomposed using a fast wavelet transform algorithm. At each level of analysis, energy and phase information are computed via a set of separable steerable filters. Then, a measure of coherence within each level was obtained by weighting an energy measure with the ratio of projections of local energy within a specified window. Each projection was computed onto the central point of a window with respect to the total energy within that window. Finally, a nonlinear operation, integrating coherence and orientation information, was applied to modify transform coefficients within distinct levels of analysis. These modified coefficients were then reconstructed, via an inverse fast wavelet transform, resulting in an improved visualization of significant mammographic features. The novelty of this algorithm lies in the detection of directional multiscale features and the removal of aliased perturbations. Compared to existing multiscale enhancement techniques, images processed with this method appeared more familiar to radiologists due to localized enhancement of features.

Mammograms can depict most of the significant changes of breast disease. The primary radiographic signs of cancer are masses (its density, size, shape, borders), spicular lesions and calcification content. These features may be extracted according to their coherence and orientation and can provide important visual cues for radiologists to locate suspicious areas without generating false positives. An artifact free enhancement algorithm based on overcomplete multiscale wavelet analysis is presented. The novelty of this algorithm lies in its detection of directional features and removal of unwanted perturbations. Compared to existing multiscale enhancement approaches, images processed with this method appear more familiar to radiologists and naturally close to the original mammogram.

Feature extraction remains an important part of low-level vision. Traditional oriented filters have been effective tools to identify features, such as lines and edges. Steerable filters, which can be adjusted at arbitrary orientation, have made decisions of feature orientations more precise. Combined with a pyramid structure of a multiscale representation, these filters can provide a reliable and efficient tool for image analysis. This paper takes advantage of multiscale steerable filters in the context of de-noising. First a set of novel filters are designed, that decompose the frequency plane into distinct directional bands. Next, we identify the dominant direction and strength at each point of an image from quadrature pairs of steerable filters. A nonlinear threshold function is then applied to the filtered coefficients to suppress noise. The denoised image is restored from coefficients modified at each level of transform space. We demonstrate the benefits of multiscale steerable filters for de-noising and show that it can greatly reduce noise while preserving image features. Two examples are presented to verify the efficacy of the technique.