Acceptance of Wearable Technology: A Meta-Analysis

Knowing what factors drive wearable technology adoption can help companies succeed in the competitive market of wearables. In this study, we conduct a meta-analysis on the relationships of technology acceptance of wearable technology based on the extant corpus (142 effect sizes from 44 samples collected in 11 countries). The results confirm the basic expectation that the core constructs of technology acceptance models as well as reveal that perceived enjoyment and usefulness are the most important to the adoption of wearables. However, more interestingly, a granular analysis of moderating effects shows that cultural factors including uncertainty avoidance, future orientation and humane orientation can significantly moderate the relationships between different determinants and wearable adoption. In addition, compared with other types of smart wearables, the users of smartwatches would place more weight on perceived self-expressiveness. These findings offer insights for future wearables-related research and also have practical implications for designing and developing successful wearable products

A Meta-Analysis of Brand Extension Success:The Effects of Parent Brand Equity and Extension Fit

Given the high failure rates of brand extensions, insights into the drivers of brand extension success are critical for marketing practitioners and scholars. Prior research has inferred that parent brand equity and extension fit are the two key success drivers; however, empirical findings are mixed. Drawing on signaling theory, categorization theory, and a large database of 2,134 effect sizes from research spanning 1990–2020, the authors address these mixed findings through a meta-analysis to develop empirical generalizations. The results show that parent brand equity and extension fit positively influence extension success. However, the multifaceted dimensions of these two drivers have differential effects. For example, among the fit dimensions, usage fit has the weakest effect. While the results suggest an overall positive interaction effect between the two drivers, a fine-grained perspective that considers the drivers’ various dimensions reveals differences. For example, brand familiarity appears to have a lower interaction effect with extension fit than the other dimensions of parent brand equity. Furthermore, the authors provide a comprehensive analysis of five groups of moderators: contextual factors (parent brand, extension, communication, and consumer factors) and research method factors. The authors offer managerial and future research implications for the design of brand extension strategies.</p

IBVC: Interpolation-driven B-frame Video Compression

Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-driven B-frame Video Compression (IBVC). Our approach only involves two major operations: video frame interpolation and artifact reduction compression. IBVC introduces a bit-rate free MEMC based on interpolation, which avoids optical-flow quantization and additional compression distortions. Later, to reduce duplicate bit-rate consumption and focus on unaligned artifacts, a residual guided masking encoder is deployed to adaptively select the meaningful contexts with interpolated multi-scale dependencies. In addition, a conditional spatio-temporal decoder is proposed to eliminate location errors and artifacts instead of using MEMC coding in other methods. The experimental results on B-frame coding demonstrate that IBVC has significant improvements compared to the relevant state-of-the-art methods. Meanwhile, our approach can save bit rates compared with the random access (RA) configuration of H.266 (VTM). The code will be available at https://github.com/ruhig6/IBVC.Comment: Submitted to IEEE TCSV

JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation

Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references. Most existing works utilize spatio-temporal semantic information extractor to realize motion estimation and interpolation modeling. However, they insufficiently consider the real mechanistic rationality of generated middle motions. In this paper, we reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to model the complicated motions of inter-frame. Specifically, the motion trajectory between the target frame and the multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. ConvLSTM is adopted to construct this joint distribution of complete motions in temporal dimension. Moreover, the feature learning network is designed to optimize for the joint regression modeling. A coarse-to-fine synthesis enhancement module is also conducted to learn visual dynamics at different resolutions through repetitive regression and interpolation. Experimental results on VFI show that the effectiveness and significant improvement of joint motion regression compared with the state-of-the-art methods. The code is available at https://github.com/ruhig6/JNMR.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Effective noninvasive zygosity determination by maternal plasma target region sequencing

Background: Currently very few noninvasive molecular genetic approaches are available to determine zygosity for twin pregnancies in clinical laboratories. This study aimed to develop a novel method to determine zygosity by using maternal plasma target region sequencing. Methods: We constructed a statistic model to calculate the possibility of each zygosity type using likelihood ratios (Li) and empirical dynamic thresholds targeting at 4,524 single nucleotide polymorphisms (SNPs) loci on 22 autosomes. Then two dizygotic (DZ) twin pregnancies, two monozygotic (MZ) twin pregnancies and two singletons were recruited to evaluate the performance of our novel method. Finally we estimated the sensitivity and specificity of the model in silico under different cell-free fetal DNA (cff-DNA) concentration and sequence depth. Results/Conclusions: We obtained 8.90 Gbp sequencing data on average for six clinical samples. Two samples were classified as DZ with L values of 1.891 and 1.554, higher than the dynamic DZ cut-off values of 1.162 and 1.172, respectively. Another two samples were judged as MZ with 0.763 and 0.784 of L values, lower than the MZ cut-off values of 0.903 and 0.918. And the rest two singleton samples were regarded as MZ twins, with L values of 0.639 and 0.757, lower than the MZ cut-off values of 0.921 and 0.799. In silico, the estimated sensitivity of our noninvasive zygosity determination was 99.90% under 10% total cff-DNA concentration with 2 Gbp sequence data. As the cff-DNA concentration increased to 15%, the specificity was as high as 97% with 3.50 Gbp sequence data, much higher than 80% with 10% cff-DNA concentration. Significance: This study presents the feasibility to noninvasively determine zygosity of twin pregnancy using target region sequencing, and illustrates the sensitivity and specificity under various detecting condition. Our method can act as an alternative approach for zygosity determination of twin pregnancies in clinical practice.Multidisciplinary SciencesSCI(E)2ARTICLE6null

Determinants and cross-national moderators of wearable health tracker adoption: A meta-analysis

Wearable health trackers improve people’s health management and thus are beneficial for social sustainability. Many prior studies have contributed to the knowledge on the determinants of wearable health tracker adoption. However, these studies vary remarkably in focal determinants and countries of data collection, leading to a call for a structured and quantitative review on what determinants are generally important, and whether and how their effects on adoption vary across countries. Therefore, this study performed the first meta-analysis on the determinants and cross-national moderators of wearable health tracker adoption. This meta-analysis accumulated 319 correlations between nine determinants and adoption from 59 prior studies in 18 countries/areas. The meta-analytic average effects of the determinants revealed the generalized effect and the relative importance of each determinant. For example, technological characteristics generally had stronger positive correlations with adoption than consumer characteristics, except for privacy risk. Second, drawing on institutional theory, it was observed that cross-national characteristics regarding socioeconomic status, regulative systems, and cultures could moderate the effects of the determinants on adoption. For instance, the growth rate of gross domestic product decreased the effect of innovativeness on adoption, while regulatory quality and control of corruption could increase this effect

Ultrahigh Sensitivity Mach−Zehnder Interferometer Sensor Based on a Weak One-Dimensional Field Confinement Silica Waveguide

We report a novel Mach−Zehnder interferometer (MZI) sensor that utilizes a weak one-dimensional field confinement silica waveguide (WCSW). The WCSW has a large horizontal and vertical aspect ratio and low refractive index difference, which features easy preparation and a large evanescent field for achieving high waveguide sensitivity. We experimentally achieved WCSW ultrahigh waveguide sensitivity of 0.94, MZI sensitivity of 44,364 π/RIU and a low limit of detection (LOD) of 6.1 × 10−7 RIU