The Impact and Evolution of Individual’s Learning: An Empirical Study in Open Innovation Community

Learning is critical for individuals to increase their performance. However, this benefit of learning is not always realized. Previous studies have distinguished different classifications of learning approaches and reached inconsistent results. Therefore, this study further refines the classification of learning approaches in an open innovation community and explore the individual’s learning curve from a dynamic perspective. Specifically, we focus on whether and under what conditions learning can increase individual’s performance, and how individual\u27s learning curve changes over the tenure. To examine our hypotheses, we collect a dataset includes 48,820 game mods developed by 6,141 creators spanning 7-years from an open game innovation community. The results not only show the significant curve relationship between the four learning approaches and performance, but also demonstrate individual’s learning curve evolves over the tenure. This paper provides valuable suggestions and implications for individuals to choose appropriate learning approaches and obtain better performance under different tenures

List-avoiding orientations

Given a graph $G$ with a set $F(v)$ of forbidden values at each $v \in V(G)$, an $F$-avoiding orientation of $G$ is an orientation in which $deg^+(v) \not \in F(v)$ for each vertex $v$. Akbari, Dalirrooyfard, Ehsani, Ozeki, and Sherkati conjectured that if $|F(v)| < \frac{1}{2} deg(v)$ for each $v \in V(G)$, then $G$ has an $F$-avoiding orientation, and they showed that this statement is true when $\frac{1}{2}$ is replaced by $\frac{1}{4}$. In this paper, we take a step toward this conjecture by proving that if $|F(v)| < \lfloor \frac{1}{3} deg(v) \rfloor$ for each vertex $v$, then $G$ has an $F$-avoiding orientation. Furthermore, we show that if the maximum degree of $G$ is subexponential in terms of the minimum degree, then this coefficient of $\frac{1}{3}$ can be increased to $\sqrt{2} - 1 - o(1) \approx 0.414$. Our main tool is a new sufficient condition for the existence of an $F$-avoiding orientation based on the Combinatorial Nullstellensatz of Alon and Tarsi

Mrdbscan: An efficient parallel density-based clustering algorithm using mapreduce

Abstract-Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scaleup of our work are very efficient

Gradient differences of immunotherapy efficacy in metastatic melanoma related to sunlight exposure pattern: A population-based study

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized metastatic melanoma (MM) treatment in just a few years. Ultraviolet (UV) in sunlight is the most significant environmental cause of melanoma, which is considered to be the main reason for tumor mutation burden (TMB) increase in melanoma. High TMB usually predicts that PD-1 inhibitors are effective. The sunlight exposure pattern of MM might be a clinical feature that matches TMB. The relationship between sunlight exposure patterns and immunotherapy response in MM is unclear. This study aims to investigate the correlation between sunlight exposure patterns and immunotherapy response in MM and establish nomograms that predict 3- and 5-year overall survival (OS) rate.MethodsWe searched the Surveillance, Epidemiology, and End Results (SEER) database and enrolled MM cases from 2005-2016. According to the advent of ICIs in 2011, the era was divided into the non-ICIs era (2005-2010) and the ICIs era (2011-2016). Patients were divided into three cohorts according to the primary site sunlight exposure patterns: head and neck in the first cohort, trunk arms and legs in the second cohort, and acral sites in the third cohort. We compared survival differences for each cohort between the two eras, performed stratified analysis, established nomograms for predicting 3- and 5-year OS rate, and performed internal validation.ResultsComparing the survival difference between the ICIs and non-ICIs era, head and neck melanoma showed the greatest improvement in survival, with 3- and 5-year OS rate increasing by 10.2% and 9.1%, respectively (P=0.00011). In trunk arms and legs melanoma, the 3- and 5-year OS rate increased by 4.6% and 3.9%, respectively (P&lt;0.0001). There is no improvement in survival in acral melanoma (AM) between the two eras (P=0.78). The receiver operating characteristic (ROC) curve, area under the ROC curve (AUC) and calibration graphs show good discrimination and accuracy of nomograms. Decision curve analysis (DCA) suggests good clinical utility of nomograms.ConclusionsBased on the classification of sunlight exposure patterns, there is a gradient difference in immunotherapy efficacy for MM. The degree of sunlight exposure is positively correlated with immunotherapy response. The nomograms are sufficiently accurate to predict 3- and 5-year OS rate for MM, allowing for individualized clinical decisions for future clinical work

Examining Gifting on Social Live Streaming Services: An Identity Investment Perspective

Social live streaming services (SLSS) is becoming popular worldwide for its real-time interaction and successful commercial model. Therefore, it is important to understand user’s gifting behavior on SLSS. Yet it is not explored clearly. From an identity investment perspective, this research studies how class identity and relational identity would impact user’s gifting choices (i.e., consuming amount and number). Based on the identity signaling theory, this study also explores the moderating role of social density elicited by real-time comments. The theoretical framework is tested on 232, 416 individual data collected on Douyu platform. This research contributes not only to the online gifting review literature by revealing different effects of user’s multiple identities on gifting behavior, but also to identity signaling theory by identifying the moderating consequences of social density as rooted in social environments, which provides valuable implications on how to promote sales of virtual gifts on SLSS

Application of 3D bio-printing for skin repair

The skin is the largest organ of the human body, and its structural integrity plays a crucial role in the maintenance of the normal physiological function of the human body. The repair of large skin trauma and the healing of chronic skin ulcers are clinical problems to be solved. Although great progress has been made in the field of skin tissue engineering over the last decades, it is still a challenge to develop the engineered skin by incorporating with skin appendages, such as hair follicles, sweat glands, sebaceous glands and vascularization. However, 3D bio-printing technology provides a potential to solve these problems. In this review, we briefly discuss the application, prospective, and challenges of 3D bio-printing technology in skin wound repair

Land Use Classification of High-Resolution Multispectral Satellite Images With Fine-Grained Multiscale Networks and Superpixel Postprocessing

Land use recognition from multispectral satellite images is fundamentally critical for geological applications, but the results are not satisfied. The scale dimension of current multiscale learning is too coarse to account for rich scales in multispectral images, and pixel-wise classification tends to produce &#x201C;salt-and-pepper&#x201D; labels due to possible misclassification in heterogeneous regions. In this article, these issues are addressed by proposing a new pixel-wise classification model with finer scales for convolutional neural networks. The model is designed to extract multiscale contextual information using multiscale networks at a fine-grained level, addressing the issue of insufficient multiscale learning for classification. Furthermore, a small-scale segmentation-combination method is introduced as a postprocessing solution to smooth fragmented classification results. The proposed method is tested on GF-1, GF-2, DEIMOS-2, GeoEye-1, and Sentinel-2 satellite images, and compared with six neural-network-based algorithms. The results demonstrate the effectiveness of the proposed model in finding objects of large scale difference, improving classification accuracy, and reducing classified fragments. The discussion also illustrates that convolutional neural networks and pixel-wise inference are more practical than transformer and patch-wise recognition

Injectable Electroactive Hydrogels Formed via Host–Guest Interactions

Injectable conducting hydrogels (ICHs) are promising conductive materials in biomedicine and bioengineering fields. However, the synthesis of ICHs in previous work involved chemical cross-linking, and this may result in biocompatibility problems of the hydrogels. We present the successful synthesis of ICHs via noncovalent host–guest interactions, avoiding the side effect of covalent chemical cross-linking. The ICHs are based on the γ-cyclodextrin dimer as the host molecule and tetraaniline and poly­(ethylene glycol) as the guests in a synthetic well-defined hydrophilic copolymer. The sol–gel transition mechanism of the in situ hydrogel is thoroughly investigated. This novel synthesis approach of ICHs via supramolecular chemistry will lead to various new biomedical applications for conducting polymers

Nanofiber Yarn/Hydrogel Core–Shell Scaffolds Mimicking Native Skeletal Muscle Tissue for Guiding 3D Myoblast Alignment, Elongation, and Differentiation

Designing scaffolds that can mimic native skeletal muscle tissue and induce 3D cellular alignment and elongated myotube formation remains an ongoing challenge for skeletal muscle tissue engineering. Herein, we present a simple technique to generate core–shell composite scaffolds for mimicking native skeletal muscle structure, which comprise the aligned nanofiber yarn (NFY) core and the photocurable hydrogel shell. The aligned NFYs are prepared by the hybrid composition including poly(caprolactone), silk fibroin, and polyaniline <i>via</i> a developed dry–wet electrospinning method. A series of core–shell column and sheet composite scaffolds are ultimately obtained by encapsulating a piece and layers of aligned NFY cores within the hydrogel shell after photo-cross-linking. C2C12 myoblasts are seeded within the core–shell scaffolds, and the good biocompatibility of these scaffolds and their ability to induce 3D cellular alignment and elongation are successfully demonstrated. Furthermore, the 3D elongated myotube formation within core–shell scaffolds is also performed after long-term cultivation. These data suggest that these core–shell scaffolds combine the aligned NFY core that guides the myoblast alignment and differentiation and the hydrogel shell that provides a suitable 3D environment for nutrition exchange and mechanical protection to perform a great practical application for skeletal muscle regeneration

Interwoven Aligned Conductive Nanofiber Yarn/Hydrogel Composite Scaffolds for Engineered 3D Cardiac Anisotropy

Mimicking the anisotropic cardiac structure and guiding 3D cellular orientation play a critical role in designing scaffolds for cardiac tissue regeneration. Significant advances have been achieved to control cellular alignment and elongation, but it remains an ongoing challenge for engineering 3D cardiac anisotropy using these approaches. Here, we present a 3D hybrid scaffold based on aligned conductive nanofiber yarns network (NFYs-NET, composition: polycaprolactone, silk fibroin, and carbon nanotubes) within a hydrogel shell for mimicking the native cardiac tissue structure, and further demonstrate their great potential for engineering 3D cardiac anisotropy for cardiac tissue engineering. The NFYs-NET structures are shown to control cellular orientation and enhance cardiomyocytes (CMs) maturation. 3D hybrid scaffolds were then fabricated by encapsulating NFYs-NET layers within hydrogel shell, and these 3D scaffolds performed the ability to promote aligned and elongated CMs maturation on each layer and individually control cellular orientation on different layers in a 3D environment. Furthermore, endothelialized myocardium was constructed by using this hybrid strategy via the coculture of CMs on NFYs-NET layer and endothelial cells within hydrogel shell. Therefore, these 3D hybrid scaffolds, containing NFYs-NET layer inducing cellular orientation, maturation, and anisotropy and hydrogel shell providing a suitable 3D environment for endothelialization, has great potential in engineering 3D cardiac anisotropy