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

Construction of an interferon regulatory factors-related risk model for predicting prognosis, immune microenvironment and immunotherapy in clear cell renal cell carcinoma

BackgroundInterferon regulatory factors (IRFs) played complex and essential roles in progression, prognosis, and immune microenvironment in clear cell renal cell carcinoma (ccRCC). The purpose of this study was to construct a novel IRFs-related risk model to predict prognosis, tumor microenvironment (TME) and immunotherapy response in ccRCC.MethodsMulti-omics analysis of IRFs in ccRCC was performed based on bulk RNA sequencing and single cell RNA sequencing data. According to the expression profiles of IRFs, the ccRCC samples were clustered by non-negative matrix factorization (NMF) algorithm. Then, least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were applied to construct a risk model to predict prognosis, immune cells infiltration, immunotherapy response and targeted drug sensitivity in ccRCC. Furthermore, a nomogram comprising the risk model and clinical characteristics was established.ResultsTwo molecular subtypes with different prognosis, clinical characteristics and infiltration levels of immune cells were identified in ccRCC. The IRFs-related risk model was developed as an independent prognostic indicator in the TCGA-KIRC cohort and validated in the E-MTAB-1980 cohort. The overall survival of patients in the low-risk group was better than that in the high-risk group. The risk model was superior to clinical characteristics and the ClearCode34 model in predicting the prognosis. In addition, a nomogram was developed to improve the clinical utility of the risk model. Moreover, the high-risk group had higher infiltration levels of CD8+ T cell, macrophages, T follicular helper cells and T helper (Th1) cells and activity score of type I IFN response but lower infiltration levels of mast cells and activity score of type II IFN response. Cancer immunity cycle showed that the immune activity score of most steps was remarkably higher in the high-risk group. TIDE scores indicated that patients in the low-risk group were more likely responsive to immunotherapy. Patients in different risk groups showed diverse drug sensitivity to axitinib, sorafenib, gefitinib, erlotinib, dasatinib and rapamycin.ConclusionsIn brief, a robust and effective risk model was developed to predict prognosis, TME characteristics and responses to immunotherapy and targeted drugs in ccRCC, which might provide new insights into personalized and precise therapeutic strategies

Who are the convoys of the happiness of Chinese urban residents? Research on social relations and subjective well-being based on the convoy model

IntroductionWhile the rapid advancement of urbanization has driven the improvement of material living standards, it has also brought about rapid social changes and intensified competition. In this “involutive” environment characterized by highly competitive and strong pressure, urban residents tend to fall into a state of “mental exhaustion.” Anxiety, depression, sleep disorders, and other mental illnesses have seriously threatened public health in Chinese cities. Support from social relations is crucial for enhancing residents’ subjective well-being (SWB) and promoting their mental health, especially in China’s highly contextualized collectivist culture.MethodsAccording to the social structure of China’s “difference sequence pattern,” this paper constructs a theoretical framework of the relationship between social relations and SWB based on the convoy model and uses CGSS2018 data to verify the applicability of the theoretical framework.ResultsKinship and friendship positively relate to SWB, and their interaction effect is significantly negative. There is no necessary correlation between neighborhood and SWB. The relationship between social relations and SWB of different age groups is heterogeneous. In addition, the moderating effects of relative income and social class are significantly negative.DiscussionKinship and friendship are Chinese urban residents’ SWB convoys, and these two factors have an obvious substitution effect. The neighborhood has withdrawn from the convoy orbit of Chinese urban residents’ SWB, which may be related to neighborhood indifference caused by China’s housing system reform. From the life course perspective, the SWB convoys of young and middle-aged groups consist of kinship and friendship, while those of elderly people include kinship and neighborhood. In addition, for poor individuals living at the bottom of society, support from kinship is the most important source of social capital. These findings provide new insights into the relationship between social relations and the welfare of Chinese urban residents

A gripper-like exoskeleton design for robot grasping demonstration

Learning from demonstration (LfD) is a practical method for transferring skill knowledge from a human demonstrator to a robot. Several studies have shown the effectiveness of LfD in robotic grasping tasks to improve the success rate of grasping and to accelerate the development of new robotic grasping tasks. A well-designed demonstration device can effectively represent human grasping motion to transfer grasping skills to robots. In this paper, an improved gripper-like exoskeleton with a data collection system is proposed. First, we present the mechatronic details of the exoskeleton and its motion-tracking system, considering the manipulation flexibility and data acquisition requirements. We then present the capabilities of the device and its data collection system, which collects the position, pose and displacement of the gripper on the exoskeleton. The collected data is further processed by the data acquisition and processing software. Next, we describe the principles of Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) in robot skill learning, which are used to transfer the raw data from demonstrations to robot motions. In the experiment, an optimized trajectory was learned from multiple demonstrations and reproduced on a robot. The results show that the GMR complemented with GMM is able to learn a smooth trajectory from demonstration trajectories with noise

Fractal assessment analysis of China's air-HSR network integration

High-speed rail (HSR) has emerged as a significant mode for intercity transport in several countries, particularly China, setting an environment that may promote integration between air and HSR networks. To better measure the current level of integration of China's air-HSR intermodal network and identify implementation issues, this paper establishes a novel assessment framework that considers three primary areas: service capability, network connectivity and transfer potential. The framework is based on a comprehensive literature review of network measurement and assessment methodologies. Then, fractal theory is used to establish an assessment model that associates the fractal dimension to the level of intermodal integration, which can serve as an important complement to traditional weighting methods. The model and framework are applied to the 10 cities in China with the potential for air-HSR integration. The results show that international hub airports, together with their closest HSR station, do not necessarily perform at a higher integration level than regional hubs. The paper also proposes policy and practical recommendations to enhance air-HSR network integration levels from service supply, network coordination and transfer design perspectives

Serum lactate dehydrogenase activities as systems biomarkers for 48 types of human diseases

Most human diseases are systems diseases, and systems biomarkers are better fitted for diagnostic, prognostic, and treatment monitoring purposes. To search for systems biomarker candidates, lactate dehydrogenase (LDH), a housekeeping protein expressed in all living cells, was investigated. To this end, we analyzed the serum LDH activities from 172,933 patients with 48 clinically defined diseases and 9528 healthy individuals. Based on the median values, we found that 46 out of 48 diseases, leading by acute myocardial infarction, had significantly increased (p  0.8) for hepatic encephalopathy and lung fibrosis

Jump-Start Reinforcement Learning

Reinforcement learning (RL) provides a theoretical framework for continuously improving an agent's behavior via trial and error. However, efficiently learning policies from scratch can be very difficult, particularly for tasks with exploration challenges. In such settings, it might be desirable to initialize RL with an existing policy, offline data, or demonstrations. However, naively performing such initialization in RL often works poorly, especially for value-based methods. In this paper, we present a meta algorithm that can use offline data, demonstrations, or a pre-existing policy to initialize an RL policy, and is compatible with any RL approach. In particular, we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs two policies to solve tasks: a guide-policy, and an exploration-policy. By using the guide-policy to form a curriculum of starting states for the exploration-policy, we are able to efficiently improve performance on a set of simulated robotic tasks. We show via experiments that JSRL is able to significantly outperform existing imitation and reinforcement learning algorithms, particularly in the small-data regime. In addition, we provide an upper bound on the sample complexity of JSRL and show that with the help of a guide-policy, one can improve the sample complexity for non-optimism exploration methods from exponential in horizon to polynomial.Comment: 20 pages, 10 figure

Investigations into the effects of scaffold microstructure on slow-release system with bioactive factors for bone repair

In recent years, bone tissue engineering (BTE) has played an essential role in the repair of bone tissue defects. Although bioactive factors as one component of BTE have great potential to effectively promote cell differentiation and bone regeneration, they are usually not used alone due to their short effective half-lives, high concentrations, etc. The release rate of bioactive factors could be controlled by loading them into scaffolds, and the scaffold microstructure has been shown to significantly influence release rates of bioactive factors. Therefore, this review attempted to investigate how the scaffold microstructure affected the release rate of bioactive factors, in which the variables included pore size, pore shape and porosity. The loading nature and the releasing mechanism of bioactive factors were also summarized. The main conclusions were achieved as follows: i) The pore shapes in the scaffold may have had no apparent effect on the release of bioactive factors but significantly affected mechanical properties of the scaffolds; ii) The pore size of about 400 μm in the scaffold may be more conducive to controlling the release of bioactive factors to promote bone formation; iii) The porosity of scaffolds may be positively correlated with the release rate, and the porosity of 70%–80% may be better to control the release rate. This review indicates that a slow-release system with proper scaffold microstructure control could be a tremendous inspiration for developing new treatment strategies for bone disease. It is anticipated to eventually be developed into clinical applications to tackle treatment-related issues effectively