Sleep/wake-up scheduling is one of the fundamental problems in wireless sensor networks, since the energy of sensor nodes is limited and they are usually unrechargeable. The purpose of sleep/wake-up ...scheduling is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this paper, a self-adaptive sleep/wake-up scheduling approach is proposed. Unlike most existing studies that use the duty cycling technique, which incurs a tradeoff between packet delivery delay and energy saving, the proposed approach, which does not us duty cycling, avoids such a tradeoff. The proposed approach, based on the reinforcement learning technique, enables each node to autonomously decide its own operation mode (sleep, listen, or transmission) in each time slot in a decentralized manner. Simulation results demonstrate the good performance of the proposed approach in various circumstances.
Interstitial lung disease (ILD) is frequent in patients with rheumatoid arthritis (RA) and is a potentially life-threatening complication with significant morbidity and mortality. This meta-analysis ...aims to systematically determine the factors associated with the development of rheumatoid arthritis-related interstitial lung disease (RA-ILD).
All primary studies which reported the factors associated with of RA-ILD were eligible for the review except case reports. The Cochrane Library, PubMed, Embase, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), and WANFANG electronic databases were searched through to December 30, 2022, for studies investigating the factors associated with RA-ILD. The methodological quality assessment of the eligible studies was performed using the Newcastle-Ottawa Scale (NOS). 2 reviewers extracted relevant data independently. Then, weighed mean differences (WMDs) or pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were obtained for the relationships between the factors and RA-ILD. The statistical meta-analysis, subgroup and sensitivity analyses were performed using the Review Manager 5.3, and publication bias with Egger's test were performed using the Stata12.0 software.
A total of 22 articles were screened for a meta-analysis which involved 1887 RA-ILD patients and 8066 RA without ILD patients. Some identified factors that were associated with an increased risk of RA-ILD included male sex (OR = 1.92, 95% CI: 1.54-2.39; P < 0.00001), older age (WMD = 5.77 years, 95% CI: 3.50-8.04; P < 0.00001), longer duration of RA (WMD = 0.80 years, 95% CI 0.12-1.47; P = 0.02), older age at onset of RA (WMD = 6.41 years, 95% CI: 3.17-9.64; P = 0.0001), smoking (OR = 1.69, 95% CI: 1.30-2.18; P < 0.0001). Five factors of laboratory items associated with the development of RA-ILD were evaluated in the meta-analysis. Compared with RA without ILD patients, positive rheumatoid factor (RF) (OR = 1.72, 95% CI: 1.47-2.01; P < 0.00001) and positive anti-citrullinated protein antibodies (ACPA) (OR = 1.58, 95% CI: 1.31-1.90; P < 0.00001) increased the risk of RA-ILD. Meanwhile, RF titer (WMD = 183.62 (IU/mL), 95% CI: 66.94-300.30; P = 0.002) and ACPA titer (WMD = 194.18 (IU/mL), 95% CI: 115.89-272.47; P < 0.00001) were significantly associated with increased risk of RA-ILD. Elevated erythrocyte sedimentation rate (ESR) (WMD = 7.41 (mm/h), 95% CI: 2.21-12.61; P = 0.005) and C-reactive protein (CRP) (WMD = 4.98 (mg/L), 95% CI: 0.76-9.20; P = 0.02) were also significantly associated with the development of the RA-ILD, whereas antinuclear antibody (ANA) positive status was not significantly associated with increased risk of RA-ILD (OR = 1.27, 95% CI: 1.00-1.60; P = 0.05).
This meta-analysis showed that male gender, older age, longer duration of RA, older age at onset of RA, smoking, positive RF, positive ACPA, elevated RF titer, elevated ACPA titer, higher ESR and higher CRP were associated with RA-ILD.
Manuscript Type
Empirical
Research Question/Issue
What is the impact of entrepreneurship on GDP/capita, unemployment, exports/GDP, and patents per population across countries? Is the impact of ...entrepreneurship mitigated by legal and cultural differences across countries? Do different international datasets provide different answers to these questions? We empirically compare the impact of entrepreneurship on GDP/capita, unemployment, exports/GDP, and patents per population across countries by examining three datasets from the World Bank, the OECD, and Compendia.
Research Findings/Insights
Based on a comprehensive sample of all available countries and years, with the World Bank data being the most comprehensive, we find entrepreneurship has a significantly positive impact on GDP/capita, exports/GDP, and patents per population, and a negative impact on unemployment. Inferences from the Compendia data are very consistent. By contrast, inferences from the OECD data are not supportive of any of these propositions.
Theoretical/Academic Implications
Our findings point to institutional and cultural impediments to the effectiveness of entrepreneurship. Most notably, the impact of entrepreneurship is significantly mitigated by excessively strong creditor rights that limit entrepreneurial risk‐taking. Furthermore, the data indicate that cultural attitudes associated with low risk‐taking limit the effectiveness of entrepreneurship. By contrast, the impact of entrepreneurship on exports/GDP does not appear to be directly tied to costs of exporting, which is perhaps best explained by the new economy goods and services created by entrepreneurs that depend less on such costs. For some subsets of the data we find evidence consistent with the view that top tier venture capital funds enhance the impact of entrepreneurship on GDP/capita. Finally, our results show how different definitions of new business entry matter for empirical analysis of entrepreneurship across countries.
Practitioner/Policy Implications
The data highlight the importance of access to finance without downside costs so that entrepreneurs are encouraged to take risk. Further, the data highlight institutional differences in risk attitudes that more generally inhibit risk‐taking and thereby limit the effectiveness of entrepreneurship. Moreover, the data highlight a central role for careful measurement of entrepreneurial activities and for inclusion of as many countries and years as possible in order to effectively analyze the impact of entrepreneurship.
Autonomous driving is one of the most important AI applications and has attracted extensive interest in recent years. A large number of studies have successfully applied reinforcement learning ...techniques in various aspects of autonomous driving, ranging from low-level control of driving maneuvers to higher level of strategic decision-making. However, comparatively less progress has been made in investigating how co-existing autonomous vehicles would interact with each other in a common environment and how reinforcement learning can be helpful in such situations by applying multiagent reinforcement learning techniques in the high-level strategic decision-making of the following or overtaking for a group of autonomous vehicles in highway scenarios. Learning to achieve coordination among vehicles in such situations is challenging due to the unique feature of vehicular mobility, which renders it infeasible to directly apply the existing coordinated learning approaches. To solve this problem, we propose using dynamic coordination graph to model the continuously changing topology during vehicles' interactions and come up with two basic learning approaches to coordinate the driving maneuvers for a group of vehicles. Several extension mechanisms are then presented to make these approaches workable in a more complex and realistic setting with any number of vehicles. The experimental evaluation has verified the benefits of the proposed coordinated learning approaches, compared with other approaches that learn without coordination or rely on some traditional mobility models based on some expert driving rules.
Wireless sensor networks (WSNs) have been widely investigated in recent years. One of the fundamental issues in WSNs is packet routing, because in many application domains, packets have to be routed ...from source nodes to destination nodes as soon and as energy efficiently as possible. To address this issue, a large number of routing approaches have been proposed. Although every existing routing approach has advantages, they also have some disadvantages. In this paper, a multi-agent framework is proposed that can assist existing routing approaches to improve their routing performance. This framework enables each sensor node to build a cooperative neighbour set based on past routing experience. Such cooperative neighbours, in turn, can help the sensor to effectively relay packets in the future. This framework is independent of existing routing approaches and can be used to assist many existing routing approaches. Simulation results demonstrate the good performance of this framework in terms of four metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage.
This paper surveys the literature over the last decades in the field of self-organizing multiagent systems. Self-organization has been extensively studied and applied in multiagent systems and other ...fields, e.g., sensor networks and grid systems. Self-organization mechanisms in other fields have been thoroughly surveyed. However, there has not been a survey of self-organization mechanisms developed for use in multiagent systems. In this paper, we provide a survey of existing literature on self-organization mechanisms in multiagent systems. We also highlight the future work on key research issues in multiagent systems. This paper can serve as a guide and a starting point for anyone who will conduct research on self-organization in multiagent systems. Also, this paper complements existing survey studies on self-organization in multiagent systems.
A traditional distribution network carries electricity from a central power resource to consumers, and the power dispatch is controlled centrally. Distributed generators (DGs) emerge as an ...alternative power resource to distribution networks at a smaller and distributed scale, which will bring benefits such as reduced voltage drop and loss. However, because most of high penetration DGs are not utility owned and characterized by high degree of uncertainty such as solar and wind, the distribution network may perform differently from the conventionally expected behaviors. How to dynamically and efficiently manage the power dispatch in a distribution network to balance the supply and demand by considering the variability of DGs and loads becomes a significant research issue. In this paper, a multi-agent system (MAS) was proposed to solve this problem through introducing five types of autonomous agents, the electricity management mechanisms, the agent communication ontology, and the agent cooperation strategy. The simulation of the MAS by using InterPSS, JADE and JUNE well demonstrates the performance of the system on dynamic supply and demand balance by considering both efficiency and economy.
Temozolomide (TMZ) was used for the treatment of glioblastoma (GBM) for over a decade, but its treatment benefits are limited by acquired resistance, a process that remains incompletely understood. ...Here we report that an enhancer, located between the promoters of marker of proliferation Ki67 (MKI67) and O6-methylguanine-DNA-methyltransferase (MGMT) genes, is activated in TMZ-resistant patient-derived xenograft (PDX) lines and recurrent tumor samples. Activation of the enhancer correlates with increased MGMT expression, a major known mechanism for TMZ resistance. We show that forced activation of the enhancer in cell lines with low MGMT expression results in elevated MGMT expression. Deletion of this enhancer in cell lines with high MGMT expression leads to a dramatic reduction of MGMT and a lesser extent of Ki67 expression, increased TMZ sensitivity, and impaired proliferation. Together, these studies uncover a mechanism that regulates MGMT expression, confers TMZ resistance, and potentially regulates tumor proliferation.
Microwave hyperspectral instruments represent one of the main atmospheric sounders of China’s next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral ...observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective.
Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can ...achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. In particular, the spatial version of social dilemmas is considered to study the impact of local interactions on the emergence of cooperation in the whole system. A double-layered emotional multiagent reinforcement learning framework is proposed to endow agents with internal cognitive and emotional capabilities that can drive these agents to learn cooperative behaviors. Experimental results reveal that various network topologies and agent heterogeneities have significant impacts on agent learning behaviors in the proposed framework, and under certain circumstances, high levels of cooperation can be achieved among the agents.
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