等离子喷涂NiCrAl/Cr2O3-8%TiO2涂层残余应力模拟分析

建立了模拟涂层残余应力的数学模型,对等离子喷涂不同厚度NiCrAl/Cr2O3-8%TiO2涂层的残余应力进行了模拟,模拟结果表明:由于涂层与基体的热膨胀系数不匹配等原因,在界面等区域存在严重的应力集中,涂层内部的残余应力水平,随涂层厚度增加而增加,涂层中的径向、轴向、切向应力均为压应力,径向应力是最主要的应力。     

试析清初汉军旗人的特点——兼论清初重用汉军旗人的原因

以清初的汉军旗人为研究对象 ,从民族融合的角度 ,剖析汉军旗人“大明骨 ,大清肉”的性格特点 ,阐明清初统治者重用汉军旗人的原因所在。同时 ,指出了汉军旗人———满汉融合的“混血儿” ,在清初成功地充当了满汉民族融合的”粘合剂”。     

试论中共八大的突破和缺憾——以三大历史性转变为视角

中共八大是又一次于中国革命和建设的历史转变的关头召开的具有深远历史意义和政治意义的会议。本文根据当时我国所处的三大历史性转变来评析中共八大在中国革命和建设的理论认识、路线和方针政策等方面取得的重大突破和存在的局限、缺憾。     

Assessing behavioral data science privacy issues in government artificial intelligence deployment

In today's global culture where the Internet has established itself as the main tool for communication and commerce, the capability to massively analyze and predict citizens' behavior has become a priority for governments in terms of collective intelligence and security. At the same time, in the context of novel possibilities that artificial intelligence (AI) brings to governments in terms of understanding and developing collective behavior analysis, important concerns related to citizens' privacy have emerged. In order to identify the main uses that governments make of AI and to define citizens' concerns about their privacy, in the present study, we undertook a systematic review of the literature, conducted in-depth interviews, and applied data-mining techniques. Based on our results, we classified and discussed the risks to citizens' privacy according to the types of AI strategies used by governments that may affect collective behavior and cause massive behavior modification. Our results revealed 11 uses of AI strategies used by the government to improve their interaction with citizens, organizations in cities, services provided by public institutions or the economy, among other areas. In relation to citizens' privacy when AI is used by governments, we identified 8 topics related to human behavior predictions, intelligence decision making, decision automation, digital surveillance, data privacy law and regulation, and the risk of behavior modification. The paper concludes with a discussion of the development of regulations focused on the ethical design of citizen data collection, where implications for governments are presented aimed at regulating security, ethics, and data privacy. Additionally, we propose a research agenda composed by 16 research questions to be investigated in further research.     

The role of linguistic features in science and math comprehension and performance: A systematic review and desiderata for future research

There is a growing body of research on the role of linguistic features (LF) in comprehension and performance in science and math. Of particular interest is which LF facilitate or hinder comprehension. In this systematic review, we provide an overview of findings on LF at the word, sentence and text levels, on comprehension and performance in science and math. Our literature search revealed n = 40 articles included in this review. Overall, the role of LF in comprehension and performance in science and math is complex, with findings varying across the different LF. For each LF, we discuss the findings and uncover remaining questions. In the general discussion, we uncover strengths and weaknesses of previous research and discuss open questions before making eight recommendations and identifying tasks for future research aiming to understand the complex nature of LF.     

What makes or breaks competitive research proposals? A mixed-methods analysis of research grant evaluation reports

The evaluation of grant proposals is an essential aspect of competitive research funding. Funding bodies and agencies rely in many instances on external peer reviewers for grant assessment. Most of the research available is about quantitative aspects of this assessment, and there is little evidence from qualitative studies. We used a combination of machine learning and qualitative analysis methods to analyse the reviewers' comments in evaluation reports from 3667 grant applications to the Initial Training Networks (ITN) of the Marie Curie Actions under the Seventh Framework Programme (FP7). Our results show that the reviewers' comments for each evaluation criterion were aligned with the Action's prespecified criteria and that the evaluation outcome was more influenced by the proposals’ weaknesses than by their strengths.     

Fair and Explainable Depression Detection in Social Media

Detection at an early stage is vital for the diagnosis of the majority of critical illnesses and is the same for identifying people suffering from depression. Nowadays, a number of researches have been done successfully to identify depressed persons based on their social media postings. However, an unexpected bias has been observed in these studies, which can be due to various factors like unequal data distribution. In this paper, the imbalance found in terms of participation in the various age groups and demographics is normalized using the one-shot decision approach. Further, we present an ensemble model combining SVM and KNN with the intrinsic explainability in conjunction with noisy label correction approaches, offering an innovative solution to the problem of distinguishing between depression symptoms and suicidal ideas. We achieved a final classification accuracy of 98.05%, with the proposed ensemble model ensuring that the data classification is not biased in any manner.