智能锅炉水位控制系统设计

智能锅炉水位控制系统在实际工程中是一种用途广泛的控制设备。本设计基于Atmega8单片机作为核心控制器,采用模糊PID控制算法,在控制电路和驱动电路的作用下实现对锅炉水位的自动控制和故障报警。该电路具有简单、经济、稳定可靠、容易实现等特点。     

Up-regulation interleukin-6 and interleukin-8 by activated protein C in lipopolysaccharide-treated human umbilical vein endothelial cells

Objective： To investigate the effect of activated protein C （APC） on inflammatory responses in human umbilical vein endothelial cells （HUVEC） stimulated with lipopolysaccharide （LPS）. Methods： The second passage of collagenase digested HUVEC was divided into the following groups： serum free medium control group （SFM control）, phosphate buffer solution control group （PBS control）, LPS group with final concentration of 1 μg/ml （LPS group）, APC group with final concentration of 7 μg/ml, Pre-APC group （APC pretreatment for 30 min prior to LPS challenge）, and Post-APC group （APC administration 30 min after LPS challenge）. Supernatant was harvested at 0, 4, 8, 12 and 24 h after LPS challenge. Interleukin-6 （IL-6） and Interleukin-8 （IL-8） levels were analyzed with ELISA. Cells were harvested at 24 h after LPS challenge, and total RNA was extracted. Messenger RNA levels for IL-6 and IL-8 were semi-quantitatively determined by RT-PCR. Results： Compared with control group, IL-6 and IL-8 levels steadily increased 4 to 24 h after LPS stimulation. APC treatment could increase LPS-induced IL-6 and IL-8 production. The mRNA levels of IL-6 and IL-8 exhibited a similar change. Conclusion： APC can further increase the level of IL-6 and IL-8 induced by LPS. The effect of these elevated cytokines is still under investigation.     

英语专业高年级学生阅读策略实证研究

为帮助英语专业高年级学生更好地备考专业八级阅读理解,通过问卷调查及访谈研究了学生常用阅读策略的使用情况,探讨了阅读策略与阅读效率的相关性,提出了一些改进建议。     

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.