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| 2. |
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| 3. |
- Öberg, Christina, 1970-, et al.
(author)
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- 2005
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In: IMP Conference,2005. - Rotterdam : RSM Erasmus University. ; , s. 49-
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Conference paper (peer-reviewed)
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| 4. |
- Aad, G., et al.
(author)
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- 2013
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Journal article (peer-reviewed)
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| 5. |
- Krishnan, Rajet, 1982, et al.
(author)
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Large-Scale Analysis of Linear Massive MIMO Precoders in the Presence of Phase Noise
- 2015
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In: 2015 IEEE International Conference on Communication Workshop. - 9781467363051 ; , s. 1172-1177
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Conference paper (peer-reviewed)abstract
- We study the impact of phase noise on the downlink performance of a multi-user multiple-input multiple-output system, where the base station (BS) employs a large number of transmit antennas $M$. We consider a setup where the BS employs $M_{\mr{osc}}$ free-running oscillators, and $M/M_{\mr{osc}}$ antennas are connected to each oscillator. For this configuration, we analyze the impact of phase noise on the performance of regularized zero-forcing (RZF) precoding, when $M$ and the number of users $K$ are asymptotically large, while the ratio $M/K=\beta$ is fixed. We analytically show that the impact of phase noise on the signal-to-interference-plus-noise ratio (SINR) can be quantified as an effective reduction in the quality of the channel state information available at the BS when compared to a system without phase noise. As a consequence, we observe that as $M_{\mr{osc}}$ increases, the SINR of the RZF precoder degrades as the interference power increases, and the desired signal power decreases. On the other hand, the variance of the random phase variations caused by the BS oscillators reduces with increasing $M_{\mr{osc}}$. Through simulations, we verify our analytical results, and study the performance of the RZF precoder for different phase noise and channel noise variances.
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| 8. |
- Li, Wei, et al.
(author)
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Autophagy dysfunction and regulatory cystatin C in macrophage death of atherosclerosis
- 2016
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In: Journal of Cellular and Molecular Medicine. - : Wiley. - 1582-1838 .- 1582-4934. ; 20:9, s. 1664-1672
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Journal article (peer-reviewed)abstract
- Autophagy dysfunction in mouse atherosclerosis models has been associated with increased lipid accumulation, apoptosis and inflammation. Expression of cystatin C (CysC) is decreased in human atheroma, and CysC deficiency enhances atherosclerosis in mice. Here, we first investigated the association of autophagy and CysC expression levels with atheroma plaque severity in human atherosclerotic lesions. We found that autophagy proteins Atg5 and LC3β in advanced human carotid atherosclerotic lesions are decreased, while markers of dysfunctional autophagy p62/SQSTM1 and ubiquitin are increased together with elevated levels of lipid accumulation and apoptosis. The expressions of LC3β and Atg5 were positively associated with CysC expression. Second, we investigated whether CysC expression is involved in autophagy in atherosclerotic apoE-deficient mice, demonstrating that CysC deficiency (CysC-/-) in these mice results in reduction of Atg5 and LC3β levels and induction of apoptosis. Third, macrophages isolated from CysC-/- mice displayed increased levels of p62/SQSTM1 and higher sensitivity to 7-oxysterol-mediated lysosomal membrane destabilization and apoptosis. Finally, CysC treatment minimized oxysterol-mediated cellular lipid accumulation. We conclude that autophagy dysfunction is a characteristic of advanced human atherosclerotic lesions and is associated with reduced levels of CysC. The deficiency of CysC causes autophagy dysfunction and apoptosis in macrophages and apoE-deficient mice. The results indicate that CysC plays an important regulatory role in combating cell death via the autophagic pathway in atherosclerosis. Copyright
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| 9. |
- Deng, Jifei, et al.
(author)
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Offline reinforcement learning for industrial process control : A case study from steel industry
- 2023
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In: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 632, s. 221-231
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Journal article (peer-reviewed)abstract
- Flatness is a crucial indicator of strip quality that presents a challenge in regulation due to the high-speed process and the nonlinear relationship between flatness and process parameters. Conventional methods for controlling flatness are based on the first principles, empirical models, and predesigned rules, which are less adaptable to changing rolling conditions. To address this limitation, this paper proposed an offline reinforcement learning (RL) based data-driven method for flatness control. Based on the data collected from a factory, the offline RL method can learn the process dynamics from data to generate a control policy. Unlike online RL methods, the proposed method does not require a simulator for training, the policy can be potentially safer and more accurate since a simulator involves simplifications that can introduce bias. To obtain a steady performance, the proposed method incorporated ensemble Q-functions into policy evaluation to address uncertainty estimation. To address distributional shifts, based on Q-values from ensemble Q-functions, behavior cloning was added to policy improvement. Simulation and comparison results showed that the proposed method outperformed the state-of-the-art offline RL methods and achieved the best performance in producing strips with lower flatness.
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| 10. |
- Ding, Qian, 1983-, et al.
(author)
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Setting Risk-Based Occupational Exposure Limits for Non-Threshold Carcinogens
- 2014
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In: Human and Ecological Risk Assessment. - : Informa UK Limited. - 1080-7039 .- 1549-7860. ; 20:5, s. 1329-1344
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Journal article (peer-reviewed)abstract
- Several regulators have recently issued so-called risk-based occupational exposure limits for carcinogenic substances, and also reported estimates of the risk of fatality that exposure to the limit value would give rise to. This practice provides an opportunity to study how differences in the exposure limits set by different regulators are influenced by differences in the scientific judgment (what is the risk at different levels?) and in the policy judgment (how should large risks be accepted?). Based on a broad search, a list was compiled of exposure limits for carcinogens that the respective regulator associates with a numerical risk estimate. For benzene, such data was available from six regulators. The differences in estimates of the risk/exposure relationship and in risk tolerance were about equal in size for benzene, while the range for acceptability was somewhat wider. A similar pattern was observed, although less clearly, for substances with data from only two or three regulators. It is concluded that the science factor and the policy factor both contribute to differences in exposure limits for carcinogens. It was not possible to judge which of these two factors has the larger influence.
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