Nature in Robert Browning’s Poems

Different from Romantic understanding of nature, in Browning’s poems, when nature is mentioned, there are two kinds of depictions. One type is to describe nature itself only, and there is nothing symbolic one can infer from the descriptions of nature or the descriptions have little relationship with its subject, that is, nature for nature’s sake. Though some depictions are related to the subject, they present the negative aspects of nature. As illustrated in “Childe Roland to the Dark Tower Came”, the natural settings are forlorn and bleak, like in a wasteland

The N-terminal domain of Lhcb proteins is critical for recognition of the LHCII kinase

AbstractThe light-harvesting chlorophyll (Chl) a/b complex of photosystem (PS) II (LHCII) plays important roles in the distribution of the excitation energy between the two PSs in the thylakoid membrane during state transitions. In this process, LHCII, homo- or heterotrimers composed of Lhcb1–3, migrate between PSII and PSI depending on the phosphorylation status of Lhcb1 and Lhcb2. We have studied the mechanisms of the substrate recognition of a thylakoid threonine kinase using reconstituted site-directed trimeric Lhcb protein–pigment complex mutants. Mutants lacking the positively charged residues R/K upstream of phosphorylation site (Thr) in the N-terminal domain of Lhcb1 were no longer phosphorylated. Besides, the length of the peptide upstream of the phosphorylated site (Thr) is also crucial for Lhcb phosphorylation in vitro. Furthermore, the two N-terminal residues of Lhcb appear to play a key role in the phosphorylation kinetics because Lhcb with N-terminal RR was phosphorylated much faster than with RK. Therefore, we conclude that the substrate recognition of the LHCII kinase is determined to a large extent by the N-terminal sequence of the Lhcb proteins. The study provides new insights into the interactions of the Lhcb proteins with the LHCII kinase

Multi-Factors Aware Dual-Attentional Knowledge Tracing

With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.Comment: Accepted by CIKM 2021, 10 pages, 10 figures, 6 table

Optimization with a Genetic Algorithm for Multilayer Electromagnetic Wave Absorption Cement Mortar Filled with Expended Perlite

Abstract: Due to the complexity of the design of multilayer electromagnetic (EM) wave absorbing materials, it is difficult to establish the relationship between material parameters (type and filling ratios) and EM properties using traditional trial and error methods. Based on the measured EM parameters within a few materials and Boltzmann mixing theory, a database of EM parameters was thereafter built up. In this study, the genetic algorithm (GA) was used to design the multilayer wave-absorbing cement mortar. In order to verify this method, a multilayer mortar was fabricated and measured. The simulated and measured results are well consistent, which convincingly verifies computer-aided design. In addition, the optimized result expresses that the first layer as a matching layer guides EM waves into the interior of the material, while the other layers as absorption layers attenuate EM waves. The multilayer material may not meet the impedance gradient principle but still exhibits better EM wave absorption performance. The reflection loss (RL) of all optimized three layer sample is below –6.89 dB in the full frequency band and the minimum RL is –26.21 dB. This composite absorbing material and the GA method provide more design ideas for the design of future cement-based wave-absorbing materials and save a lot of time and material cost