The Routledge handbook of second language acquisition and technology. Ziegler, N. & González-Lloret, M. (Eds.) (2022). Routledge, New York and London, 409 pages, ISBN: 978-1-351-11758-6

The COVID-19 pandemic has “thrusted both teachers and learners” (p. 16) into a new language learning environment where the relentlessly progressing technology is exerting an increasingly indispensable role, which has stimulated a reevaluation on the relationship between second language acquisition (SLA) and technology. Contributed by a pool of expertise with full scholarly apparatus, The Routledge Handbook of Second Language Acquisition and Technology offers enlightening insights into this reevaluation. FUNDING INFORMATION: This review was supported by Shanghai International Studies University (grant no. 2020114210).The COVID-19 pandemic has “thrusted both teachers and learners” (p. 16) into a new language learning environment where the relentlessly progressing technology is exerting an increasingly indispensable role, which has stimulated a reevaluation on the relationship between second language acquisition (SLA) and technology. Contributed by a pool of expertise with full scholarly apparatus, The Routledge Handbook of Second Language Acquisition and Technology offers enlightening insights into this reevaluation. FUNDING INFORMATION: This review was supported by Shanghai International Studies University (grant no. 2020114210)

Semi-supervised Domain Adaptation in Graph Transfer Learning

As a specific case of graph transfer learning, unsupervised domain adaptation on graphs aims for knowledge transfer from label-rich source graphs to unlabeled target graphs. However, graphs with topology and attributes usually have considerable cross-domain disparity and there are numerous real-world scenarios where merely a subset of nodes are labeled in the source graph. This imposes critical challenges on graph transfer learning due to serious domain shifts and label scarcity. To address these challenges, we propose a method named Semi-supervised Graph Domain Adaptation (SGDA). To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding, thus the node classifier trained on labeled source nodes can be transferred to the target nodes. Moreover, to address the label scarcity, we propose pseudo-labeling on unlabeled nodes, which improves classification on the target graph via measuring the posterior influence of nodes based on their relative position to the class centroids. Finally, extensive experiments on a range of publicly accessible datasets validate the effectiveness of our proposed SGDA in different experimental settings

Polytypism and Unexpected Strong Interlayer Coupling of two-Dimensional Layered ReS2

The anisotropic two-dimensional (2D) van der Waals (vdW) layered materials, with both scientific interest and potential application, have one more dimension to tune the properties than the isotropic 2D materials. The interlayer vdW coupling determines the properties of 2D multi-layer materials by varying stacking orders. As an important representative anisotropic 2D materials, multilayer rhenium disulfide (ReS2) was expected to be random stacking and lack of interlayer coupling. Here, we demonstrate two stable stacking orders (aa and a-b) of N layer (NL, N>1) ReS2 from ultralow-frequency and high-frequency Raman spectroscopy, photoluminescence spectroscopy and first-principles density functional theory calculation. Two interlayer shear modes are observed in aa-stacked NL-ReS2 while only one interlayer shear mode appears in a-b-stacked NL-ReS2, suggesting anisotropic-like and isotropic-like stacking orders in aa- and a-b-stacked NL-ReS2, respectively. The frequency of the interlayer shear and breathing modes reveals unexpected strong interlayer coupling in aa- and a-b-NL-ReS2, the force constants of which are 55-90% to those of multilayer MoS2. The observation of strong interlayer coupling and polytypism in multi-layer ReS2 stimulate future studies on the structure, electronic and optical properties of other 2D anisotropic materials

Nanoscale pore and crack evolution in shear thin layers of shales and the shale gas reservoir effect

Studies on matrix-related pores from the nanometer to the micrometer scale in shales have made considerable progress in recent decades. However, nanoscale pores and cracks developed in the shear thin layers have not been systematically discussed. In this work, interlayer shear slip occurring in shales are observed through practical examples. The results show that the shear thin layer constructed by nanograin coating is widely distributed on superimposed shear slip planes. Usually, the development of the shear thin layer undergoes viscoelastic-rheological-embrittling deformation stages, and the nanograin texture assembled in the shear thin layer can demonstrate three pore and crack structure types. Based on the mechanical analysis concerning nanoscale cohesion force, it is identified that, as long as force remains a state, the shear thin layer must bear a nanoscale pore and crack character. Furthermore, the shale gas reservoir effect of the nanoscale pore and crack is simply discussed. Obviously, the adsorbed gas effect of the nanograin itself has a larger nanoscale size and surface functionality than those of kerogen and clay particles in the shales; three structure types of the nanoscale pore and crack can act as given controlling factors of storage and permeability for the free gas. Both the matrix-related pores and the three pore and crack structures have an intimate connection with respect to each other in the genetic mechanism and temporal-spatial evolution. This work has important theoretical implications for supplementing the pore and crack classification of shale. Moreover, it makes a significant contribution to shale gas exploration and development.Cited as: Sun, Y., Ju, Y., Zhou, W., Qiao, P., Tao, L., Xiao, L. Nanoscale pore and crack evolution in shear thin layers of shales and the shale gas reservoir effect. Advances in Geo-Energy Research, 2022, 6(3): 221-229. https://doi.org/10.46690/ager.2022.03.0

Effects of Influent Organic Loading Rates and Electrode Locations on the Electrogenesis Capacity of Constructed Wetland-Microbial Fuel Cell Systems

Three novel constructed wetland-microbial fuel cells (CW-MFCs), based on electrode location, were developed for wastewater treatment and sustainable electricity production by embedding a MFC into a CW system. In the three CW-MFCs, electrodes were placed in different locations, including bottom anode-rhizosphere cathode CW-MFC (BA-RC-CW-MFC), rhizosphere anode-air cathode CW-MFC (RA-AC-CW-MFC), and bottom anode-air cathode CW-MFC (BA-AC-CW-MFC), to investigate the combined effects of organic loading rates (OLRs) and reactor configurations on the electrogenesis capacity of the hybrid system. All the systems operated continuously to treat five types of synthetic wastewater with increasing OLRs: 9.2, 18.4, 27.6, 55.2, and 92.0 g chemical oxygen demand (COD) m(-2) d(-1). The BA-RC-CW-MFC failed to produce electricity at any OLR, whereas the maximum power densities of 0.79 +/- 0.01 and 10.77 +/- 0.52 mW m(-2) were achieved in the RA-AC-CW-MFC with 18.4 g COD m(-2) d(-1) influent OLR and in the BA-AC-CW-MFC with 27.6 g COD m(-2) d(-1) influent OLR, respectively. The coulombic efficiencies of the RA-AC-CW-MFC and BA-AC-CW-MFC decreased gradually with the increase in influent OLRs. (C) 2016 American Institute of Chemical Engineers Environ Prog, 36: 435-441, 2017</p

Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation

The objective of topic inference in research proposals aims to obtain the most suitable disciplinary division from the discipline system defined by a funding agency. The agency will subsequently find appropriate peer review experts from their database based on this division. Automated topic inference can reduce human errors caused by manual topic filling, bridge the knowledge gap between funding agencies and project applicants, and improve system efficiency. Existing methods focus on modeling this as a hierarchical multi-label classification problem, using generative models to iteratively infer the most appropriate topic information. However, these methods overlook the gap in scale between interdisciplinary research proposals and non-interdisciplinary ones, leading to an unjust phenomenon where the automated inference system categorizes interdisciplinary proposals as non-interdisciplinary, causing unfairness during the expert assignment. How can we address this data imbalance issue under a complex discipline system and hence resolve this unfairness? In this paper, we implement a topic label inference system based on a Transformer encoder-decoder architecture. Furthermore, we utilize interpolation techniques to create a series of pseudo-interdisciplinary proposals from non-interdisciplinary ones during training based on non-parametric indicators such as cross-topic probabilities and topic occurrence probabilities. This approach aims to reduce the bias of the system during model training. Finally, we conduct extensive experiments on a real-world dataset to verify the effectiveness of the proposed method. The experimental results demonstrate that our training strategy can significantly mitigate the unfairness generated in the topic inference task.Comment: 19 pages, Under review. arXiv admin note: text overlap with arXiv:2209.1391