Electroactive ionenes combining caged‐shaped diazabicyclic cations and aromatic diimides were developed as interlayers in organic solar cells (OSCs). These ionenes reduce the work‐function of air‐stable metal electrodes (e.g., Ag, Cu and Au) by generating strong interfacial dipoles, and their optoelectronic and morphological characters can be modulated by aromatic diimides, leading to high conductivity and good compatibility with active layers. The optimal ionene exhibits superior charge‐transport, desirable crystallinity, and weak visible‐absorption, boosting the efficiency of benchmark PM6 : Y6‐based OSCs up to 17.44 %. The corresponding normal devices show excellent stability at maximum power point test under one sun illumination for 1000 h. Replacing Y6 with L8‐BO promotes the efficiency to 18.43 %, one of the highest in binary OSCs. Notably, high efficiencies >16 % are maintained as the interlayer thickness increasing to 105 nm, the best result with interlayer‐thickness over 100 nm.
Glaucoma is a serious disease that can cause complete, permanent blindness, and its early diagnosis is very difficult. In recent years, computer-aided screening and diagnosis of glaucoma has made considerable progress. The optic cup segmentation from fundus images is an extremely important part for the computer-aided screening and diagnosis of glaucoma. This paper presented an automatic optic cup segmentation method that used both color difference information and vessel bends information from fundus images to determine the optic cup boundary. During the implementation of this algorithm, not only were the locations of the 2 types of information points used, but also the confidences of the information points were evaluated. In this way, the information points with higher confidence levels contributed more to the determination of the final cup boundary. The proposed method was evaluated using a public database for fundus images. The experimental results demonstrated that the cup boundaries obtained by the proposed method were more consistent than existing methods with the results obtained by ophthalmologists.
This article considers potential impacts the study of language, including ecolinguistics, can have on important real-world issues, and how linguists and others can involve themselves in addressing these issues for a sustainable future. The article is divided into two parts. The first part provides an illustrative study in which computer tools were utilized to investigate media reporting. The study examined the relative coverage of issues of basic human needs (food, clean water, and sanitation), which are part of the focus of the UN Sustainable Development Goals, and of the COVID-19 pandemic outbreak, in four major newspapers from Malaysia, Singapore, the UK and the US. Data were collected between November 1, 2019 to March 31, 2020 when the COVID-19 pandemic was in its early days in terms of worldwide attention. During that time period, the pandemic received far more coverage in those newspapers than did the other issues, even though basic human needs greatly outweighed the COVID-19 pandemic as to deaths and other forms of suffering at the time of data collection, not to mention the toll on human life in the many years before the COVID-19 pandemic outbreak. Reasons for this severe discrepancy were explored with insights from professionals working in the media and related sectors. The skewed distribution of media coverage, we argue, reflects a crisis of responsibility and values. The second part of the article serves to highlight how those of us in language studies can make a contribution to the wider discussion about, among other important concerns, the role and responsibility of media in shaping the public’s views and actions on issues that are at the heart of sustainable development, and how we can be more socially engaged. We conclude by arguing that ecolinguists have much to contribute to the sustainability of the world, which ultimately requires a respect for the entire ecological community.
Zwitterionic polymers or small molecules are a class of widely used interlayer materials in organic solar cells (OSCs). It is challenging to develop such materials that combine good film-forming property, high batch-to-batch reproducibility, and broad thickness processing window in device applications. Herein, we designed and synthesized two self-doped conjugated mesopolymer zwitterions (CMZs), namely, MT 2 PDIMz and MT 2 PDINz (PDI = perylenediimide, M = mesopolymer, T 2 = dithiophene, Mz = imidazolium zwitterion, Nz = ammonium zwitterion). Both show good processability and effectively reduce the work function of metal electrodes. The substitution of imidazolium cations with ammonium cations on the side-chains can further modulate the solution assembly and self-doping effect of CMZs, enabling improved interfacial compatibility with active layers and higher electron mobility in MT 2 PDIMz. The versatility of CMZs interlayers for OSCs is confirmed with several binary and ternary active layers, affording efficiencies up to 19.01%. Notably, over 98% of the optimal efficiency is maintained as the thickness of the interlayers increases to 40 nm with good reproducibility.
We present XCMRC, the first public cross-lingual language understanding (XLU) benchmark which aims to test machines on their cross-lingual reading comprehension ability. To be specific, XCMRC is a Cross-lingual Cloze-style Machine Reading Comprehension task which requires the reader to fill in a missing word (we additionally provide ten noun candidates) in a sentence written in target language (English / Chinese) by reading a given passage written in source language (Chinese / English). Chinese and English are rich-resource language pairs, in order to study low-resource cross-lingual machine reading comprehension (XMRC), besides defining the common XCMRC task which has no restrictions on use of external language resources, we also define the pseudo low-resource XCMRC task by limiting the language resources to be used. In addition, we provide two baselines for common XCMRC task and two for pseudo XCMRC task respectively. We also provide an upper bound baseline for both tasks. We found that for common XCMRC task, translation-based method and multilingual sentence encoder-based method can obtain reasonable performance but still have much room for improvement. As for pseudo low-resource XCMRC task, due to strict restrictions on the use of language resources, our two approaches are far below the upper bound so there are many challenges ahead.
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