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A novel hybrid of quasi-random nanostructures and Archimedean spiral arrangement light trapping coating, and its low-cost, fast-production technique are presented here. Both numerical simulations and experiments confirmed the superiority of efficiency enhancement and omnidirectional light-trapping capacity when coating the created nanostructures on thin-film solar cells. 

Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS). We observe that with precise type information, entity disambiguation becomes a straightforward task. However, fine-grained type information is usually not available in biomedical domain. Thus, we propose LATTE, a LATent Type Entity Linking model, that improves entity linking by modeling the latent fine-grained type information about mentions and entities. Unlike previous methods that perform entity linking directly between the mentions and the entities, LATTE jointly does entity disambiguation, and latent fine-grained type learning, without direct supervision. We evaluate our model on two biomedical datasets: MedMentions, a large scale public dataset annotated with UMLS concepts, and a de-identified corpus of dictated doctor’s notes that has been annotated with ICD concepts. Extensive experimental evaluation shows our model achieves significant performance improvements over several state-of-the-art techniques. 

The growth of the Web in recent years has resulted in the development of various online platforms that provide healthcare information services. These platforms contain an enormous amount of information, which could be beneficial for a large number of people. However, navigating through such knowledgebases to answer specific queries of healthcare consumers is a challenging task. A majority of such queries might be non-factoid in nature, and hence, traditional keyword-based retrieval models do not work well for such cases. Furthermore, in many scenarios, it might be desirable to get a short answer that sufficiently answers the query, instead of a long document with only a small amount of useful information. In this paper, we propose a neural network model for ranking documents for question answering in the healthcare domain. The proposed model uses a deep attention mechanism at word, sentence, and document levels, for efficient retrieval for both factoid and non-factoid queries, on documents of varied lengths. Specifically, the word-level cross-attention allows the model to identify words that might be most relevant for a query, and the hierarchical attention at sentence and document levels allows it to do effective retrieval on both long and short documents. We also construct a new large-scale healthcare question-answering dataset, which we use to evaluate our model. Experimental evaluation results against several state-of-the-art baselines show that our model outperforms the existing retrieval techniques. 

To quantify the volatility of organic aerosols (OA), a comprehensive campaign was conducted in the Chinese megacity. Volatility distributions of OA and particle‐phase organic nitrate (pON) were estimated based on five methods: (a) empirical method and (b) kinetic model based on the measurement of a thermodenuder (TD) coupled with an aerosol mass spectrometer; (c) Formula‐based SIMPOL model‐driven method; (d) Element‐based estimations using molecular formula measurements of OA; and (e) gas/particle partitioning. Our results demonstrate that the ambient OA volatility distribution shows good agreement between the two heating methods and the formula‐based method when assuming ambient OA was mainly composed of organic nitrate (pON), organic sulfate and acid groups using the SIMPOL model. However, the element‐based method tends to overestimate the volatility of OA compared to the above three methods, suggesting large uncertainties in the parameterizations or in the representativeness of the molecular measurements that need further refinement. The volatility of ambient OA is generally lower than that of the laboratory‐derived secondary OA, emphasizing the impact of aging. A large fraction at the higher and lower volatility ranges (approximately logC* ≤ −9 and ≥2 μg m⁻³) was found for pON, implying the importance of both extremely low volatile and semi‐volatile species. Overall, this study evaluates different methods for volatility estimation and gives new insight into the volatility of OA and pON in urban areas.