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Metamaterials are promising candidates for broadband light absorbers, but complex manufacturing procedures with expensive instruments have limited their practical applications. Herein, this work develops an all‐solution‐processed three‐dimensional (3D) metamaterial—based on a gold nanocage (GNC)/silk fibroin (SF) nanocomposite thin film—that achieves near‐unity (>94%) and broadband (350–2000 nm) light absorption. At a low particle density, the film exhibits a typical signal for localized surface plasmon resonance (LSPR) absorption at 856 nm, originating from its hollow geometrical shape of GNCs. In contrast, at high particle densities the films display ultrahigh broadband absorption, even in the non‐LSPR regions. This work attributes this behavior to the highly dense GNCs dispersed uniformly in the SF matrix acting optically as extremely fragmented metallic films, thereby retaining the highly absorptive nature of the metal while strongly suppressing its highly reflective properties. Moreover, the metamaterial absorber displays a desirable broadband‐light‐induced photothermal effect when illuminated with a halogen lamp. Finally, investigations into the photothermal electronic properties of this metamaterial combined with an Al/Si Schottky diode reveal an excellent photoresponse and photostability. The as‐designed systems can function at telecommunication wavelengths longer than the typical operation wavelengths of Si‐based devices. Such 3D metamaterial absorbers can be efficient platforms for various photothermal applications. [ABSTRACT FROM AUTHOR]

Psychiatric query document retrieval can assist individuals to locate query documents relevant to their depression-related problems efficiently and effectively. By referring to relevant documents, individuals can understand how to alleviate their depression-related symptoms according to recommendations from health professionals. This work presents an extended probabilistic Hyperspace Analog to Language (epHAL) model to achieve this aim. The epHAL incorporates the close temporal associations between words in query documents to represent word cooccurrence relationships in a high-dimensional context space. The information flow mechanism further combines the query words in the epHAL space to infer related words for effective information retrieval. The language model perplexity is considered as the criterion for model optimization. Finally, the epHAL is adopted for psychiatric query document retrieval, and indicates its superiority in information retrieval over traditional approaches. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--Retrieval models; 1.5.2 [Pattern Recognition]: Design Methodology--Classifier design and evaluation; J.3 [Life and Medical Sciences]: Medical information systems General Terms: Algorithms, Theory, Experimentation Additional Key Words and Phrases: Information retrieval, Hyperspace Analog to Language (HAL) model, information flow, query documents

This paper proposes a boost converter with coupled inductors and a buck-boost type of active clamp. In the converter, the active-clamp circuit is used to eliminate the voltage spike that is induced by the trapped energy in the leakage inductor of the coupled inductors. The active switch in the converter can still sustain a proper duty ratio even under high step-up applications, reducing voltage and current stresses significantly. Moreover, since both main and auxiliary switches can be turned on with zero-voltage switching, switching loss can be reduced, and conversion efficiency therefore can be improved significantly. A 200-W prototype of the proposed boost converter was built, from which experiment results have shown that efficiency can reach as high as 92% and surge can be suppressed effectively. It is relatively feasible for low-input-voltage applications, such as fuel cell and battery power conversion. Index Terms--Active clamp, boost converter, coupled inductors, zero-voltage switching (ZVS).

This investigation presents a novel approach to semantic segment extraction and matching for retrieving information from Internet FAQs with natural language queries. Two semantic segments, the question category segment (QS) and the keyword segment (KS), are extracted from the input queries and the FAQ questions with a semiautomaticatly derived question-semantic grammar. A semantic matching method is presented to estimate the similarity between the semantic segments of the query and the questions in the FAQ collection. Additionally, the vector space model (VSM) is adopted to measure the similarity between the query and the answers of the QA pairs. Finally, a multistage ranking strategy is adopted to determine the optimally performing combination of similarity metrics. The experimental results illustrate that the proposed method achieves an average rank of 4.52 and a top-10 recall rate of 90.89 percent. Compared with the query-expansion method, this method improves the performance by 4.82 places in the average rank of correct answers, 25.34 percent in the top-5 recall rate, and 5.21 percent in the top-10 recall rate. Index Terms--Natural language processing, retrieval models, query formulation, deduction and theorem proving, knowledge processing.