Plasmonic-photonic crystal coupled nanolaser

We propose and demonstrate a hybrid photonic-plasmonic nanolaser that combines the light harvesting features of a dielectric photonic crystal cavity with the extraordinary confining properties of an optical nano-antenna. In that purpose, we developed a novel fabrication method based on multi-step electron-beam lithography. We show that it enables the robust and reproducible production of hybrid structures, using fully top down approach to accurately position the antenna. Coherent coupling of the photonic and plasmonic modes is highlighted and opens up a broad range of new hybrid nanophotonic devices

Evaluation of the Effect of Saturated Silty and Fine Sand Foundation Improved by Vibro-Flotation in Seismic Area

The improvement of liquefaction foundations in seismic region has been concerning many engineers. The authors had carried out experimental studies on the improvement of saturated silty and fine sand foundations at the suburbs of Beijing by vibroflotation method. The test results are described and the improvement effects are evaluated in this paper

Bioelectricity generation by wetland plant-sediment microbial fuel cells (P-SMFC) and effects on the transformation and mobility of arsenic and heavy metals in sediment.

Two wetland plant-sediment microbial fuel cell systems (PSM1 and PSM2) and one wetland sediment microbial fuel cell system (SM) were constructed to investigate their electricity production performance and the simultaneous migration and transformation of arsenic and heavy metals in sediment and overlying water, arsenic and heavy metals uptake by plants. The bioelectricity generation was monitored for 175 days, and sediment samples were collected at three time points (64, 125 and 200 days) for the analysis. The results showed that plants improved the efficiency of the electricity production by the fuel cell system. The average output voltage was: PSM1 (0.32 V) > PSM2 (0.28 V) > SM (0.24 V)(P ≤ 0.05).The electricity production of the electrodes and the introduction of plants affected the mobility and transformation of As, Zn and Cd in the sediment, which contributed to their stability in the sediment and reduced the release of these metals into the overlying water column. The bioelectricity production process affected the bioavailability of arsenic and heavy metals in the sediment and attenuated metal uptake by plants, which indicated the potential for remediation of arsenic and heavy metals pollution in sediment

NASA Prediction of Worldwide Energy Resource High Resolution Meteorology Data For Sustainable Building Design

A primary objective of NASA's Prediction of Worldwide Energy Resource (POWER) project is to adapt and infuse NASA's solar and meteorological data into the energy, agricultural, and architectural industries. Improvements are continuously incorporated when higher resolution and longer-term data inputs become available. Climatological data previously provided via POWER web applications were three-hourly and 1x1 degree latitude/longitude. The NASA Modern Era Retrospective-analysis for Research and Applications (MERRA) data set provides higher resolution data products (hourly and 1/2x1/2 degree) covering the entire globe. Currently POWER solar and meteorological data are available for more than 30 years on hourly (meteorological only), daily, monthly and annual time scales. These data may be useful to several renewable energy sectors: solar and wind power generation, agricultural crop modeling, and sustainable buildings. A recent focus has been working with ASHRAE to assess complementing weather station data with MERRA data. ASHRAE building design parameters being investigated include heating/cooling degree days and climate zones

High-throughput cell-based screening reveals a role for ZNF131 as a repressor of ERalpha signaling

<p>Abstract</p> <p>Background</p> <p>Estrogen receptor α (ERα) is a transcription factor whose activity is affected by multiple regulatory cofactors. In an effort to identify the human genes involved in the regulation of ERα, we constructed a high-throughput, cell-based, functional screening platform by linking a response element (ERE) with a reporter gene. This allowed the cellular activity of ERα, in cells cotransfected with the candidate gene, to be quantified in the presence or absence of its cognate ligand E2.</p> <p>Results</p> <p>From a library of 570 human cDNA clones, we identified zinc finger protein 131 (ZNF131) as a repressor of ERα mediated transactivation. ZNF131 is a typical member of the BTB/POZ family of transcription factors, and shows both ubiquitous expression and a high degree of sequence conservation. The luciferase reporter gene assay revealed that ZNF131 inhibits ligand-dependent transactivation by ERα in a dose-dependent manner. Electrophoretic mobility shift assay clearly demonstrated that the interaction between ZNF131 and ERα interrupts or prevents ERα binding to the estrogen response element (ERE). In addition, ZNF131 was able to suppress the expression of pS2, an ERα target gene.</p> <p>Conclusion</p> <p>We suggest that the functional screening platform we constructed can be applied for high-throughput genomic screening candidate ERα-related genes. This in turn may provide new insights into the underlying molecular mechanisms of ERα regulation in mammalian cells.</p

Instance-Aware Domain Generalization for Face Anti-Spoofing

Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-grained and subjective, which cannot reflect real domain distributions accurately. Besides, such domain-aware methods focus on domain-level alignment, which is not fine-grained enough to ensure that learned representations are insensitive to domain styles. To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels. Specifically, Instance-Aware Domain Generalization framework is proposed to learn the generalizable feature by weakening the features' sensitivity to instance-specific styles. Concretely, we propose Asymmetric Instance Adaptive Whitening to adaptively eliminate the style-sensitive feature correlation, boosting the generalization. Moreover, Dynamic Kernel Generator and Categorical Style Assembly are proposed to first extract the instance-specific features and then generate the style-diversified features with large style shifts, respectively, further facilitating the learning of style-insensitive features. Extensive experiments and analysis demonstrate the superiority of our method over state-of-the-art competitors. Code will be publicly available at https://github.com/qianyuzqy/IADG.Comment: Accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 202