Planar Metasurfaces Enable High‐Efficiency Colored Perovskite Solar Cells

The achievement of perfect light absorption in ultrathin semiconductor materials is not only a long‐standing goal, but also a critical challenge for solar energy applications, and thus requires a redesigned strategy. Here, a general strategy is demonstrated both theoretically and experimentally to create a planar metasurface absorber comprising a 1D ultrathin planar semiconductor film (replacing the 2D array of subwavelength elements in classical metasurfaces), a transparent spacer, and a metallic back reflector. Guided by derived formulisms, a new type of macroscopic planar metasurface absorber is experimentally demonstrated with light near‐perfectly and exclusively absorbed by the ultrathin semiconductor film. To demonstrate the power and simplicity of this strategy, a prototype of a planar metasurface solar cell is experimentally demonstrated. Furthermore, the device model predicts that a colored planar metasurface perovskite solar cell can maintain 75% of the efficiency of its black counterpart despite the use of a perovskite film that is one order of magnitude thinner. The displayed cell colors have high purities comparable to those of state‐of‐the‐art color filters, and are insensitive to viewing angles up to 60°. The general theoretical framework in conjunction with experimental demonstrations lays the foundation for designing miniaturized, planar, and multifunctional solar cells and optoelectronic devices.A type of macroscopic planar metasurface absorber with light near‐perfectly and exclusively absorbed by the ultrathin semiconductor film is theoretically and experimentally demonstrated via a general strategy. Guided by this strategy, colored perovskite solar cells are further designed to meet all the desired characteristics including high power conversion efficiency, high‐purity, tunability, and angle‐insensitive colors.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/1/advs793.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/2/advs793-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/3/advs793_am.pd

In vitro pharmacokinetics of sirolimus-coated stent for tracheal stenosis

Purpose: To investigate the in vitro pharmacokinetics of sirolimus-coated stent for tracheal stenosisMethods: Naked nickel titanium alloy stent was placed in methylene chloride leaching solution with different ratios of sirolimus/poly(lactic-co-glycolic acid) (PLGA). The morphology, thickness, and pellicles on the surface of the stent were observed by scanning electronic microscopy. Drug release from the stent was determined by enzyme amplification immunoassay.Results: Sirolimus was smoothly and uniformly attached to the stent, with an optimal sirolimus: PLGA coating ratio of 1:10. Further increases in sirolimus: PLGA ratio did not improve stent drug loading. A slow release of sirolimus from the stent was observed in the first week, followed by a rapid release and then much slower release process. Release of sirolimus persisted in the stent throughout the period of 42 days.Conclusion: The sirolimus-coated stent has a good surface morphology, and sustained and effective drug release characteristics. Thus, it may be effective and safe for use in the treatment of tracheal stenosis in vivo.Keywords: Tracheal stenosis, Sirolimus, Drug-coated stents, poly(lactic-co glycolic acid) PLG

Towards a Low-Cost Remote Memory Attestation for the Smart Grid

In the smart grid, measurement devices may be compromised by adversaries, and their operations could be disrupted by attacks. A number of schemes to efficiently and accurately detect these compromised devices remotely have been proposed. Nonetheless, most of the existing schemes detecting compromised devices depend on the incremental response time in the attestation process, which are sensitive to data transmission delay and lead to high computation and network overhead. To address the issue, in this paper, we propose a low-cost remote memory attestation scheme (LRMA), which can efficiently and accurately detect compromised smart meters considering real-time network delay and achieve low computation and network overhead. In LRMA, the impact of real-time network delay on detecting compromised nodes can be eliminated via investigating the time differences reported from relay nodes. Furthermore, the attestation frequency in LRMA is dynamically adjusted with the compromised probability of each node, and then, the total number of attestations could be reduced while low computation and network overhead can be achieved. Through a combination of extensive theoretical analysis and evaluations, our data demonstrate that our proposed scheme can achieve better detection capacity and lower computation and network overhead in comparison to existing schemes

Integrating BDI agents into a MATSim simulation

MATSim is a mature and powerful traffic simulator, used for large scale traffic simulations, primarily to assess likely results of various infrastructure or road network changes. More recently there has been work to extend MATSim to allow its use in applications requiring what has been referred to as "within day replanning". In the work described here we have coupled MATSim with a BDI (Belief Desire Intention) system to allow both more extensive modelling of the agent's decision making, as well as reactivity to environmental situations. The approach used allows for all agents to be "intelligent" or for some to be "intelligent"/reactive, while others operate according to plans that are static within a single day. The former is appropriate for simulations such as a bushfire evacuation, where all agents will be reacting to the changing environment. The latter is suited to introducing agents such as taxis into a standard MATSim simulation, as they cannot realistically have a predetermined plan, but must constantly respond to the current situation. We have prototype applications for both bushfire evacuation and taxis. By extending the capabilities of MATSim to allow agents to respond intelligently to changes in the environment, we facilitate the use of MATSim in a wide range of simulation applications. The work also opens the way for MATSim to be used alongside other simulation components, in a simulation integrating multiple components

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%

Optical clearing of laser-induced tissue plasma

We studied the effect of optical clearing (OC) by glycerol on laser-induced tissue plasma using the immersion method. The results demonstrated the apparently enhanced effect of glycerol on the molecular spectra of the laser induced plasma. The OC is more sensitive to the molecular bands than atomic lines. After tissue immersion in the glycerol, the electron density of tissue plasma is decreased. The laser plasma temperature of the glycerol treated tissue is higher than for virgin fresh tissue. The tissue plasma after the glycerol application is still in the local thermal equilibrium plasma state. This work presents a new perspective for OC application that can extend from tissue better imaging quality to improvement of laser plasma generation

A duplex real-time reverse transcriptase polymerase chain reaction assay for detecting western equine and eastern equine encephalitis viruses

In order to establish an accurate, ready-to-use assay for simultaneous detection of Eastern equine encephalitis virus (EEEV) and Western equine encephalitis virus (WEEV), we developed one duplex TaqMan real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay, which can be used in human and vector surveillance. First, we selected the primers and FAM-labeled TaqMan-probe specific for WEEV from the consensus sequence of NSP3 and the primers and HEX-labeled TaqMan-probe specific for EEEV from the consensus sequence of E3, respectively. Then we constructed and optimized the duplex real-time RT-PCR assay by adjusting the concentrations of primers and probes. Using a series of dilutions of transcripts containing target genes as template, we showed that the sensitivity of the assay reached 1 copy/reaction for EEEV and WEEV, and the performance was linear within the range of at least 10(6 )transcript copies. Moreover, we evaluated the specificity of the duplex system using other encephalitis virus RNA as template, and found no cross-reactivity. Compared with virus isolation, the gold standard, the duplex real time RT-PCR assay we developed was 10-fold more sensitive for both WEEV and EEEV detection

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.Comment: Accepted by The Web Conference 2020 (WWW 2020) as full paper (oral presentation

Development of an ELISA-array for simultaneous detection of five encephalitis viruses

Japanese encephalitis virus(JEV), tick-borne encephalitis virus(TBEV), and eastern equine encephalitis virus (EEEV) can cause symptoms of encephalitis. Establishment of accurate and easy methods by which to detect these viruses is essential for the prevention and treatment of associated infectious diseases. Currently, there are still no multiple antigen detection methods available clinically. An ELISA-array, which detects multiple antigens, is easy to handle, and inexpensive, has enormous potential in pathogen detection. An ELISA-array method for the simultaneous detection of five encephalitis viruses was developed in this study. Seven monoclonal antibodies against five encephalitis-associated viruses were prepared and used for development of the ELISA-array. The ELISA-array assay is based on a "sandwich" ELISA format and consists of viral antibodies printed directly on 96-well microtiter plates, allowing for direct detection of 5 viruses. The developed ELISA-array proved to have similar specificity and higher sensitivity compared with the conventional ELISAs. This method was validated by different viral cultures and three chicken eggs inoculated with infected patient serum. The results demonstrated that the developed ELISA-array is sensitive and easy to use, which would have potential for clinical use