Characterization of PM2.5 Mass Concentration in the Onshore of Sanya, China

Numbers of real-time data (E-BAM) of PM2.5 were collected in the period from Jan 8th 2012 to Jan 1st 2013 at the laboratory of Tropical Ocean University (Sanya, China). The average mass concentration was 19.7 μg/m³. The highest 40.5 μg/m³ in October compared to the lowest 14.1 μg/m³ in July. From a seasonal perspective, the average PM2.5 mass concentration in fall and winter are relatively higher than that in both spring and summer. On the basis of satellite map of fire points and backward trajectories of the air masses, we primarily deduced that the PM2.5 in Sanya may be caused by the biomass burning and industrial pollutants from the area of Pearl River Delta of China and the Indo-China peninsula (e.g. Vietnam, Laos)

Detecting the Thermal Properties of Bone Cement by Temperature Sensor

Polymethylmethacrylate-based (PMMA) bone cements containing functionalized carbon nanotubes (CNTs) were prepared, and the thermal properties of the resultant nanocomposite cements were characterized in accordance with the international standard for acrylic resin cements. The aim of this study was to determine the peak temperatures during the polymerization reaction in PMMA bone cement by thermocouple (temperature sensor). The CNTs were uniformly dispersed in the cement matrix. The setting time of the cement increased and the maximum temperature during exothermic polymerization reaction was effectively reduced by the incorporation of functionalized CNTs. This reduction decreased thermal necrosis of the respective nanocomposite cements, which probably could reduce the hyperthermia experienced in vivo

Offline Equilibrium Finding

Offline reinforcement learning (Offline RL) is an emerging field that has recently begun gaining attention across various application domains due to its ability to learn behavior from earlier collected datasets. Using logged data is imperative when further interaction with the environment is expensive (computationally or otherwise), unsafe, or entirely unfeasible. Offline RL proved very successful, paving a path to solving previously intractable real-world problems, and we aim to generalize this paradigm to a multi-agent or multiplayer-game setting. Very little research has been done in this area, as the progress is hindered by the lack of standardized datasets and meaningful benchmarks. In this work, we coin the term offline equilibrium finding (OEF) to describe this area and construct multiple datasets consisting of strategies collected across a wide range of games using several established methods. We also propose a benchmark method -- an amalgamation of a behavior-cloning and a model-based algorithm. Our two model-based algorithms -- OEF-PSRO and OEF-CFR -- are adaptations of the widely-used equilibrium finding algorithms Deep CFR and PSRO in the context of offline learning. In the empirical part, we evaluate the performance of the benchmark algorithms on the constructed datasets. We hope that our efforts may help to accelerate research in large-scale equilibrium finding. Datasets and code are available at https://github.com/SecurityGames/oef

Leveraging Team Correlation for Approximating Equilibrium in Two-Team Zero-Sum Games

Two-team zero-sum games are one of the most important paradigms in game theory. In this paper, we focus on finding an unexploitable equilibrium in large team games. An unexploitable equilibrium is a worst-case policy, where members in the opponent team cannot increase their team reward by taking any policy, e.g., cooperatively changing to other joint policies. As an optimal unexploitable equilibrium in two-team zero-sum games, correlated-team maxmin equilibrium remains unexploitable even in the worst case where players in the opponent team can achieve arbitrary cooperation through a joint team policy. However, finding such an equilibrium in large games is challenging due to the impracticality of evaluating the exponentially large number of joint policies. To solve this problem, we first introduce a general solution concept called restricted correlated-team maxmin equilibrium, which solves the problem of being impossible to evaluate all joint policy by a sample factor while avoiding an exploitation problem under the incomplete joint policy evaluation. We then develop an efficient sequential correlation mechanism, and based on which we propose an algorithm for approximating the unexploitable equilibrium in large games. We show that our approach achieves lower exploitability than the state-of-the-art baseline when encountering opponent teams with different exploitation ability in large team games including Google Research Football

Identifying potential RNAi targets in grain aphid (Sitobion avenae F.) based on transcriptome profiling of its alimentary canal after feeding on wheat plants

BACKGROUND: The grain aphid (Sitobion avenae F.) is a major agricultural pest which causes significant yield losses of wheat in China, Europe and North America annually. Transcriptome profiling of the grain aphid alimentary canal after feeding on wheat plants could provide comprehensive gene expression information involved in feeding, ingestion and digestion. Furthermore, selection of aphid-specific RNAi target genes would be essential for utilizing a plant-mediated RNAi strategy to control aphids via a non-toxic mode of action. However, due to the tiny size of the alimentary canal and lack of genomic information on grain aphid as a whole, selection of the RNAi targets is a challenging task that as far as we are aware, has never been documented previously. RESULTS: In this study, we performed de novo transcriptome assembly and gene expression analyses of the alimentary canals of grain aphids before and after feeding on wheat plants using Illumina RNA sequencing. The transcriptome profiling generated 30,427 unigenes with an average length of 664 bp. Furthermore, comparison of the transcriptomes of alimentary canals of pre- and post feeding grain aphids indicated that 5490 unigenes were differentially expressed, among which, diverse genes and/or pathways were identified and annotated. Based on the RPKM values of these unigenes, 16 of them that were significantly up or down-regulated upon feeding were selected for dsRNA artificial feeding assay. Of these, 5 unigenes led to higher mortality and developmental stunting in an artificial feeding assay due to the down-regulation of the target gene expression. Finally, by adding fluorescently labelled dsRNA into the artificial diet, the spread of fluorescence signal in the whole body tissues of grain aphid was observed. CONCLUSIONS: Comparison of the transcriptome profiles of the alimentary canals of pre- and post-feeding grain aphids on wheat plants provided comprehensive gene expression information that could facilitate our understanding of the molecular mechanisms underlying feeding, ingestion and digestion. Furthermore, five novel and effective potential RNAi target genes were identified in grain aphid for the first time. This finding would provide a fundamental basis for aphid control in wheat through plant mediated RNAi strategy

Pulsed ultrasound-modulated optical tomography using spectral hole-burning

We present a novel optical quantum sensor using spectral hole-burning for detecting signals in ultrasound-modulated optical tomography. In this technique, we utilize the capability of sub-MHz spectral filtering afforded by a spectral hole burning crystal to select the desired spectral component from the ultrasound-modulated diffuse light. This technique is capable of providing a large etendue, processing a large number of speckles in parallel, tolerating speckle decorrelation, and imaging in real-time. Experimental results are presented

1-Methyl­hydrazinium picrate

In the title salt, CH7N2 +·C6H2N3O7 −, the dihedral angles between the three nitro groups and the plane of the benzene ring are 22.4 (2), 35.3 (2) and 2.8 (2)°. In the crystal, the components are linked by N—H⋯O and N—H⋯N hydrogen bonds into a two-dimensional network parallel to (10)

Burgverein Gösting

Blick zur Burgruine Göstin