Anisotropic Deformation in the Compressions of Single Crystalline Copper Nanoparticles

Atomistic simulations are performed to probe the anisotropic deformation in the compressions of face-centred-cubic metallic nanoparticles. In the elastic regime, the compressive load-depth behaviors can be characterized by the classical Hertzian model or flat punch model, depending on the surface configuration beneath indenter. On the onset of plasticity, atomic-scale surface steps serve as the source of heterogeneous dislocation in nanoparticle, which is distinct from indenting bulk materials. Under [111] compression, the gliding of jogged dislocation takes over the dominant plastic deformation. The plasticity is governed by nucleation and exhaustion of extended dislocation ribbons in [110] compression. Twin boundary migration mainly sustain the plastic deformation under [112] compression. This study is helpful to extract the mechanical properties of metallic nanoparticles and understand their anisotropic deformation behaviors.Comment: 25 pages, 9 figure

Epidemiologic parameters and evaluation of control measure for 2009 novel influenza a (H1N1) in Xiamen, Fujian Province, China

<p>Abstract</p> <p>Background</p> <p>Containment of influenza A H1N1 virus spread was implemented successfully in Xiamen, with large-scale inoculation to reduce morbidity. To identify beneficial elements and to guide decision-making in epidemic containment, we analyzed the epidemiologic parameters and evaluated the control measures.</p> <p>Method</p> <p>We determined various parameters from laboratory-confirmed cases, including incubation period, duration of illness and reproductive number (R₀), and evaluated the control measures.</p> <p>Results</p> <p>There were1414 cases with dates of onset between June 14, 2009 and March 22, 2010. The incidence was 56.79/100,000, and mortality was 0.12/100,000. The incidence during the community epidemic phase was 6.23 times higher than in the containment phase. A total of 296,888 subjects were inoculated with domestic influenza H1N1 virus cleavage vaccine. An epidemic curve showed that vaccination in students cut the peak incidence of illness significantly. Men (relative risk (RR) = 1.30, 95% confidence interval (CI): 1.17-1.45) and persons aged 0-14 years were at greater risk of infection. The incidence increased with younger age (<it>χ</it>²= 950.675, <it>p </it>= ∞). Morbidity was lower in urban than in rural areas (RR = 0.56, 95%CI: 0.50-0.62). The median incubation time was 2 days, median duration of symptoms was 7 days, and the within-school reproductive number was 1.35.</p> <p>Conclusion</p> <p>Our analysis indicated that the characteristics of this novel influenza virus were similar to those of seasonal influenza. The principle of "interception of imported cases" applied at Xiamen ports, and vaccination of students effectively limited the spread of the influenza pandemic and reduced the epidemic peak.</p

Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model

Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us to predict the formation energy with sufficient precision of a large number of materials of which only the structural information is available. The enlarged training set is then used to train a convolutional neural network model (the screening model) with the generic Magpie elemental features with high prediction power. Extensive experiments demonstrate the superior performance of our transfer learning model and screening model compared to the baseline models. We then applied the screening model to filter out promising new perovskite materials out of 21,316 hypothetical perovskite structures with a large portion of them confirmed by existing literature

Sustained oxygenation accelerates diabetic wound healing by promoting epithelialization and angiogenesis and decreasing inflammation

Nonhealing diabetic wounds are common complications for diabetic patients. Because chronic hypoxia prominently delays wound healing, sustained oxygenation to alleviate hypoxia is hypothesized to promote diabetic wound healing. However, sustained oxygenation cannot be achieved by current clinical approaches, including hyperbaric oxygen therapy. Here, we present a sustained oxygenation system consisting of oxygen-release microspheres and a reactive oxygen species (ROS)-scavenging hydrogel. The hydrogel captures the naturally elevated ROS in diabetic wounds, which may be further elevated by the oxygen released from the administered microspheres. The sustained release of oxygen augmented the survival and migration of keratinocytes and dermal fibroblasts, promoted angiogenic growth factor expression and angiogenesis in diabetic wounds, and decreased the proinflammatory cytokine expression. These effects significantly increased the wound closure rate. Our findings demonstrate that sustained oxygenation alone, without using drugs, can heal diabetic wounds

Characteristics of carbon sources and sinks and their relationships with climate factors during the desertification reversal process in Yulin, China

Research on carbon sources/sinks in desert ecosystems is of great importance to understand the carbon cycle and its response to climate change. Net primary productivity (NPP) and net ecosystem productivity (NEP) are the two most important indictors for quantitatively evaluating carbon storage and can be used to indicate the response of terrestrial ecosystems to climate change. In this study, we used remote sensing data, meteorological data and vegetation type data to estimate the NPP and NEP using CASA model and soil respiration model from 2000 to 2020 in the region of Yulin, which is a typical desertification reversal region in the Mu Us Sandy Land. The spatial and temporal features of the NPP and NEP and their relationships with temperature and precipitation were determined. The results showed that both the annual NPP and NEP showed an increasing trend from 2000 to 2020 in the region of Yulin, where the terrestrial ecosystem acted as a carbon source until 2001 but turned into a sink thereafter. The carbon storage showed an increasing trend with a rate of 0.50 Tg C·a−1 from 2000 to 2020. Both the mean annual NPP and the total NEP increased from the west to the east of the region in spatial distribution. The total NEP indicated that the area with a carbon sink accounted for 89.22% of the total area, showing a carbon accumulation of 103.0 Tg C, and the carbon source area accounted for 10.78% of the total area with a carbon emission of 4.40 Tg C. The net carbon sequestration was 99.44 Tg C in the region of Yulin during the period from 2000 to 2020. Temperature had no significant effects on NPP and NEP for most areas of the region, while precipitation had a positive effect on the increasing NPP in 75.3% of areas and NEP in 30.07% of areas of the region. These results indicated that it is of utmost significance to protect terrestrial ecosystems from degradation, and ecological restoration projects are essential in combating desertification, which would be helpful for soil water conservation and could effectively increase carbon storage in desert ecosystems

Collaborative Distributed Scheduling Approaches for Wireless Sensor Network

Energy constraints restrict the lifetime of wireless sensor networks (WSNs) with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs) based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes' energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs