Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems

Enamel matrix derivative improves gingival fibroblast cell behavior cultured on titanium surfaces.

OBJECTIVE Although an extensive amount of research has demonstrated the positive effects of an enamel matrix derivative (EMD) on soft tissue wound healing around intrabony defects, little information is available describing its effect on peri-implant soft tissues, an area that has recently gained tremendous awareness due to the increasing prevalence of peri-implantitis. The aim of the present study was to assess the role of EMD when gingival fibroblasts were cultured on titanium surface with different surface topographies. METHODS Human primary gingival fibroblasts were cultured on pickled (PT) and sand-blasted with large grit followed by acid etching (SLA) surfaces and assessed for cell adhesion at 2, 4, and 8 h, cell morphology at 2, 4, 8, and 24 h as well as cell proliferation at 1, 3, and 5 days post-seeding. Furthermore, genes encoding collagen 1a1, vascular endothelial growth factor-A (VEGF-A), and fibronectin were assessed by real-time PCR. Human gingival fibroblasts were also quantified for their ability to synthesize a collagen matrix on the various titanium surfaces with and without EMD by immunofluorescence staining. RESULTS The results from the present study demonstrate that EMD significantly increased cell spreading at 2, 4, 8, and 24 h on PT surfaces and 4, 8, and 24 h on SLA surfaces. Furthermore, proliferation at 5 days on PT surfaces and 3 and 5 days on SLA surfaces was also increased for groups containing EMD. Real-time PCR results demonstrated that the culture of gingival fibroblasts with EMD significantly increased extracellular matrix synthesis of collagen 1 as well as improved mRNA levels of VEGF-A and fibronectin. Collagen1 immuno-fluorescent staining revealed a significantly higher area of staining for cells seeded on PT + EMD at 7 and 14 days and 14 days for SLA + EMD when compared to control samples. CONCLUSION The results from the present study favor the use of EMD for colonization of gingival fibroblasts on titanium surfaces by increasing cell growth, spreading, and synthesis of an extracellular matrix. The improvements were primarily irrespective of surface topography. Future animal and human studies are necessary to fully characterize the beneficial effects of incorporating EMD during soft tissue regeneration of implant protocols. CLINICAL RELEVANCE The use of EMD may speed up the quality of soft tissue integration around dental implants by facilitating gingival cell attachment, proliferation, and matrix synthesis of collagen 1

Plant volatile analogues strengthen attractiveness to insect

Green leaf bug Apolygus lucorum (Meyer-Dür) is one of the major pests in agriculture. Management of A. lucorum was largely achieved by using pesticides. However, the increasing population of A. lucorum since growing Bt cotton widely and the increased awareness of ecoenvironment and agricultural product safety makes their population-control very challenging. Therefore this study was conducted to explore a novel ecological approach, synthetic plant volatile analogues, to manage the pest. Here, plant volatile analogues were first designed and synthesized by combining the bioactive components of β-ionone and benzaldehyde. The stabilities of β-ionone, benzaldehyde and analogue 3 g were tested. The electroantennogram (EAG) responses of A. lucorum adult antennae to the analogues were recorded. And the behavior assay and filed experiment were also conducted. In this study, thirteen analogues were acquired. The analogue 3 g was demonstrated to be more stable than β-ionone and benzaldehyde in the environment. Many of the analogues elicited EAG responses, and the EAG response values to 3 g remained unchanged during seven-day period. 3 g was also demonstrated to be attractive to A. lucorum adults in the laboratory behavior experiment and in the field. Its attractiveness persisted longer than β-ionone and benzaldehyde. This indicated that 3 g can strengthen attractiveness to insect and has potential as an attractant. Our results suggest that synthetic plant volatile analogues can strengthen attractiveness to insect. This is the first published study about synthetic plant volatile analogues that have the potential to be used in pest control. Our results will support a new ecological approach to pest control and it will be helpful to ecoenvironment and agricultural product safety

Bone grafting material in combination with Osteogain for bone repair: a rat histomorphometric study.

OBJECTIVES Enamel matrix derivative (EMD) has been successfully used for the regeneration of periodontal tissues including new cementum, periodontal ligament, and alveolar bone. Combination of EMD with bone grafting materials has however generated variable clinical results. Recently, we have demonstrated that a new formulation of EMD in a liquid carrier system (Osteogain®) has improved physicochemical properties for the adsorption of EMD to a bone grafting material. The aim of the present study was to investigate the regenerative potential of Osteogain®, in combination with a bone graft, on new bone formation in a rat femur defect model. MATERIALS AND METHODS Fifty-four critically sized femur defects (3 mm in diameter) were created bilaterally in 27 rats and treated following the group allocation: (1) drilled unfilled control, (2) a natural bone mineral (NBM), and (3) NBM + Osteogain®. All defects were histologically analyzed at 2, 4, and 8 weeks after surgical intervention. Micro-CT analysis, hematoxylin and eosin (H&E) staining, and Safranin O staining were performed to quantify new bone formation. RESULTS Significantly more new bone formation was observed in defects treated with NBM + Osteogain® at both 4 and 8 weeks when compared to NBM alone and the control unfilled defects (P < 0.05). Histologically, the formation of more mature mineralized bone with the presence of osteocytes were found more commonly in defects treated with Osteogain® + NBM at 8 weeks post-healing when compared to NBM alone. CONCLUSIONS The present study demonstrate that Osteogain® in combination with a bone grafting material improves the speed and quality of new bone formation in rat osseous defects. CLINICAL RELEVANCE Future clinical research are now warranted to fully characterize the benefits of Osteogain®, a new formulation of enamel matrix proteins delivered in liquid formation when used in combination with a bone grafting material

Grassland Degradation and Recovery Based on Remote Sensing and GIS in Inner Mongolia

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Relevance-based Word Embedding

Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.Comment: to appear in the proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17

(E)-N-{(E)-2-[(3,5-Dimethylbiphenyl-4-yl)imino]­acenaphthen-1-yl­idene}-2,6-di­methyl-4-phenyl­aniline

The title compound, C40H32N2, has crystallographic twofold rotation symmetry, with two C atoms lying on the axis. The dihedral angle between the two benzene rings of the 4-phenyl-2,6-dimethyl­phenyl group is 35.74 (17)°. The acenaphthene ring makes an angle of 76.93 (11)° with the benzene ring bonded to the N atom and an angle of 41.53 (13)° with the other benzene ring