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Construction projects face various constraints in terms of materials, labor, equipment, and documents, which can interrupt the scheduled work. Package‐based constraint management (PCM) is a state‐of‐the‐art graph‐based approach that follows the lean theory to effectively model, monitor, and remove constraints before the commencement of work, ensuring smooth construction and minimizing delay and waste. PCM relies on exploring and investigating project knowledge bases (KBs), formed by entity‐relation‐entity triples of constraints. However, most PCM KBs are incomplete and suffer from poor semantics, which hinders the PCM functions. Although many KB completion (KBC) methods exist in the field of artificial intelligence, they primarily focus on general knowledge and exclude the features of specific domains. Therefore, they cannot be directly applied to complete PCM KBs. To address the issue, this study proposes a novel deep learning model, referred to as the domain information enhanced graph neural network (D‐GNN). The features of the developed D‐GNN include (1) building a domain ontology to enrich semantics with rule reasoning, (2) applying the GNN to learn and encode embeddings of constraint entities and relations, and (3) employing a convolution neural network (CNN) for decoding and identifying missing triples. D‐GNN improves the existing KBC methods by integrating two types of domain information, namely, the ontological classes and working contexts into GNN and CNN, respectively. The experimental results verified that the D‐GNN reached an accuracy of 0.848–0.951, and the domain information integration increased the performance by up to 0.263. In practical testing, the D‐GNN significantly reduced the KBC time to 1/6–1/35 of the manual approach and reached higher accuracy. Therefore, the proposed D‐GNN can facilitate PCM by providing complete KBs and supporting downstream constraint monitoring and removal. [ABSTRACT FROM AUTHOR]

The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real‐world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include time‐series‐based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto‐encoder‐based approaches. In addition, future development directions of deep‐learning‐based wind energy forecasting have also been discussed. [ABSTRACT FROM AUTHOR]

This paper explores the wake of a wind turbine yaw and its influence on the output power of the downstream wind turbine. A wind turbine model (hub height and diameter of 1.2 m and 1.0 m, respectively) was used in the experiments, and the yaw angle of the upstream wind turbine was successively varied through values of 0°, 15°, and 30°. The wind speed and the intensity of turbulence in the wake region were measured using a hot‐wire anemometer. The measuring positions were changed to 3, 5, and 7 rotor diameters behind the upstream wind turbine. In addition, the output power of the downstream wind turbine model was measured using the rotational speed torque meter. The results show that, in the case of large‐angle yaw of the upstream wind turbine, the recovery of the wind speed in the wake area was greatly improved, and up to 30% recovery was achieved. The power output of the downstream wind turbine was also significantly increased. Under the yaw conditions of 15° and 30° for the upstream wind turbine, the average output power of the downstream wind turbine increased by 17.6% and 21.6%, respectively. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the effects of the novel nanopore Ti surface and amine-terminated Ti surface on peri-implant bone formation and bone-implant bonding strength in a rabbit tibiae model. Characterization of the modified Ti surface, such as root-mean square roughness, contact angle, and surface energy was evaluated using scanning electron microscopy, atomic force microscopy, and a Contact Angle Measurement System, respectively. The results from alkali treatment indicated that an interpenetrating dimensional porosity surface had been formed, whose morphology was similar to the amine-terminated surface of self-assembled monolayers after alkali treatment. The micro-CT demonstrated that the amine-terminated surface had higher bone volume ratio and higher mean trabecular thickness after 4 weeks of implantation. The push-out test and histological examination showed no significant differences in the maximal force and bone-implant contact between the terminated surface and alkali-treated surface. These novel dimensional nanoporous structures can significantly improve the initial stabilization and the osseointegration. © 2012 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 100A:3428-3435, 2012. [ABSTRACT FROM AUTHOR]

Background: The amino acid response (AAR) is an evolutionarily conserved protective mechanism activated by amino acid deficiency through a key kinase, general control nonderepressible 2. In addition to mobilizing amino acids, the AAR broadly affects gene and protein expression in a variety of pathways and elicits antifibrotic, autophagic, and anti-inflammatory activities. However, little is known regarding its role in cardiac stress. Our aim was to investigate the effects of halofuginone, a prolyl-tRNA synthetase inhibitor, on the AAR pathway in cardiac fibroblasts, cardiomyocytes, and in mouse models of cardiac stress and failure., Methods and Results: Consistent with its ability to inhibit prolyl-tRNA synthetase, halofuginone elicited a general control nonderepressible 2-dependent activation of the AAR pathway in cardiac fibroblasts as evidenced by activation of known AAR target genes, broad regulation of the transcriptome and proteome, and reversal by l-proline supplementation. Halofuginone was examined in 3 mouse models of cardiac stress: angiotensin II/phenylephrine, transverse aortic constriction, and acute ischemia reperfusion injury. It activated the AAR pathway in the heart, improved survival, pulmonary congestion, left ventricle remodeling/fibrosis, and left ventricular function, and rescued ischemic myocardium. In human cardiac fibroblasts, halofuginone profoundly reduced collagen deposition in a general control nonderepressible 2-dependent manner and suppressed the extracellular matrix proteome. In human induced pluripotent stem cell-derived cardiomyocytes, halofuginone blocked gene expression associated with endothelin-1-mediated activation of pathologic hypertrophy and restored autophagy in a general control nonderepressible 2/eIF2α-dependent manner., Conclusions: Halofuginone activated the AAR pathway in the heart and attenuated the structural and functional effects of cardiac stress., (© 2017 The Authors and GlaxoSmithKline. Published on behalf of the American Heart Association, Inc., by Wiley.)