Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, ...turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights
Wide‐scale exploitation of renewable energy requires low‐cost efficient energy storage devices. The use of metal‐free, inexpensive redox‐active organic materials represents a promising direction for ...environmental‐friendly, cost‐effective sustainable energy storage. To this end, a liquid battery is designed using hydroquinone (H2BQ) aqueous solution as catholyte and graphite in aprotic electrolyte as anode. The working potential can reach 3.4 V, with specific capacity of 395 mA h g−1 and stable capacity retention about 99.7 % per cycle. Such high potential and capacity is achieved using only C, H and O atoms as building blocks for redox species, and the replacement of Li metal with graphite anode can circumvent potential safety issues. As H2BQ can be extracted from biomass directly and its redox reaction mimics the bio‐electrochemical process of quinones in nature, using such a bio‐inspired organic compound in batteries enables access to greener and more sustainable energy‐storage technology.
Versatile energy‐storage devices: The use of metal‐free redox‐active organic materials is promising for the development of environmental‐friendly, cost‐effective, and sustainable energy‐storage devices. As hydroquinone can be extracted from biomass directly, using such a bio‐inspired organic compound in batteries enables access to green and sustainable energy‐storage technology.
The Woodbury formula is an efficient tool in mathematics to calculate low‐rank perturbation problems and has been applied to improve the computational efficiency of nonlinear seismic response ...analysis (NSRA) of structures with local nonlinearity. Using the Woodbury formula for NSRA can avoid the time‐consuming recalculation and factorization of the large‐dimensional global stiffness matrix of a structure by only solving a small‐dimensional Schur complement system representing local nonlinearity per iteration. Because the dimension of the Schur complement matrix is determined by the inelastic degree of freedom (IDOF) number, which represents the scale of local nonlinear regions, a small IDOF number is helpful for achieving the high‐efficiency advantage of the Woodbury formula. However, when performing NSRA for large‐scale structures, the IDOF number is usually relatively large, which contradicts the efficiency requirement of the Woodbury formula. To solve this problem and extend the advantage of the Woodbury formula to the NSRA of large‐scale structures, this paper first proposes a two‐stage IDOF number reduction method by eliminating the IDOFs that have insignificant effects on the results, and consequently, a variant Woodbury formula is derived. Because only the principal component in the Schur complement matrix is retained, the dimension of this matrix and the cost for factorizing it can be reduced significantly without losing accuracy, thus greatly improving the efficiency of the proposed method. Moreover, to reduce the additional computational time introduced by the IDOF number reduction procedure and to further improve the computational performance of the proposed method, an OpenMP parallel computational strategy is incorporated. Finally, the validity of the proposed method is verified by implementing incremental dynamic analysis for a large‐scale reinforced concrete structure.
The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Here, we profiled host responses to ...COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients, including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine-learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by enzyme-linked immunosorbent assay (ELISA) using a larger cohort. These markedly altered proteins, including the biomarkers, mediate pathophysiological pathways, such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers and shed light on the pathogenesis and potential therapeutic targets of COVID-19.
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•We profile plasma proteomics of COVID-19 cases at distinct symptoms and time points•The alterations of host plasma proteins are linked with COVID-19 development•Machine-learning-based models distinguish patients with different severity•Biomarker combinations show the power to predict COVID-19 clinical outcomes
Proteomic quantifications and experimental validation of plasma samples from three cohorts of COVID-19 patients with distinct symptoms at different time points identify differentially expressed host proteins that correlate with disease severity and prioritize biomarker combinations for accurately predicting COVID-19 clinical outcomes.
Electrocatalytic nitrogen reduction reaction (NRR) under ambient conditions provides an intriguing picture for the conversion of N2 into NH3. However, electrocatalytic NRR mainly relies on ...metal‐based catalysts, and it remains a grand challenge in enabling effective N2 activation on metal‐free catalysts. Here we report a defect engineering strategy to realize effective NRR performance (NH3 yield: 8.09 μg h−1 mg−1cat., Faradaic efficiency: 11.59 %) on metal‐free polymeric carbon nitride (PCN) catalyst. Illustrated by density functional theory calculations, dinitrogen molecule can be chemisorbed on as‐engineered nitrogen vacancies of PCN through constructing a dinuclear end‐on bound structure for spatial electron transfer. Furthermore, the N−N bond length of adsorbed N2 increases dramatically, which corresponds to “strong activation” system to reduce N2 into NH3. This work also highlights the significance of defect engineering for improving electrocatalysts with weak N2 adsorption and activation ability.
Carbon nitride materials: An efficient metal‐free polymeric carbon nitride catalyst has been obtained by a defect‐engineering strategy for the electrocatalyic ammonia synthesis. The metal‐free catalyst has a high density of nitrogen vacancies which are as active sites responsible for chemisorption and activation of N2 and lead to a high NH3 yield and Faradaic efficiency.
Interface modification plays an important role in improving the power conversion efficiency (PCE) of organic solar cells (OSCs). However, the low non‐covalent interaction between the cathode ...interface layer (CIL) and nonfullerene acceptor (NFA) directly affects the charge collection of OSCs. Here, the non‐covalent interaction between the CIL and NFA is enhanced by introducing the 2D vermiculite (VML) in the poly(9,9‐bis(3′‐(N,N‐dimethyl)‐Nethylammonium‐propyl‐2,7‐fluorene)‐alt‐2,7‐(9,9‐dioctylfluorene)) dibromide (PFN‐Br) interface layer to form an efficient electron transport channel. As a result, the electron extraction efficiency from the active layer to the CIL is increased, and the PCE of OSCs based on PBDB‐T:ITIC is boosted from 10.87% to 12.89%. In addition, the strategy of CIL doping VML is proven to be universal in different CIL materials, for which the PCE is boosted from 10.21% to 11.57% for OSCs based on PDINN and from 9.82% to 11.27% for OSCs based on PNDIT‐F3N. The results provide a viable option for designing efficient CIL for high‐performance non‐fullerene OSCs, which may promote the commercialization of OSCs.
The non‐covalent interaction between the cathode interface layer and nonfullerene acceptor is enhanced by introducing vermiculite in the PFN‐Br interlayer. As a result, the power conversion efficiency of organic solar cells based on PBDB‐T:ITIC is boosted from 10.87% to 12.89%.
Abstract
The two-dimensional topological insulators host a full gap in the bulk band, induced by spin–orbit coupling (SOC) effect, together with the topologically protected gapless edge states. ...However, it is usually challenging to suppress the bulk conductance and thus to realize the quantum spin Hall (QSH) effect. In this study, we find a mechanism to effectively suppress the bulk conductance. By using the quasiparticle interference technique with scanning tunneling spectroscopy, we demonstrate that the QSH candidate single-layer 1
T
’-WTe
2
has a semimetal bulk band structure with no full SOC-induced gap. Surprisingly, in this two-dimensional system, we find the electron–electron interactions open a Coulomb gap which is always pinned at the Fermi energy (
E
F
). The opening of the Coulomb gap can efficiently diminish the bulk state at the
E
F
and supports the observation of the quantized conduction of topological edge states.
Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: (1) lack of aligned training pairs and (2) multiple possible outputs from ...a single input image. In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images. To synthesize diverse outputs, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. Our model takes the encoded content features extracted from a given input and attribute vectors sampled from the attribute space to synthesize diverse outputs at test time. To handle unpaired training data, we introduce a cross-cycle consistency loss based on disentangled representations. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks without paired training data. For quantitative evaluations, we measure realism with user study and Fréchet inception distance, and measure diversity with the perceptual distance metric, Jensen–Shannon divergence, and number of statistically-different bins.
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