Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, ...aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem.
In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren–Miner index) in composite materials by processing the signals of Lamb Waves – a non-destructive SHM technique – with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude.
The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an online learning context where streams of data are fed continuously to the model, avoids repeated training—essential for reliable real-time, continuous monitoring.
•This algorithm automatically determines all the hyperparameters involved in the prediction.•A maximum entropy algorithm is proposed and no training is required.•Compared with hypertuned ML models the results have a similar level of accuracy.
Climate change alters surface water availability (WA; precipitation minus evapotranspiration, P − ET) and consequently impacts agricultural production and societal water needs, leading to increasing ...concerns on the sustainability of water use. Although the direct effects of climate change on WA have long been recognized and assessed, indirect climate effects occurring through adjustments in terrestrial vegetation are more subtle and not yet fully quantified. To address this knowledge gap, here we investigate the interplay between climate‐induced changes in leaf area index (LAI) and ET and quantify its ultimate effect on WA during the period 1982–2016 at the global scale, using an ensemble of data‐driven products and land surface models. We show that ~44% of the global vegetated land has experienced a significant increase in growing season‐averaged LAI and climate change explains 33.5% of this greening signal. Such climate‐induced greening has enhanced ET of 0.051 ± 0.067 mm year−2 (mean ± SD), further amplifying the ongoing increase in ET directly driven by variations in climatic factors over 36.8% of the globe, and thus exacerbating the decline in WA prominently in drylands. These findings highlight the indirect impact of positive feedbacks in the land–climate system on the decline of WA, and call for an in‐depth evaluation of these phenomena in the design of local mitigation and adaptation plans.
Climate change has indirectly promoted a rise of global terrestrial evapotranspiration through adjustments in vegetation over the past three and a half decades, further amplifying the ongoing increase in evapotranspiration directly driven by variations in climatic factors. Such indirect effects further exacerbate the declining water availability in regions already vulnerable to climate change, particularly arid zones with limited recharge rates, and therefore threaten the water supply for terrestrial ecosystems and rainfed agricultural productions in drylands.
Cross-industry networks of multiple supply chains have evolved in the circular economy model using approaches such as industrial and urban symbiosis. However, the implementation of such sustainable ...industrial networks with matrix-like structures is not straightforward. Despite the clear benefits of big data-driven industrial symbiosis, corporates have noted that social, environmental and economic perspectives are also highly appreciated in the cross-industry networks. Moreover, gaps remain in operational data-driven and recycle, reduce and reuse optimization solutions, which may be the key components of industrial symbiosis practices.
The massive amounts of publication data are highly valuable, because in addition to the advancement in science, technology, and policy, such data can provide critical information and guidance on what ...have been published, what topical changes have evolved, and what are the trending fields deserving more attention. In the 21st century, biochar has played an indispensable role in the long-term global development strategies in response to “Carbon neutralization”, “Agricultural management”, and “Environmental restoration”, and accumulated many high-quality publications. Herein, this study provides a new data-driven bibliometric analysis strategy and framework for mining the core content of massive literature data, and aims at bringing unique insights for the research prospects as well as opportunities of biochar. The results show that biochar researches have made great progress from 1999 to 2020, but cross-disciplinary teamwork should be further emphasized. The research frontier identification reveals that sewage treatment, efficient removal, and functional composite materials will be the issues which must be paid continual attention at present and in the future. Furthermore, studies on global climate impact, biomass resource utilization, carbon sequestration, carbon cycle, and even the negative effects of biochar have gradually begun to be taken seriously.
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•A new data-driven strategy is provided for studying the evolution of biochar field.•Contribution and cooperation are determined via descriptive statistical analysis.•Co-citation and clustering analysis have been used to analyze research hotspots.•The research frontiers are visually explored by time change and timeline analysis.•Improvement outlooks of bibliometrics applying for subject fields are proposed.
The circular supply chain has recently received more attention as a relevant solution to effectively tackle environmental issues while simultaneously achieving resource recovery and circular business ...strategy benefits. This study builds a hierarchical circular supply chain structure from big data including qualitative and quantitative information. This study uses data‐driven analysis to clarify circular supply chain trends and opportunities in practice. A valid hierarchical circular supply chain structure is composed of a big dataset. However, the attributes of the hierarchical circular supply chain structure must be explored to identify the opportunities and challenges of the circular supply chain. A combination of data‐driven content and cluster analysis, including the fuzzy Delphi method, fuzzy decision‐making trials, evaluation laboratories, and the entropy weight method, is utilized to address this gap. The study analyzes a set of five attributes from the literature, and 23 criteria are validated. The results show that resource recovery implementation, Industry 4.0 and digitalization, and reverse supply chain practice pertain to the causal group, while circular business strategy and life cycle sustainability assessment are included in the effect group. The conclusive criteria comprise material efficiency, waste‐to‐energy, machine learning, e‐waste, plastic recycling, and artificial intelligence.
The increasing integration of distributed energy resources calls for new planning and operational tools. However, such tools depend on system topology and line parameters, which may be missing or ...inaccurate in distribution grids. With abundant data, one idea is to use linear regression to find line parameters, based on which topology can be identified. Unfortunately, the linear regression method is accurate only if there is no noise in both the input measurements (e.g., voltage magnitude and phase angle) and output measurements (e.g., active and reactive power). For topology estimation, even with a small error in measurements, the regression-based method is incapable of finding the topology using nonzero line parameters with a proper metric. To model input and output measurement errors simultaneously, we propose the error-in-variables model in a maximum-likelihood estimation framework for joint line parameter and topology estimation. While directly solving the problem is NP-hard, we successfully adapt the problem into a generalized low-rank approximation problem via variable transformation and noise decorrelation. For accurate topology estimation, we let it interact with parameter estimation in a fashion that is similar to expectation-maximization algorithm in machine learning. The proposed PaToPa approach does not require a radial network setting and works for mesh networks. We demonstrate the superior performance in accuracy for our method on IEEE test cases with actual feeder data from Southern California Edison.
Available data on amine‐based sorbents used for the direct air capture (DAC) process were gathered and analyzed to identify the correlations between various aspects of these sorbents and the ...operating conditions they are used in. It is demonstrated that a moderately high temperature (∼ 50 °C) can help with higher CO2 capture capacity. The effect of sorbent preparation method on its activity and stability was studied. Also, the influence of amine groups and support choice on amine efficiency and CO2 capture capacity was discussed. The DAC process conditions proved to play a major role in determining the optimal sorbent. An outlook for characteristics to be sought for in future DAC sorbents for CO2 removal is proposed.
CO2 sorbents are considered key components of the direct air capture (DAC) process. Various types of amines are widely applied for CO2 capture from the atmosphere. Their activities and efficiencies depend on various factors including their support material, operating conditions, and environment. Statistical data analysis is done to provide more insights into the performance of these amines for DAC.
Starting in early 2020, the novel coronavirus disease (COVID-19) severely attached the U.S., causing substantial changes in the operations of bulk power systems and electricity markets. In this ...paper, we develop a data-driven analysis to substantiate the pandemic’s impacts from the perspectives of power system security, electric power generation, electric power demand and electricity prices. Our results suggest that both electric power demand and electricity prices have discernibly dropped during the COVID-19 pandemic. Geographically diverse impacts are observed and quantified, while the bulk power systems and markets in the northeast region are most severely affected. All the data sources, assessment criteria, and analysis codes reported in this paper are available on a GitHub repository.
To study the clinical, serological and histologic features of primary Sjögren's syndrome (pSS) patients with early (young ≤35 years) or late (old ≥65 years) onset and to explore the differential ...effect on lymphoma development.
From a multicentre study population of 1997 consecutive pSS patients, those with early or late disease onset, were matched and compared with pSS control patients of middle age onset. Data driven analysis was applied to identify the independent variables associated with lymphoma in both age groups.
Young pSS patients (19%, n = 379) had higher frequency of salivary gland enlargement (SGE, lymphadenopathy, Raynaud's phenomenon, autoantibodies, C4 hypocomplementemia, hypergammaglobulinemia, leukopenia, and lymphoma (10.3% vs. 5.7%, p = 0.030, OR = 1.91, 95% CI: 1.11-3.27), while old pSS patients (15%, n = 293) had more frequently dry mouth, interstitial lung disease, and lymphoma (6.8% vs. 2.1%, p = 0.011, OR = 3.40, 95% CI: 1.34-8.17) compared to their middle-aged pSS controls, respectively. In young pSS patients, cryoglobulinemia, C4 hypocomplementemia, lymphadenopathy, and SGE were identified as independent lymphoma associated factors, as opposed to old pSS patients in whom SGE, C4 hypocomplementemia and male gender were the independent lymphoma associated factors. Early onset pSS patients displayed two incidence peaks of lymphoma within 3 years of onset and after 10 years, while in late onset pSS patients, lymphoma occurred within the first 6 years.
Patients with early and late disease onset constitute a significant proportion of pSS population with distinct clinical phenotypes. They possess a higher prevalence of lymphoma, with different predisposing factors and lymphoma distribution across time.
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