We present a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a -class flare within the next 24 hr. We consider three classes, namely ≥M5.0 class, ≥M ...class, and ≥C class, and build three LSTM models separately, each corresponding to a class. Each LSTM model is used to make predictions of its corresponding -class flares. The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples. Each data sample has 40 features including 25 magnetic parameters obtained from the Space-weather HMI Active Region Patches and related data products as well as 15 flare history parameters. We survey the flare events that occurred from 2010 May to 2018 May, using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and select flares with identified ARs in the NCEI flare catalogs. These flare events are used to build the labels (positive versus negative) of the data samples. Experimental results show that (i) using only 14-22 most important features including both flare history and magnetic parameters can achieve better performance than using all 40 features together; (ii) our LSTM network outperforms related machine-learning methods in predicting the labels of the data samples. To our knowledge, this is the first time that LSTMs have been used for solar-flare prediction.
Adverse space-weather effects can often be traced to solar flares, the prediction of which has drawn significant research interests. The Helioseismic and Magnetic Imager (HMI) produces full-disk ...vector magnetograms with continuous high cadence, while flare prediction efforts utilizing this unprecedented data source are still limited. Here we report results of flare prediction using physical parameters provided by the Space-weather HMI Active Region Patches (SHARP) and related data products. We survey X-ray flares that occurred from 2010 May to 2016 December and categorize their source regions into four classes (B, C, M, and X) according to the maximum GOES magnitude of flares they generated. We then retrieve SHARP-related parameters for each selected region at the beginning of its flare date to build a database. Finally, we train a machine-learning algorithm, called random forest (RF), to predict the occurrence of a certain class of flares in a given active region within 24 hr, evaluate the classifier performance using the 10-fold cross-validation scheme, and characterize the results using standard performance metrics. Compared to previous works, our experiments indicate that using the HMI parameters and RF is a valid method for flare forecasting with fairly reasonable prediction performance. To our knowledge, this is the first time that RF has been used to make multiclass predictions of solar flares. We also find that the total unsigned quantities of vertical current, current helicity, and flux near the polarity inversion line are among the most important parameters for classifying flaring regions into different classes.
Overcoming barriers to circular product design Wang, Jason X.; Burke, Haydn; Zhang, Abraham
International journal of production economics,
January 2022, 2022-01-00, Volume:
243
Journal Article
Peer reviewed
Open access
The circular economy concept provides sustainability research with a new vision in place of the present linear economic model. This study focuses on product design, the starting point of applying ...circular thinking in supply chain functions. We investigate barriers to circular product design from a stakeholder perspective. Using thematic analysis and data collected from 15 semi-structured interviews in New Zealand, we identify four prominent barriers: financial constraints, inadequate infrastructure, government inaction, and global market barriers. The most influential stakeholder classes for overcoming the barriers are consumers, industry leaders, and governments. Circumventing measures lie in sustainable end-of-life product and waste management, resource circularity, modularity and standardization in design, and supply chain collaboration. Based on these new insights, we develop a roadmap for circular product design, providing practical guidance for businesses and policymakers. We also add to research on stakeholder theory by exploring its descriptive aspect in the context of a transition to circular economy at the supply chain level.
Ethics and governance for digital disease surveillance Mello, Michelle M; Wang, C Jason
Science (American Association for the Advancement of Science),
2020-May-29, 2020-05-29, 20200529, Volume:
368, Issue:
6494
Journal Article
Peer reviewed
The question is not whether to use new data sources but how
Digital epidemiology—the use of data generated outside the public health system for disease surveillance—has been in use for more than a ...quarter century see supplementary materials (SM). But several countries have taken digital epidemiology to the next level in responding to COVID-19. Focusing on core public health functions of case detection, contact tracing, and isolation and quarantine, we explore ethical concerns raised by digital technologies and new data sources in public health surveillance during epidemics. For example, some have voiced concern that trust and participation in such approaches may be unevenly distributed across society; others have raised privacy concerns. Yet counterbalancing such concerns is the argument that “sometimes it is unethical
not
to use available data” (
1
); some trade-offs may be not only ethically justifiable but ethically obligatory. The question is not whether to use new data sources—such as cellphones, wearables, video surveillance, social media, internet searches and news, and crowd-sourced symptom self-reports—but how.
Abstract
Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a ...deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation.
Circular economy (CE) provides an alternative development model to the dominant take-make-dispose linear approach, and thus a new vision for solving sustainability challenges. Firms need to ...operationalise CE in their supply chain operations, starting from circular product design as the foundational step. The purpose of this paper is to investigate how to integrate product design and supply chain management (SCM) decisions for a CE transition. A thematic analysis was conducted on data collected from 15 semi-structured interviews in New Zealand. Four propositions were established based on the identified themes, namely, end-of-life thinking in product design, circular SCM, business model innovation, and sustainable organisational values. The study results provide a novel insight into the integration of product design and SCM for a CE transition. The operational framework developed provides guidance to product designers, managers, and researchers to advance the CE cause at the supply chain level.
Abstract
Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ...ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a
$$\gamma$$
γ
-class flare within the next 24 to 72 h. We consider three
$$\gamma$$
γ
classes, namely the
$$\ge$$
≥
M5.0 class, the
$$\ge$$
≥
M class and the
$$\ge$$
≥
C class, and build three transformers separately, each corresponding to a
$$\gamma$$
γ
class. Each transformer is used to make predictions of its corresponding
$$\gamma$$
γ
-class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.
Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve ...physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016. Logged EHR timestamps were extracted from Stanford Hospital's EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift's end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation = 2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions.
•This paper reviews practices and research in circular supply chain management.•Circular supply chain management is established as a multi-dimensional concept.•Five noticeable research-practice gaps ...are identified from the comparative review.•Eight promising research directions are discussed in circular supply chain management.
The circular economy (CE) concept has gained wide attention in practice as well as in academia in recent years. This paper reviews the state-of-the-art practices and research in “circular supply chain management” (CSCM), i.e., the integration of CE thinking into supply chain management (SCM) with the goal of achieving “zero wastes”. The review covers 68 real-life CE implementation cases collected by the Ellen MacArthur Foundation and 124 publications in well-established, high-ranking academic journals in operations and supply chain management. The comparative review shows that CSCM encompasses multiple dimensions, including closed-loop SCM, reverse SCM, remanufacturing SCM, recycling SCM, and industrial symbiosis. A multi-dimensional CSCM (MD-CSCM) framework is developed to synthesize their interrelationships and to categorize academic publications into multiple research themes. Based on the identified research-practice gaps and pressing research needs, this study discusses important directions for future studies to advance supply chain circularity.
We propose a new machine-learning approach to Stokes inversion based on a convolutional neural network (CNN) and the Milne-Eddington (ME) method. The Stokes measurements used in this study were taken ...by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory. By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS. Experimental results show that our CNN method produces smoother and cleaner magnetic maps than the widely used ME method. Furthermore, the CNN method is four to six times faster than the ME method and able to produce vector magnetic fields in nearly real time, which is essential to space weather forecasting. Specifically, it takes ∼50 s for the CNN method to process an image of 720 × 720 pixels comprising Stokes profiles of GST/NIRIS. Finally, the CNN-inferred results are highly correlated to the ME-calculated results and closer to the ME's results with the Pearson product-moment correlation coefficient (PPMCC) being closer to 1, on average, than those from other machine-learning algorithms, such as multiple support vector regression and multilayer perceptrons (MLP). In particular, the CNN method outperforms the current best machine-learning method (MLP) by 2.6%, on average, in PPMCC according to our experimental study. Thus, the proposed physics-assisted deep learning-based CNN tool can be considered as an alternative, efficient method for Stokes inversion for high-resolution polarimetric observations obtained by GST/NIRIS.
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