Many nations have committed to achieving carbon neutrality to combat climate change, but little is known about its drivers at the micro level and implications for firm performance and supply chain ...management. To address the knowledge gap, this research conducts case studies of seven early movers in the initiative by exploring the key drivers, influential stakeholders and effects of institutional pressures. We find four major drivers: ‘customer enforcement’, ‘sustainable business value’, ‘environmental legitimacy’ and ‘competitive pressures’. Customers and competitors were the most influential external stakeholders. Shareholders and top management with intrinsic environmental values, being internal stakeholders, played pivotal roles in a proactive move to carbon neutrality when there was limited regulatory pressure. The early movers believed in the long‐term economic benefits of transitioning to carbon neutrality. We also identify the implications of carbon neutrality initiatives for supply chain management. Based on the research findings, we develop a decision support framework to guide firms in transitioning towards carbon neutrality in a multi‐tier supply chain context.
Predicting the response, or sensitivity, of a clinical drug to a specific cancer type is an important research problem. By predicting the clinical drug response correctly, clinicians are able to ...understand patient-to-patient differences in drug sensitivity outcomes, which in turn results in lesser time spent and lower cost associated with identifying effective drug candidates. Although technological advances in high-throughput drug screening in cells led to the generation of a substantial amount of relevant data, the analysis of such data would be a challenging task. There is a critical need for advanced machine learning (ML) algorithms to generate accurate predictions of clinical drug response. A major goal of this work is to provide advanced ML tools to data analysts, who would in turn build prediction calculators to be incorporated into intelligent clinical decision support systems. Such innovative tools could be used to enhance patient-care, among other uses. To achieve this goal, we develop new ML techniques, including a transfer learning approach coupled with or without a boosting technique. Experimental results on real clinical data pertaining to breast cancer, multiple myeloma, and triple-negative cancer patients demonstrate the effectiveness and superiority of the proposed approaches compared to baseline approaches, including existing transfer learning methods.
•We propose two transfer learning approaches for predicting clinical drug response.•We compare the proposed approaches against baseline approaches including existing transfer learning methods.•We present experimental results for several prediction tasks of clinical drug response.•Our results demonstrate the effectiveness and superiority of the proposed approaches over the baselines.
Reopening colleges and universities during the coronavirus disease 2019 (COVID-19) pandemic poses a special challenge worldwide. Taiwan is one of the few countries where schools are functioning ...normally. To secure the safety of students and staff, the Ministry of Education in Taiwan established general guidelines for college campuses. The guidelines delineated creation of a task force at each university; school-based risk screening based on travel history, occupation, contacts, and clusters; measures on self-management of health and quarantine; general hygiene measures (including wearing masks indoors); principles on ventilation and sanitization; regulations on school assemblies; a process for reporting suspected cases; and policies on school closing and make-up classes. It also announced that a class should be suspended if 1 student or staff member in it tested positive and that a school should be closed for 14 days if it had 2 or more confirmed cases. As of 18 June 2020, there have been 7 confirmed cases in 6 Taiwanese universities since the start of the pandemic. One university was temporarily closed, adopted virtual classes, and quickly reopened after 14 days of contact tracing and quarantine of possible contacts. Taiwan's experience suggests that, under certain circumstances, safely reopening colleges and universities this fall may be feasible with a combination of strategies that include containment (access control with contact tracing and quarantine) and mitigation (hygiene, sanitation, ventilation, and social distancing) practices.
Abstract
We present a new deep-learning method, named FibrilNet, for tracing chromospheric fibrils in H
α
images of solar observations. Our method consists of a data preprocessing component that ...prepares training data from a threshold-based tool, a deep-learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations. The FibrilNet tool is applied to high-resolution H
α
images from an active region (AR 12665) collected by the 1.6 m Goode Solar Telescope (GST) equipped with high-order adaptive optics at the Big Bear Solar Observatory (BBSO). We quantitatively assess the FibrilNet tool, comparing its image segmentation algorithm and fibril-fitting algorithm with those employed by the threshold-based tool. Our experimental results and major findings are summarized as follows. First, the image segmentation results (i.e., the detected fibrils) of the two tools are quite similar, demonstrating the good learning capability of FibrilNet. Second, FibrilNet finds more accurate and smoother fibril orientation angles than the threshold-based tool. Third, FibrilNet is faster than the threshold-based tool and the uncertainty maps produced by FibrilNet not only provide a quantitative way to measure the confidence on each detected fibril, but also help identify fibril structures that are not detected by the threshold-based tool but are inferred through machine learning. Finally, we apply FibrilNet to full-disk H
α
images from other solar observatories and additional high-resolution H
α
images collected by BBSO/GST, demonstrating the tool’s usability in diverse data sets.
Abstract
Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are ...associated with coronal mass ejections. We apply machine learning to systematically compare SMFRs, MCs, and ambient solar wind plasma properties. We construct a data set of 3-minute averaged sequential data points of the solar wind’s instantaneous bulk fluid plasma properties using about 20 years of measurements from Wind. We label samples by the presence and type of MFRs containing them using a catalog based on Grad–Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs (with samples in neither labeled non-MFRs). We apply the random forest machine learning algorithm to find which categories can be more easily distinguished and by what features. MCs were distinguished from non-MFRs with an area under the receiver-operator curve (AUC) of 94% and SMFRs with an AUC of 89%, and had distinctive plasma properties. In contrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, this appears to rely solely on the 〈
B
〉 > 5 nT threshold applied by the GS catalog. The results indicate that SMFRs have virtually the same plasma properties as the ambient solar wind, unlike the distinct plasma regimes of MCs. We interpret our findings as additional evidence that most SMFRs at 1 au are generated within the solar wind. We also suggest that they should be considered a salient feature of the solar wind’s magnetic structure rather than transient events.
Deep learning has drawn significant interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments. Here we propose a new deep ...learning method, called SolarUnet, to identify and track solar magnetic flux elements or features in observed vector magnetograms based on the Southwest Automatic Magnetic Identification Suite (SWAMIS). Our method consists of a data preprocessing component that prepares training data from the SWAMIS tool, a deep learning model implemented as a U-shaped convolutional neural network for fast and accurate image segmentation, and a postprocessing component that prepares tracking results. SolarUnet is applied to data from the 1.6 m Goode Solar Telescope at the Big Bear Solar Observatory. When compared to the widely used SWAMIS tool, SolarUnet is faster while agreeing mostly with SWAMIS on feature size and flux distributions and complementing SWAMIS in tracking long-lifetime features. Thus, the proposed physics-guided deep learning-based tool can be considered as an alternative method for solar magnetic tracking.
We present two recurrent neural networks (RNNs), one based on gated recurrent units and the other based on long short-term memory, for predicting whether an active region (AR) that produces an M- or ...X-class flare will also produce a coronal mass ejection (CME). We model data samples in an AR as time series and use the RNNs to capture temporal information on the data samples. Each data sample has 18 physical parameters, or features, derived from photospheric vector magnetic field data taken by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. We survey M- and X-class flares that occurred from 2010 to 2019 May using the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and select those flares with identified ARs in the NCEI catalogs. In addition, we extract the associations of flares and CMEs from the Space Weather Database of Notifications, Knowledge, Information. We use the information gathered above to build the labels (positive versus negative) of the data samples at hand. Experimental results demonstrate the superiority of our RNNs over closely related machine learning methods in predicting the labels of the data samples. We also discuss an extension of our approach to predict a probabilistic estimate of how likely an M- or X-class flare is to initiate a CME, with good performance results. To our knowledge this is the first time that RNNs have been used for CME prediction.
The coronavirus disease 2019 (COVID-19) pandemic has had significant economic impact on radiology with markedly decreased imaging case volumes. The purpose of this study was to quantify the imaging ...volumes during the COVID-19 pandemic across patient service locations and imaging modality types.
Imaging case volumes in a large health care system were retrospectively studied, analyzing weekly imaging volumes by patient service locations (emergency department, inpatient, outpatient) and modality types (x-ray, mammography, CT, MRI, ultrasound, interventional radiology, nuclear medicine) in years 2020 and 2019. The data set was split to compare pre-COVID-19 (weeks 1-9) and post-COVID-19 (weeks 10-16) periods. Independent-samples t tests compared the mean weekly volumes in 2020 and 2019.
Total imaging volume in 2020 (weeks 1-16) declined by 12.29% (from 522,645 to 458,438) compared with 2019. Post-COVID-19 (weeks 10-16) revealed a greater decrease (28.10%) in imaging volumes across all patient service locations (range 13.60%-56.59%) and modality types (range 14.22%-58.42%). Total mean weekly volume in 2020 post-COVID-19 (24,383 95% confidence interval 19,478-29,288) was statistically reduced (P = .003) compared with 33,913 95% confidence interval 33,429-34,396 in 2019 across all patient service locations and modality types. The greatest decline in 2020 was seen at week 16 specifically for outpatient imaging (88%) affecting all modality types: mammography (94%), nuclear medicine (85%), MRI (74%), ultrasound (64%), interventional (56%), CT (46%), and x-ray (22%).
Because the duration of the COVID-19 pandemic remains uncertain, these results may assist in guiding short- and long-term practice decisions based on the magnitude of imaging volume decline across different patient service locations and specific imaging modality types.
Transfer learning (TL) algorithms aim to improve the prediction performance in a target task (e.g. the prediction of cisplatin sensitivity in triple-negative breast cancer patients) via transferring ...knowledge from auxiliary data of a related task (e.g. the prediction of docetaxel sensitivity in breast cancer patients), where the distribution and even the feature space of the data pertaining to the tasks can be different. In real-world applications, we sometimes have a limited training set in a target task while we have auxiliary data from a related task. To obtain a better prediction performance in the target task, supervised learning requires a sufficiently large training set in the target task to perform well in predicting future test examples of the target task. In this paper, we propose a TL approach for cancer drug sensitivity prediction, where our approach combines three techniques. First, we shift the representation of a subset of examples from auxiliary data of a related task to a representation closer to a target training set of a target task. Second, we align the shifted representation of the selected examples of the auxiliary data to the target training set to obtain examples with representation aligned to the target training set. Third, we train machine learning algorithms using both the target training set and the aligned examples. We evaluate the performance of our approach against baseline approaches using the Area Under the receiver operating characteristic (ROC) Curve (AUC) on real clinical trial datasets pertaining to multiple myeloma, nonsmall cell lung cancer, triple-negative breast cancer, and breast cancer. Experimental results show that our approach is better than the baseline approaches in terms of performance and statistical significance.
Abstract Small-scale interplanetary magnetic flux ropes (SMFRs) are similar to ICMEs in magnetic structure, but are smaller and do not exhibit coronal mass ejection plasma signatures. We present a ...computationally efficient and GPU-powered version of the single-spacecraft automated SMFR detection algorithm based on the Grad–Shafranov (GS) technique. Our algorithm can process higher resolution data, eliminates selection bias caused by a fixed 〈 B 〉 threshold, has improved detection criteria demonstrated to have better results on an MHD simulation, and recovers full 2.5D cross sections using GS reconstruction. We used it to detect 512,152 SMFRs from 27 yr (1996–2022) of 3 s cadence Wind measurements. Our novel findings are the following: (1) the SMFR filling factor (∼ 35%) is independent of solar activity, distance to the heliospheric current sheet, and solar wind plasma type, although the minority of SMFRs with diameters greater than ∼0.01 au have a strong solar activity dependence; (2) SMFR diameters follow a log-normal distribution that peaks below the resolved range (≳10 4 km), although the filling factor is dominated by SMFRs between 10 5 and 10 6 km; (3) most SMFRs at 1 au have strong field-aligned flows like those from Parker Solar Probe measurements; (4) the radial density (generally ∼1 detected per 10 6 km) and axial magnetic flux density of SMFRs are higher in faster solar wind types, suggesting that they are more compressed. Implications for the origin of SMFRs and switchbacks are briefly discussed. The new algorithm and SMFR dataset are made freely available.
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