. This study aims to cross-validate ground-based and satellite-based models of three photobiological UV effective dose products: the Commission Internationale de l'Eclairage (CIE) erythemal UV, the production of vitamin D in the skin, and DNA damage, using high-temporal-resolution surface-based measurements of solar UV spectral irradiances from a synergy of instruments and models. The satellite-based Tropospheric Emission Monitoring Internet Service (TEMIS; version 1.4) UV daily dose data products were evaluated over the period 2009 to 2014 with ground-based data from a Norsk Institutt for Luftforskning (NILU)-UV multifilter radiometer located at the northern midlatitude super-site of the Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (LAP/AUTh), in Greece. For the NILU-UV effective dose rates retrieval algorithm, a neural network (NN) was trained to learn the nonlinear functional relation between NILU-UV irradiances and collocated Brewer-based photobiological effective dose products. Then the algorithm was subjected to sensitivity analysis and validation. The correlation of the NN estimates with target outputs was high (r = 0. 988 to 0.990) and with a very low bias (0.000 to 0.011 in absolute units) proving the robustness of the NN algorithm. For further evaluation of the NILU NN-derived products, retrievals of the vitamin D and DNA-damage effective doses from a collocated Yankee Environmental Systems (YES) UVB-1 pyranometer were used. For cloud-free days, differences in the derived UV doses are better than 2 % for all UV dose products, revealing the reference quality of the ground-based UV doses at Thessaloniki from the NILU-UV NN retrievals. The TEMIS UV doses used in this study are derived from ozone measurements by the SCIAMACHY/Envisat and GOME2/MetOp-A satellite instruments, over the European domain in combination with SEVIRI/Meteosat-based diurnal cycle of the cloud cover fraction per 0. 5° × 0. 5° (lat  ×  long) grid cells. TEMIS UV doses were found to be ∼ 12.5 % higher than the NILU NN estimates but, despite the presence of a visually apparent seasonal pattern, the R2 values were found to be robustly high and equal to 0.92–0.93 for 1588 all-sky coincidences. These results significantly improve when limiting the dataset to cloud-free days with differences of 0.57 % for the erythemal doses, 1.22 % for the vitamin D doses, and 1.18 % for the DNA-damage doses, with standard deviations of the order of 11–13 %. The improvement of the comparative statistics under cloud-free cases further testifies to the importance of the appropriate consideration of the contribution of clouds in the UV radiation reaching the Earth's surface. For the urban area of Thessaloniki, with highly variable aerosol, the weakness of the implicit aerosol information introduced to the TEMIS UV dose algorithm was revealed by comparison of the datasets to aerosol optical depths at 340 nm as reported by a collocated CIMEL sun photometer, operating in Thessaloniki at LAP/AUTh as part of the NASA Aerosol Robotic Network.

/pdf/temis-uv-product-validation-using-nilu-uv-ground-based-58iexjdnm7.pdf

TEMIS UV product validation using NILU-UV ground-based measurements in Thessaloniki, Greece

Despite significant improvement in Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval, high-resolution–high-accuracy AOD retrieval remains a challenging tas...

MODIS aerosol optical depth retrieval based on random forest approach

The surge of machine learning (ML) applications and increasing usage of data driven approach for resilience enhancement provide great opportunities for applying ML techniques to power outage prediction (POP) during hurricanes. Considering the substantial damage that wind hazards can cause to power system network (PSN) in countries such as the USA and China, researchers have devised numerous approaches for disaster prevention to reduce their susceptibility. Since the selection of optimal ML algorithms for POP during hurricanes is a complex task, this paper helps researchers easily comprehend the numerous prediction techniques used worldwide for power outages during hurricanes. The paper provides a brief review of several categories of hurricanes and the damage it causes to the PSN more specifically to distribution side, the necessary measures to improve the PSN resilience, and finally a sequential approach for optimal ML model selection techniques that researchers and engineers can use to improve the POP during hurricanes. In this study the effectiveness of ML algorithms has been explored with the help of performance evaluation metrics which serve as the basic deciding criteria for optimal model selection. This research also highlights some of the key issues and challenges of integrating machine learning algorithms with existing PSN. 

Machine learning for power outage prediction during hurricanes: An extensive review

Background: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all socio-humanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI/ML literacy in underserved communities and build a more diverse AI/ML design and development workforce engaged in health research. Objective: AI/ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help to put ethics and fairness at the forefront of AI

Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in Artificial Intelligence/Machine Learning: A Modified Delphi Approach (Preprint)

Technological advancements and innovations in enhancing resilience of electrical distribution systems

Uncertainty Analysis of Neural-Network-Based Aerosol Retrieval

Electric grid continually monitors spatiotemporal data from sparse service areas. As power systems grow and get more complex, and with the deployment of more sensors and data collection capabilities, monitoring and analyzing data streams for outage prediction will get more complicated. In addition, the burden on human operators to analyze such data is getting challenging. Furthermore, climate change introduces new challenges to power grid reliability and makes the human grid operators’ task more critical. To address some of these challenges, this research proposes a novel model to jointly predict power grid outages and discover precursors from spatiotemporal data using multi-level data. The new method utilizes multi-task learning (MTL) and multi-instance learning (MIL) to jointly predict outages and learn event precursors. This is achieved by introducing distance-aware self-attention to capture relationships between locations and improve event detection and precursor discovery while utilizing multi-level data (local weather data, global demand, and forecast data) in a sparse setting. Experiments are conducted using five years of data collected in the U.S. Pacific Northwest. The proposed methodology achieves an Area Under the Precision-Recall Curve (AU-PRC) of 0.97 using 12 hours of data before the event. Experiments showed that the proposed model could predict events several hours ahead with high accuracy, where such early predictions allow grid operators to deploy outage mitigation plans. In addition, the new framework effectively discovers spatiotemporal precursors for power outages. Grid operators can use such event precursors to help mitigate outages and improve grid reliability.

Enhancing Weather-Related Outage Prediction and Precursor Discovery Through Attention-Based Multi-Level Modeling

A hospital readmission risk prediction model based on electronic health record (EHR) data can be an important tool for identifying high-risk patients in need of additional support. Performant readmission models based on deep learning approaches require large, high-quality training datasets to perform optimally. Utilizing EHR data from a source hospital system to enhance prediction on a target hospital using traditional approaches might bias the dataset if distributions of the source and target data are different. There is a lack of an end-to-end readmission model that can capture cross-domain knowledge. Herein, we propose an early readmission risk temporal deep adaptation network, ERR-TDAN, for cross-domain spatial knowledge transfer. ERR-TDAN transforms source and target data to a common embedding space while capturing temporal dependencies of the sequential EHR data. Domain adaptation is then applied on a domain-specific fully connected linear layer. The model is optimized by a loss function that combines distribution discrepancy loss to match the mean embeddings of the two distributions and the task loss to optimize predicting readmission at the target hospital. In a use case of patients with diabetes, a model developed using target data of 37,091 patients from an urban academic hospital was enhanced by transferring knowledge from high-quality source data of 20,471 patients from a rural academic hospital. The proposed method yielded a 5% increase in F1-score compared to baselines. ERR-TDAN may be an effective way to increase a readmission risk model’s performance when data from multiple sites are available. 

Spatial Knowledge Transfer with Deep Adaptation Network for Predicting Hospital Readmission

With the increase in volume of daily online news items, it is more and more difficult for readers to identify news articles relevant to their interests. Thus, effective recommendation systems are critical for an effective user news consumption experience. Existing news recommendation methods usually rely on the news click history to model user interest. However, there are other signals about user behaviors, such as user commenting activity, which have not been used before. We propose a recommendation algorithm that predicts articles a user may be interested in, given her historical sequential commenting behavior on news articles. We show that following this sequential user behavior the news recommendation problem falls into in the class of session-based recommendation. The techniques in this class seek to model users' sequential and temporal behaviors. While we seek to follow the general directions in this space, we face unique challenges specific to news in modeling temporal dynamics, e.g., users' interests shift over time, users comment irregularly on articles, and articles are perishable items with limited lifespans. We propose a recency-regularized neural attentive framework for session-based news recommendation. The proposed method is able to capture the temporal dynamics of both users and news articles, while maintaining interpretability. We design a lag-aware attention and a recency regularization to model the time effect of news articles and comments. We conduct extensive empirical studies on 3 real-world news datasets to demonstrate the effectiveness of our method.

Session-based News Recommendation from Temporal User Commenting Dynamics

The methods for power system event detection using field-recorded data from Phasor Measurement Units (PMUs) often require many labeled events, which can be costly or infeasible to obtain. We show that events in one power system can be accurately detected by reusing a small number of carefully selected labeled PMU data from another without the need for additional labeling. Our transfer learning-based approach outperforms alternative state-of-the-art conventional machine learning (ML) methods on a large PMU historical dataset. We demonstrate this approach with a use case of detecting events from historical PMU data recorded in the Eastern Interconnection in the USA by using similar labeled PMU data from the Western Interconnection. This technique may be propagated to other situations where some of the events’ data from one power system may be applied to enhance learning in another.

Transfer Learning on Phasor Measurement Data from a Power System to Detect Events in Another System

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