Validation of Dry Bulk Pile Volume Estimation Algorithm based on Angle of Repose using Experimental Images

Estimation of volume of piles in shipping ports plays a pivotal role for logistics management, facilitates better ship rescheduling and rerouting for economic benefits and contributes to overall efficient shipping management. This paper presents validation results for a volume estimation algorithm for dry bulk cargo piles stored in open ports. Using remote sensing images obtained in a laboratory setting, the method first detects the contour of the pile and then reconstructs its 3D model based on the material's angle of repose, and estimates the volume accordingly. We validated the algorithm on full conical piles and single-ridge elongated piles, and further tested it on reclaimed conical and elongated piles. The results demonstrated the algorithm's strong potential for accurately estimating pile volume from experimental images and a reference satellite image, achieving high accuracy in our validation.

Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish

Background: Clinical natural language processing (NLP) refers to the use of computational methods for extracting, processing, and analyzing unstructured clinical text data, and holds a huge potential to transform healthcare in various clinical tasks. Objective: The study aims to perform a systematic review to comprehensively assess and analyze the state-of-the-art NLP methods for the mainland Scandinavian clinical text. Method: A literature search was conducted in various online databases including PubMed, ScienceDirect, Google Scholar, ACM digital library, and IEEE Xplore between December 2022 and February 2024. Further, relevant references to the included articles were also used to solidify our search. The final pool includes articles that conducted clinical NLP in the mainland Scandinavian languages and were published in English between 2010 and 2024. Results: Out of the 113 articles, 18% (n=21) focus on Norwegian clinical text, 64% (n=72) on Swedish, 10% (n=11) on Danish, and 8% (n=9) focus on more than one language. Generally, the review identified positive developments across the region despite some observable gaps and disparities between the languages. There are substantial disparities in the level of adoption of transformer-based models. In essential tasks such as de-identification, there is significantly less research activity focusing on Norwegian and Danish compared to Swedish text. Further, the review identified a low level of sharing resources such as data, experimentation code, pre-trained models, and rate of adaptation and transfer learning in the region. Conclusion: The review presented a comprehensive assessment of the state-of-the-art Clinical NLP for electronic health records (EHR) text in mainland Scandinavian languages and, highlighted the potential barriers and challenges that hinder the rapid advancement of the field in the region.