The recent interest in measuring methane (CH) emissions from abandoned oil and gas wells has resulted in five methods being typically used. In line with the US Federal Orphaned Wells Program's (FOWP) guidelines and the American Carbon Registry's (ACR) protocols, quantification methods must be able to measure minimum emissions of 1 g of CH h to within ±20%. To investigate if the methods meet the required standard, dynamic chambers, a Hi-Flow (HF) sampler, and a Gaussian plume (GP)-based approach were all used to quantify a controlled emission (; g h) of 1 g of CH h. After triplicate experiments, the average accuracy (; %) and the upper (; %) and lower (; %) uncertainty bounds of all methods were calculated. Two dynamic chambers were used, one following the ACR guidelines, and a second "mobile" chamber made from lightweight materials that could be constructed around a source of emission on a well head. The average emission calculated from the measurements made using the dynamic chamber ( = 1.01 g CH h, = +0.9%), the mobile chamber ( = 0.99 g CH h, = -1.4%), the GP approach ( = 0.97 g CH h, = -2.6%), and the HF sampler ( = 1.02 g CH h, = +2.2%) were all within ±3% of 1 g of CH h and met the requirements of the FOWP and ACR protocols. The results also suggest that the individual measurements made using the dynamic chamber can quantify emissions of 1 g of CH h to within ±6% irrespective of the design (material, number of parts, geometrical shape, and hose length), and changes to the construction or material specifications as defined via ACR make no discernible difference to the quantification uncertainty. Our tests show that a collapsible chamber can be easily constructed around the emission source on an abandoned well and be used to quantify emissions from abandoned wells in remote areas. To our knowledge, this is the first time that methods for measuring the CH emissions of 1 g of CH h have been quantitively assessed against a known reference source and against each other.
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http://dx.doi.org/10.3390/s23229246 | DOI Listing |
MAGMA
January 2025
Aix Marseille Univ, CNRS, CRMBM, Marseille, France.
Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.
View Article and Find Full Text PDFJ Vet Intern Med
January 2025
Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA.
Background: Left ventricular (LV) volumes can be calculated from various linear, monoplane, and multiplane echocardiographic methods, and the same method can be applied to different imaging views. However, these methods and their variations have not been comprehensively evaluated against real-time 3-dimensional echocardiography (RT3D).
Hypothesis/objectives: To identify the LV volumetric approaches that produce the least bias and the best agreement with RT3D, and to assess interoperator reproducibility between an experienced and an inexperienced operator.
J Clin Med
December 2024
Department of Vascular and Endovascular Surgery, KliniK Ottakring, Montleartstrasse 37, 1160 Vienna, Austria.
: Despite advancements in vascular surgery, the mortality among peripheral arterial disease (PAD) patients undergoing major amputations remains high. While a large body of evidence has previously covered survival rates after major amputation, there is less evidence regarding the associated survival penalty from an epidemiological perspective. The present analysis aimed at quantifying the survival disadvantage after major lower limb amputation while investigating which factors are associated with mortality in this patient cohort.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical Engineering, Guizhou University, Guiyang 550028, China.
Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference.
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