Pitney Bowes (France)

Recent years have seen a rapid increase in the push for energy transition in Australia. Carbon prices have been introduced through the Safeguard Mechanism and Australian Carbon Credit Units. Consumers of hydrocarbon products are beginning to demand carbon neutral products. Oil and gas operators are pledging net-zero emissions targets. The decarbonisation efforts that ensue are expected to drive a paradigm shift in upstream development approaches. In this work, we investigate how an anonymised high carbon dioxide (CO2) gas field may be developed under prevailing carbon prices. We identify three project archetypes and estimate economically recoverable resources under each project; we also analyse the economic performance of each project. In the first archetype, we consider a project where CO2 is separated from the produced gas and vented into the atmosphere. In the second archetype, we consider a carbon sequestration project in which CO2 is transported via pipeline for storage in an onshore depleted gas reservoir. Lastly, we consider a case in which the gas buyer demands a fully carbon neutral product; as such, we study the feasibility of producing blue hydrogen using the produced gas, and how this impacts project economics and reserves. The case studies quantify by how much decarbonisation efforts negatively impact project economics and how they are the least objectionable when carbon prices are high. The analysis shows that carbon prices in Australia are currently sufficient to enable commercial development in ideal situations, but need to be higher to enable wider decarbonisation.

Some smokers switch away from smoking using e-cigarettes, but guidelines recommend trying approved medications first. We analyzed switching in adult smokers using JUUL by their recent history of quit attempts and use of smoking cessation medications. Participants were 8511 adult (21+) established smokers (at baseline), in which 50.3% are daily smokers, in a longitudinal observational study who completed a survey 12 months after first purchasing a JUUL Starter Kit. At baseline, participants reported attempts to quit smoking in the prior year and use of pharmacotherapy (nicotine replacement therapy [NRT] or prescription medication) in their most recent attempt. The outcomes were switching (self-reported no past-30-day smoking) and 50%+ reductions in cigarette consumption. Multivariable analyses were adjusted for baseline covariates. Two thirds of the participants had made a quit attempt in the year before purchasing JUUL. Overall, 59% [58%, 60%] had switched at 12 months. Switching was more likely in those who had used NRT and who attempted quitting without medication versus those who used prescription medications or made no quit attempt. In adjusted multivariable analyses, only making a past-year quit attempt (vs. not) was associated with higher odds of switching (OR =1.15 [1.04, 1.28]). Over 60% of dual users reduced cigarette consumption by ≥50%. These associations were largely similar in daily smokers. Twelve months after purchasing JUUL, almost all smokers reported either switching or reducing their smoking by 50%+, including those who had recently failed to quit smoking with approved pharmacotherapies. E-cigarettes provide an alternative route to abstinence from smoking for smokers with a history of cessation and cessation treatment failure.

Peripheral artery disease (PAD) affects 200 million individuals worldwide. In the United States, certain demographic groups experience a disproportionately higher prevalence and clinical effect of PAD. The social and clinical effect of PAD includes higher rates of individual disability, depression, minor and major limb amputation along with cardiovascular and cerebrovascular events. The reasons behind the inequitable burden of PAD and inequitable delivery of care are both multifactorial and complex in nature, including systemic and structural inequity that exists within our society. Herein, we present an overview statement of the myriad variables that contribute to PAD disparities and conclude with a summary of potential novel solutions.

Electronic nicotine delivery systems (ENDS) are a potentially lower-risk tobacco product that could help smokers switch completely away from cigarettes. However, the lack of strong evidence to date of a measurable, population-level effect on reducing smoking has generated skepticism about ENDS’ potential benefits. This study examines whether increased US ENDS sales have been associated with reduced cigarette sales. Retail data on weekly per-capita cigarette and ENDS purchases in the US during 2014-19 were obtained from a national sample of brick-and-mortar retail outlets. Trends in cigarette sales were modeled before (2014-2016) ENDS had a substantial market share, and, after adjusting for macroeconomic factors, projected into the post-period (2017-19). Actual cigarette sales were lower than projected sales (by up to 16% across the post-period), indicating a substantial ‘cigarette shortfall’ in the post-period. To explore whether general (i.e., inclusive of potentially many mechanisms) substitution by ENDS can explain the cigarette shortfall, its association with per-capita ENDS volume sales was examined. Higher ENDS sales were significantly associated with a greater cigarette shortfall: for every additional per-capita ENDS unit, cigarette sales were 1.4 packs-per-capita lower than expected (B=1.4, _p_<.0001). Error correction models which account for spurious correlation yielded similar results. These findings support ENDS serving as a substitute for cigarettes (through potentially many mechanisms including cigarette price), causing cigarette consumption to decline. Since ENDS potentially pose a lower risk than cigarettes, this general substitution effect suggests that risk-proportionate tobacco regulation could mitigate the tobacco-related health burden.

Large-scale three-dimensional spatial data has gained increasing attention with the development of self-driving, mineral exploration, CAD, and human atlases. Such 3D objects are often represented with a polygonal model at high resolution to preserve accuracy. This poses major challenges for 3D data management and spatial queries due to the massive amounts of 3D objects, e.g., trillions of 3D cells, and the high complexity of 3D geometric computation. Traditional spatial querying methods in the Filter-Refine paradigm have a major focus on indexing-based filtering using approximations like minimal bounding boxes and largely neglect the heavy computation in the refinement step at the intra-geometry level, which often dominates the cost of query processing. In this paper, we introduce 3DPro, a system that supports efficient spatial queries for complex 3D objects. 3DPro uses progressive compression of 3D objects preserving multiple levels of details, which significantly reduces the size of the objects and has the data fit into memory. Through a novel Filter-Progressive-Refine paradigm, 3DPro can have query results returned early whenever possible to minimize decompression and geometric computations of 3D objects in higher resolution representations. Our experiments demonstrate that 3DPro out-performs the state-of-the-art 3D data processing techniques by up to an order of magnitude for typical spatial queries.

Abstract Head kinematics information is very valuable as it is used to measure brain injury risk. Currently, head kinematics are measured using wearable devices or instrumentation mounted on the head. These instrumentation and wearable devices can have errors due to faulty sensors and due to relative motion between the wearable device and the respective body region. This paper proposes a novel method to predict the head kinematics directly from videos without any instrumentation using a deep learning approach. To prove the concept, a deep learning model was developed for predicting time history of head angular velocities and their respective peaks using Finite Element (FE) based crash simulation data. This FE dataset was split into training, validation, and test datasets. A combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based deep learning model was developed using the training and validations sets. The test (unseen) dataset was used to evaluate the predictive capability of the deep learning model. On the test dataset, correlation coefficient obtained between the actual and predicted peak angular velocities was 0.73, 0.85, and 0.92 for X, Y, and Z components respectively.

Chronic kidney disease (CKD) is one of the serious health concerns in the twenty-first century. CKD impacts over 37 million Americans. By applying machine learning (ML) techniques to clinical data, CKD can be diagnosed early. This early detection of CKD can prevent numerous loss of life. In this work, clinical data set of 400 patients, available on the UCI repository, are taken. Unfortunately, this data set doesn’t have an equal distribution of CKD and Non-CKD samples. This imbalanced nature of data highly influences the learning capabilities of classifiers. Genetic Programming (GP) is an ML technique based on the evolution of species. GP with standard fitness function, also impacted by this imbalanced nature of data. A new Euclidean distance-based fitness function in GP is proposed to handle this imbalanced nature of the data set. To compare the robustness of the proposed work, other classification techniques, K-nearest neighborhood (KNN), KNN with particle swarm optimization (PSO), and GP with the standard fitness function, is also applied. For ten-fold cross-validation, the KNN shows an accuracy of 83.54% with an AUC value of 0.69, the PSO-KNN shows an accuracy of 96.79% with an AUC value of 0.94, and the GP, with the newly proposed fitness function, supersedes KNN and PSO-KNN and shows the accuracy of 99.33% with an AUC value of 0.99.