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Marginal Abatement Cost Curves compare and assess greenhouse gas mitigation options available to various sectors of the economy. In the Irish agricultural sector, large anaerobic digestion facilities are currently considered a high-cost abatement solution. In prior studies of anaerobic digestion abatement costs, two options were assessed: the generation of heat and electricity from biogas (115 €/tCO2eq) and the production of renewable heat from biomethane (280 €/tCO2eq). Both scenarios encompass single cost values that may not capture the potentially variable nature of such systems. In contrast, prior techno-economic analyses and lifecycle analyses can provide a comparison of the abatement costs of anaerobic digestion systems at a range of scales. This work compares two case studies (based on prior literature) for small and medium-scale on farm anaerobic digestion systems. The small-scale system is set in Ireland with cattle slurry collected in open tanks during the winter, while the medium-scale system is set in the USA with cattle slurry collected periodically indoors all year-round. It was found that the abatement cost can vary between -117 to +79 € per t CO2eq. The key variables that affected the abatement cost were additional revenue streams such as biofertilizer sales, displaced energy savings, and additional incentives and emissions savings within the system boundary. Including only some of these options in the analysis resulted in higher abatement costs being reported. Based on the variation between system topologies and therefore system boundaries, assigning a single mitigation cost to anaerobic digestion systems may not be representative.

The veracity of an abatement cost analysis depends on a clear methodological process.The abatement cost varies based on the processes considered within the system boundary.On-farm digestion abatement costs assessed ranged from -117 to +79 €/tCO_2eq.On-farm emissions savings ranged from 609 to 10,358 tCO_2eq/yr.Abatement costs reduce when considering the income and emissions savings from co-benefits.

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Pulmonary tuberculosis (TB) is one of the top 10 causes of death worldwide caused by an infection. TB is curable with an adequate diagnosis, normally performed through bacilloscopies. Automate TB diagnosis implies bacilli detection and counting usually based on smear images processing and artificial intelligence. Works reported in the literature usually consider images with similar coloring characteristics, which are difficult to obtain due to the Ziehl - Neelsen staining method variations (excess or deficiency of coloration), provoking errors in the bacilli segmentation. This paper presents an image preprocessing technique, based on simple, fast and well-known processing techniques, to improve and standardize the contrast in the Acid-Fast Bacilli (AFB) images used to diagnose TB; these techniques are used previously to the segmentation stage to obtain accurate results. The results are validated with and without the preprocessing stage by the Jaccard index, pixel detection accuracy and UAC obtained in an Artificial Neural Network (ANN) and a Bayesian classifier with Gaussian mixture model (GMM). Obtained results indicate that the proposed approach can be applied to automate the Tuberculosis diagnostic.

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Image segmentation applied to medical image analysis is still a critical and important task. Although there exist several segmentation algorithms that have been widely studied in literature, these are subject to segmentation problems such as over- and under-segmentation as well as non-closed edges. In this paper, a simple method that combines well-known segmentation algorithms is presented. This method is applied to detect acid-fast bacilli (AFB) in bacilloscopies used to diagnose pulmonary tuberculosis (TB). This diagnosis can be performed through different tests, and the most used worldwide is smear microscopy because of its low cost and effectiveness. This diagnosis technique is based on the analysis and counting of the bacilli in the bacilloscopy observed under an optical microscope. The proposed method is used to segment the bacilli in digital images from bacilloscopies processed using Ziehl-Neelsen (ZN) staining. The proposed method is fast, has a low computational cost and good efficiency compared to other methods. The bacilli image segmentation is performed by image processing and analysis techniques, probability concepts and classifiers. In this work, a Bayesian classifier based on a Gaussian mixture model (GMM) is used. The segmentations' results are validated by using the Jaccard index, which indicates the efficiency of the classifier.

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