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Calcium looping is a promising thermochemical energy storage process to be integrated into concentrating solar power plants. This work develops for the first time a comprehensive life cycle assessment of the calcium looping integration in solar plants to assess the potential of the technology from an environmental perspective. Two representative integrations are analysed, representing daily (hot) and seasonal (cold) storage designs. Similar performance environmental impacts are observed in both, with slightly better results for the seasonal storage case due to the simplified energy storage integration. The results show the moderate environmental impact of calcium looping thermochemical energy storage technology, resulting in lower equivalent carbon dioxide emissions 24 kg/MWh) than other energy storage options such as molten salt-based solar facilities (40 kg/MWh). Plant construction involves a higher energy demand for the process, whilst the operation and maintenance on the plant represent a moderate impact due to the low environmental impact of limestone, the unique raw material of the process, and the lower water consumption compared to typical concentrating solar power plants. Besides, the energy required for the system is first time analysed, obtaining an energy payback time of 2.2 years and 2.5 years depending on the storage strategy design.

Calcium looping is a promising thermochemical energy storage process to be integrated into concentrating solar power plants. This work develops for the first time a comprehensive life cycle assessment of the calcium looping integration in solar plants to assess the potential of the technology from an environmental perspective. Two representative integrations are analysed, representing daily (hot) and seasonal (cold) storage designs. Similar performance environmental impacts are observed in both, with slightly better results for the seasonal storage case due to the simplified energy storage integration. The results show the moderate environmental impact of calcium looping thermochemical energy storage technology, resulting in lower equivalent carbon dioxide emissions 24 kg/MWh) than other energy storage options such as molten salt-based solar facilities (40 kg/MWh). Plant construction involves a higher energy demand for the process, whilst the operation and maintenance on the plant represent a moderate impact due to the low environmental impact of limestone, the unique raw material of the process, and the lower water consumption compared to typical concentrating solar power plants. Besides, the energy required for the system is first time analysed, obtaining an energy payback time of 2.2 years and 2.5 years depending on the storage strategy design.

Calcium looping is a promising thermochemical energy storage process to be integrated into concentrating solar power plants. This work develops for the first time a comprehensive life cycle assessment of the calcium looping integration in solar plants to assess the potential of the technology from an environmental perspective. Two representative integrations are analysed, representing daily (hot) and seasonal (cold) storage designs. Similar performance environmental impacts are observed in both, with slightly better results for the seasonal storage case due to the simplified energy storage integration. The results show the moderate environmental impact of calcium looping thermochemical energy storage technology, resulting in lower equivalent carbon dioxide emissions 24 kg/MWh) than other energy storage options such as molten salt-based solar facilities (40 kg/MWh). Plant construction involves a higher energy demand for the process, whilst the operation and maintenance on the plant represent a moderate impact due to the low environmental impact of limestone, the unique raw material of the process, and the lower water consumption compared to typical concentrating solar power plants. Besides, the energy required for the system is first time analysed, obtaining an energy payback time of 2.2 years and 2.5 years depending on the storage strategy design.

Objective: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach. Material and methods: The application of quantitative imaging in neuromuscular diseases requires the availability of regions of interest (ROI) drawn on muscles to extract quantitative parameters. Up to now, manual drawing of ROIs has been considered the gold standard in clinical studies, with no clear and universally accepted standardized procedure for segmentation. Several automatic methods, based mainly on machine learning and deep learning algorithms, have recently been proposed to discriminate between skeletal muscle, bone, subcutaneous and intermuscular adipose tissue. We develop a supervised deep learning approach based on a unified framework for ROI segmentation. Results: The proposed network generates segmentation maps with high accuracy, consisting in Dice Scores ranging from 0.89 to 0.95, with respect to “ground truth” manually segmented labelled images, also showing high average performance in both mild and severe cases of disease involvement (i.e. entity of fatty replacement). Discussion: The presented results are promising and potentially translatable to different skeletal muscle groups and other MRI sequences with different contrast and resolution.

Purpose: Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning. Method: Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset. Results: Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2. Conclusion: This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.