Two-phase olive pomace is a byproduct of the olive-oil extraction process that is sometimes processed further to recover more oil. During pomace oil production, especially in the early stages of olive processing, odours can be produced. These odours are a serious nuisance especially in and around the processing facilities, as well as for nearby communities. These odours can be strong and unpleasant, and they may result in various issues, including environmental concerns. Thus, in this paper, the effect of storage and drying on the composition of two-phase olive pomace was investigated. Several physicochemical and instrumental analyses were performed and it was concluded that the structural elements of pomace are not the ones who are affected during storage and drying. Storage and drying mainly impact the oil content of pomace, leading to an acidity increase and to the production of odorous oxidation compounds. Volatile acids, methyl and ethyl esters are released into the atmosphere causing serious environmental nuisance.

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim's offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.