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Many old unreinforced masonry (URM) structures still in use need to be assessed considering the safety requirements proposed by current codes. Because of the complexity of the URM response, sophisticated numerical descriptions are required for an accurate structural assessment. When inverse analysis is used for the identification of material properties, the study of the effects of measurement errors is essential for assessing the robustness of the adopted procedure. In this work, inverse analysis techniques utilising Genetic Algorithms are employed to calibrate elastic material parameters of an advanced mesoscale model for URM. In order to apply this strategy to in-situ low-invasive investigations, a non-conventional flat-jack test setup is proposed. The potential and limitations of the method are analysed using computer-generated pseudo-experimental data with different noise limits. This allows the evaluation of the influence of the measurement equipment precision on the stability of the inverse problem. [ABSTRACT FROM AUTHOR]

Introduction/Background Robotic rehabilitation is known to be at least as effective as conventional training for upper limb motor recovery after stroke; nevertheless, which patients could benefit from this treatment is unknown and finding markers that could predict rehabilitation outcome is a challenge. We aimed at understanding the neural mechanisms of motor function recovery after upper limb robotic rehabilitation in chronic stroke patients using neurophysiological markers obtained by electroencephalography recording (EEG). Material and method Fourteen chronic stroke patients (M/F: 11/3; 59.5 ± 13 yrs) with mild to moderate upper limb paresis were subjected to 10 sessions of upper limb rehabilitation with a planar mobile robotic device (MOTORE, Humanware). Fugl–Meyer Assessment Scale (FMAS) and Wolf Motor Function Test (WMFT) were administered before (t0), at the end (t1) and at 1 month follow-up (t2); at the same timing 64-channals EEG was recorded. We analyzed power spectrum density in different frequency bands of the affected and unaffected hemispheres with 64-ch EEG and their correlation with motor impairment as measured by clinical scales. Correlation analyses were performed to identify the indicators of good rehabilitative outcome. Results Clinical assessment indicated a significant functional improvement in upper limb motor function at the end of rehabilitation as assessed with FMAS and WMFT score that is maintained at follow-up. We found a positive correlation between global Alpha activity at t0 and WMFT score variation (t0–t1) and between global Beta activity at t0 and WMFT time variation (t0–t1) and a positive correlation between Beta activity at t0 in the unaffected hemisphere and FMAS variation (t0–t1 and t0–t2). Conclusion Robotic rehabilitation improves upper limb motor performance in stroke patients even in the chronic phase. The amount of Alpha and Beta band power at t0 is suggestive of rehabilitation-related motor outcome. Our results suggest that EEG recording preliminarily to robotic rehabilitation could help identifying good responders to treatment thus optimizing results. [ABSTRACT FROM AUTHOR]