Antonio Candelieri

Clinical and radiological evaluation of sacroiliac joints compared with ultrasound examination in early spondyloarthritis

OBJECTIVE To compare clinical and X-ray examinations with US findings of SI joints (SIJ) in early SpA patients. METHODS Twenty-three early SpA patients, diagnosed according to Assessment of SpondyloArthritis international Society criteria, were investigated clinically [sacral sulcus tenderness, BASMI, BASFI, BASDAI, pain and fatigue visual analogue scale (VAS), morning stiffness and sleep disturbance], with SIJ X-rays (New York score) and with My Lab70 US 7-10 MHz US (Esaote, Genoa, Italy), evaluating the width of the SIJ capsule and posterior sacroiliac (PSL) and sacrotuberosus (STL) ligament thickness and comparing the results with 23 healthy controls. RESULTS SIJ width [right 2.2 (0.6) and left 2.3 (0.7) in SpA vs 1.6 (0.1) and 1.7 (0.2) in healthy controls, respectively, expressed as mean (s.d.)] and STL thickness [right 3.9 (1.3) and left 3.4 (1.0) vs 1.8 (0.1) and 1.8 (0.1), respectively, expressed as mean (s.d.)] were higher in SpA patients than in controls (P < 0.001 and P < 0.05, respectively). PSL thickness was similar in patients and controls. Only STL thickness was higher when SIJ was tender at clinical examination (P < 0.01) and correlated with pain VAS (P < 0.001) and BASFI (P < 0.05). Furthermore, SIJ US results were unrelated to X-ray findings (similar when X-ray sacroiliitis was present and not). CONCLUSION Our exploratory study suggested that in early SpA patients US might be a promising method, complementary to other imaging techniques, to study articular and soft tissue periarticular involvement of SIJ, independent of clinical and X-ray examination.

Assessing structural health of helicopter fuselage panels through artificial neural networks hierarchies

Online assessment of the structural health of aircrafts is crucial both in military and civilian settings. In this paper, Artificial Neural Networks (ANNs) are exploited to obtain a reliable system performing two tasks: diagnosis and prognosis. Diagnosis is devoted to (a) detect a crack, (b) identify the component of the panel involved (bay or stringer) and (c) estimate crack centre and size. Prognosis aims at estimating the evolution of the crack and the Remaining Useful Life (RUL). Training of the ANNs is performed on data sets built through finite elements simulation. Two different ANN hierarchies are presented for diagnosis. Crack evolution is performed for cracks on bay and stringer, separately. Two ANNs are used to estimate the parameters of a crack propagation model (NASGRO equation) for RUL prediction.

Bayesian optimization of pump operations in water distribution systems

Bayesian optimization has become a widely used tool in the optimization and machine learning communities. It is suitable to problems as simulation/optimization and/or with an objective function computationally expensive to evaluate. Bayesian optimization is based on a surrogate probabilistic model of the objective whose mean and variance are sequentially updated using the observations and an “acquisition” function based on the model, which sets the next observation at the most “promising” point. The most used surrogate model is the Gaussian Process which is the basis of well-known Kriging algorithms. In this paper, the authors consider the pump scheduling optimization problem in a Water Distribution Network with both ON/OFF and variable speed pumps. In a global optimization model, accounting for time patterns of demand and energy price allows significant cost savings. Nonlinearities, and binary decisions in the case of ON/OFF pumps, make pump scheduling optimization computationally challenging, even for small Water Distribution Networks. The well-known EPANET simulator is used to compute the energy cost associated to a pump schedule and to verify that hydraulic constraints are not violated and demand is met. Two Bayesian Optimization approaches are proposed in this paper, where the surrogate model is based on a Gaussian Process and a Random Forest, respectively. Both approaches are tested with different acquisition functions on a set of test functions, a benchmark Water Distribution Network from the literature and a large-scale real-life Water Distribution Network in Milan, Italy.

Supporting Resilience Management of Water Distribution Networks through Network Analysis and Hydraulic Simulation

Resilience is usually associated to the capability of a networked infrastructure to guarantee an acceptable service even if some failure occurs. The paper presents a framework based on network analysis and software hydraulic simulation to supporting the resilience management in Water Distribution Networks increasing the sustainability of these Cyber Physical Systems while saving water and energy. The benefits of the proposed framework are evaluated on three real-world Water Distribution Networks. While graph-based measures computed on the graph associated to the physical network allows for computing network-wide measures and identifying components which are critical for the connectivity of the physical infrastructure (vulnerabilities) software hydraulic simulation is used to evaluate the impact of a failure on the supply service.