RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process.

The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transfer learning procedure allows to considerably reduce the computational time dedicated to the SS design procedure, leaving out many of the required phases. Two transfer learning methods have been proposed, evaluating their suitability to design SSs based on nonlinear dynamical models. Recurrent neural structures have been used to implement the SSs. In detail, recurrent neural networks and long short-term memory architectures have been compared in regard to their transferability. An industrial case of study has been considered, to evaluate the performance of the proposed procedures and the best compromise between SS performance and computational effort in transferring the model. The problem of labeled data scarcity in the target domain has been also discussed. The obtained results demonstrate the suitability of the proposed transfer learning methods in the design of nonlinear dynamical models for industrial systems.

Criminal networks analysis in missing data scenarios through graph distances.

Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network.

Protective Role of Vitamin D in Renal Tubulopathies.

Vitamin D is tightly linked with renal tubular homeostasis: the mitochondria of proximal convoluted tubule cells are the production site of 1α,25-dihydroxyvitamin D3. Patients with renal impairment or tubular injury often suffer from chronic inflammation. This alteration comes from oxidative stress, acidosis, decreased clearance of inflammatory cytokines and stimulation of inflammatory factors. The challenge is to find the right formula for each patient to correctly modulate the landscape of treatment and preserve the essential functions of the organism without perturbating its homeostasis. The complexity of the counter-regulation mechanisms and the different axis involved in the Vitamin D equilibrium pose a major issue on Vitamin D as a potential effective anti-inflammatory drug. The therapeutic use of this compound should be able to inhibit the development of inflammation without interfering with normal homeostasis. Megalin-Cubilin-Amnionless and the FGF23-Klotho axis represent two Vitamin D-linked mechanisms that could modulate and ameliorate the damage response at the renal tubular level, balancing Vitamin D therapy with an effect potent enough to contrast the inflammatory cascades, but which avoids potential severe side effects.