Augmented Neural Lyapunov Control

Machine learning-based methodologies have recently been adapted to solve control problems. The Neural Lyapunov Control (NLC) method is one such example. This approach combines Artificial Neural Networks (ANNs) with Satisfiability Modulo Theories (SMT) solvers to synthesise stabilising control laws and to prove their formal correctness. The ANNs are trained over a dataset of state-space samples to generate candidate control and Lyapunov functions, while the SMT solvers are tasked with certifying the correctness of the Lyapunov function over a continuous domain or by returning a counterexample. Despite the approach’s attractiveness, issues can occur due to subsequent calls of the SMT module at times returning similar counterexamples, which can turn out to be uninformative and may lead to dataset overfitting. Additionally, the control network weights are usually initialised with pre-computed gains from state-feedback controllers, e.g. Linear-Quadratic Regulators. To properly perform the initialisation requires user time and control expertise. In this work, we present an Augmented NLC method that mitigates these drawbacks, removes the need for the control initialisation and further improves counterexample generation. As a result, the proposed method allows the synthesis of nonlinear (as well as linear) control laws with the sole requirement being the knowledge of the system dynamics. The ANLC is tested over challenging benchmarks such as the Lorenz attractor and outperformed existing methods in terms of successful synthesis rate. The developed framework is released open-source at: https://github.com/grande-dev/Augmented-Neural-Lyapunov-Control

Expression of μ-protocadherin is negatively regulated by the activation of the β-catenin signaling pathway in normal and cancer colorectal enterocytes.

Mu-protocadherin (MUCDHL) is an adhesion molecule predominantly expressed by colorectal epithelial cells which is markedly downregulated upon malignant transformation. Notably, treatment of colorectal cancer (CRC) cells with mesalazine lead to increased expression of MUCDHL, and is associated with sequestration of β-catenin on the plasma membrane and inhibition of its transcriptional activity. To better characterize the causal relationship between β-catenin and MUCDHL expression, we performed various experiments in which CRC cell lines and normal colonic organoids were subjected to culture conditions inhibiting (FH535 treatment, transcription factor 7-like 2 siRNA inactivation, Wnt withdrawal) or stimulating (LiCl treatment) β-catenin activity. We show here that expression of MUCDHL is negatively regulated by functional activation of the β-catenin signaling pathway. This finding was observed in cell culture systems representing conditions of physiological stimulation and upon constitutive activation of β-catenin in CRC. The ability of MUCDHL to sequester and inhibit β-catenin appears to provide a positive feedback enforcing the effect of β-catenin inhibitors rather than serving as the primary mechanism responsible for β-catenin inhibition. Moreover, MUCDHL might have a role as biomarker in the development of CRC chemoprevention drugs endowed with β-catenin inhibitory activity

Implant insertion torque value in immediate loading : a retrospective study

The aim of this study is to verify if the Insertion Torque Value (ITV) of 32 Ncm for immediate loading protocol (ILP), as indicated by literature, is still, with the advance in implant research, a real significant cut-off for long-term implant survival. In this retrospective study, data from 224 patients that during three years of clinical practice, were submitted to the insertion of 322 implants with immediate loading protocol, have been recorded, pooled and analyzed. Data were organized based on Insertion Torque Value (ITV): > 32 Ncm (CG) and 32 Ncm are still characterized by a lower crestal bone resorption, there are no statistically significant differences among the two groups for what concerning the failure rate during the 2 years of follow-up and OR. These results permit us to suppose that the cut-off of ITV >32 Ncm for immediate loading implants, could be reduced to inferior values. However further studies are necessary to indicate precise clinical guidelines

Augmented Neural Lyapunov Control

Machine learning-based methodologies have recently been adapted to solve control problems. The Neural Lyapunov Control (NLC) method is one such example. This approach combines Artificial Neural Networks (ANNs) with Satisfiability Modulo Theories (SMT) solvers to synthesise stabilising control laws and to prove their formal correctness. The ANNs are trained over a dataset of state-space samples to generate candidate control and Lyapunov functions, while the SMT solvers are tasked with certifying the correctness of the Lyapunov function over a continuous domain or by returning a counterexample. Despite the approach’s attractiveness, issues can occur due to subsequent calls of the SMT module at times returning similar counterexamples, which can turn out to be uninformative and may lead to dataset overfitting. Additionally, the control network weights are usually initialised with pre-computed gains from state-feedback controllers, e.g. Linear-Quadratic Regulators. To properly perform the initialisation requires user time and control expertise. In this work, we present an Augmented NLC method that mitigates these drawbacks, removes the need for the control initialisation and further improves counterexample generation. As a result, the proposed method allows the synthesis of nonlinear (as well as linear) control laws with the sole requirement being the knowledge of the system dynamics. The ANLC is tested over challenging benchmarks such as the Lorenz attractor and outperformed existing methods in terms of successful synthesis rate. The developed framework is released open-source at: https://github.com/grande-dev/Augmented-Neural-Lyapunov-Control

Mortality Related to Chronic Obstructive Pulmonary Disease during the COVID-19 Pandemic: An Analysis of Multiple Causes of Death through Different Epidemic Waves in Veneto, Italy

Mortality related to chronic obstructive pulmonary disease (COPD) during the COVID-19 pandemic is possibly underestimated by sparse available data. The study aimed to assess the impact of the pandemic on COPD-related mortality by means of time series analyses of causes of death data. We analyzed the death certificates of residents in Veneto (Italy) aged ≥40 years from 2008 to 2020. The age-standardized rates were computed for COPD as the underlying cause of death (UCOD) and as any mention in death certificates (multiple cause of death-MCOD). The annual percent change (APC) in the rates was estimated for the pre-pandemic period. Excess COPD-related mortality in 2020 was estimated by means of Seasonal Autoregressive Integrated Moving Average models. Overall, COPD was mentioned in 7.2% (43,780) of all deaths. From 2008 to 2019, the APC for COPD-related mortality was -4.9% (95% CI -5.5%, -4.2%) in men and -3.1% in women (95% CI -3.8%, -2.5%). In 2020 compared to the 2018-2019 average, the number of deaths from COPD (UCOD) declined by 8%, while COPD-related deaths (MCOD) increased by 14% (95% CI 10-18%), with peaks corresponding to the COVID-19 epidemic waves. Time series analyses confirmed that in 2020, COPD-related mortality increased by 16%. Patients with COPD experienced significant excess mortality during the first year of the pandemic. The decline in COPD mortality as the UCOD is explained by COVID-19 acting as a competing cause, highlighting how an MCOD approach is needed

Poly(methyl methacrylate) as Healing Agent for Carbon Fibre Reinforced Epoxy Composites

Self-healing materials offer a potential solution to the problem of damage to fibre-reinforced plastics (FRPs) by allowing for the in-service repair of composite materials at a lower cost, in less time, and with improved mechanical properties compared to traditional repair methods. This study investigates for the first time the use of poly(methyl methacrylate) (PMMA) as a self-healing agent in FRPs and evaluates its effectiveness both when blended with the matrix and when applied as a coating to carbon fibres. The self-healing properties of the material are evaluated using double cantilever beam (DCB) tests for up to three healing cycles. The blending strategy does not impart a healing capacity to the FRP due to its discrete and confined morphology; meanwhile, coating the fibres with the PMMA results in healing efficiencies of up to 53% in terms of fracture toughness recovery. This efficiency remains constant, with a slight decrease over three subsequent healing cycles. It has been demonstrated that spray coating is a simple and scalable method of incorporating a thermoplastic agent into an FRP. This study also compares the healing efficiency of specimens with and without a transesterification catalyst and finds that the catalyst does not increase the healing efficiency, but it does improve the interlaminar properties of the material.This research was funded by the State Research Agency of Spain (AEI), under grant number PID2019-107501RB-I00/AEI/10.13039/50110001103

Characterization of virgin olive oils produced with autochthonous Galician varieties

The interest of Galician oil producers (NW Spain) in recovering the ancient autochthonous olive varieties Brava and Mansa has increased substantially in recent years. Virgin olive oils produced by co-crushing both varieties in two different proportions, reflecting the usual and most common practice adopted in this region, have gradually emerged for the production of virgin olive oils. Herein, the sensory and chemical characteristics of such oils were characterized by quality and genuineness-related parameters. The results of chemical analysis are discussed in terms of their effective contribution to the sensory profile, which suggests useful recommendations for olive oil producers to improve the quality of oils. Antioxidant compounds, together with aromas and coloured pigments were determined, and their contribution in determining the functional value and the sensory properties of oils was investigated. In general, given the high levels of phenolic compounds (ranging between 254 and 375 mg/kg oil), tocopherols (about 165 mg/kg oil) and carotenoids (10-12 mg/kg oil); these are oils with long stability, especially under dark storage conditions, because stability is reinforced with the contribution of chlorophylls (15-22 mg/kg oil). A major content of phenolic compounds, as well as a predominance of trans-2-hexen-1-al within odor-active compounds (from 897 to 1645 μg/kg oil), responsible for bitter sensory notes. This characterization allows to developing new antioxidant-rich and flavour-rich VOOs, when co-crushing with a higher proportion of Brava olives, satisfying the consumers' demand in having access to more healthy dishes and peculiar sensory attributes