The maximum likelihood degree of Fermat hypersurfaces

We study the critical points of the likelihood function over the Fermat hypersurface. This problem is related to one of the main problems in statistical optimization: maximum likelihood estimation. The number of critical points over a projective variety is a topological invariant of the variety and is called maximum likelihood degree. We provide closed formulas for the maximum likelihood degree of any Fermat curve in the projective plane and of Fermat hypersurfaces of degree 2 in any projective space. Algorithmic methods to compute the ML degree of a generic Fermat hypersurface are developed throughout the paper. Such algorithms heavily exploit the symmetries of the varieties we are considering. A computational comparison of the different methods and a list of the maximum likelihood degrees of several Fermat hypersurfaces are available in the last section.Comment: Final version. Accepted for publication on Journal of Algebraic Statistic

Innovative Process Cycle with Zeolite (MS13X) for Post Combustion Adsorption

This paper reports the integration of Electric swing adsorption (ESA) Process in a Natural Gas Combined Cycle. This process was investigated in the MATESA FP7 project financed by European Commission. The ESA process is modelled through ASPEN Adsorption using both heat and electricity for regenerating the sorbent. The overall heat duty of the sorbent is 4 MJ/kgCO2 where half of this is recovered in the regeneration cycle. The resulting CO2 avoided is around 90% with a net electric efficiency of about 40%. The low efficiency is consequence of the higher energetic value of electricity with respect to the thermal power typically adopted in MEA regeneration. Being the first attempt of simulating this process using multiple heat sources and the recent development of sorbents, significant improvements can be expected by ESA reducing the gap with conventional post-combustion CO2 capture technologies

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

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments

An Hardware-in-the-Loop Tool for the Design of Complex Mechanical Systems Controllers

In this paper we propose an Hardware-In-the-Loop (HIL) system for industrial drives. The system allows the user to optimize the design of control strategies by emulating complex mechanical applications. In fact, with this approach it is possible to simulate the behavior of the real mechanical system, and therefore to verify a priori the effectiveness of a control strategy and to achieve a rapid prototyping of the mechatronic system. The structure of the system consists of a couple of brushless motor which are connected by a mechanical joint. In one drive the control functions related to a specific application are implemented while the other one is used to replicate the mechanical system model of the application. A modular approach has been selected in order to allow a rapid development of a given application. In particular, a library of components has been implemented both in Simulink and in an IEC61131-3 language

Modern sedimentary facies in a progradational barrier-spit system, Goro lagoon, Po delta, Italy

Barriers and spits connected to fluvial sedimentary sources represent environments which tend to evolve rapidly and experience sudden transformations, mainly driven by changes in sediment supply and path. As a consequence, the variability of facies is significant even within small sedimentary records. The 7 km long barrier-spit system facing the Goro Lagoon, and fed by the mouth of the Po di Goro, is a typical example of an accretionary coastal morphotype, suitable to describe adjacent nearshore depositional environments and their stratigraphic signatures, variability, and relationships. Thirteen short cores of sediment were sampled in order to represent the variable depositional subenvironments from the shoreface (prodelta-delta front) to the back barrier, crossing the active barrier-spit and the ancient spit arms and relative swales. The description of the modern sedimentary records, improved upon using core X-rays, has been coupled with information on the morphological changes which occurred during the period of maximum spit development (1955\u20132000), based on available aerial photos and a cartographic/topographic dataset. The results obtained allow for the description and interpretation of the depositional environments changing at the human-scale. Sediments of the upper shoreface are quite uniform, composed by evenly laminated sands; the transition between delta front and prodelta at a depth of 6m is marked by the alternation of sand and mud beds. These reflect the periodic changes in sediment supply by the river, as well as storm events. The most recent spit branch and the relative back barrier-swale environment are the results of the rapid progradation of the spit system, which implies phases of rapid longshore growth, hooked spit development, cannibalization, overwash, and breaching. Morphodynamic changes have resulted in an overlap of short sedimentary records where stratigraphic signatures are linked either to phases of sediment transport and selection by waves and tidal currents (cross-bedding, foreset, and planar laminated sands, shell imbrication, massive beds) or to phases of sedimentary stasis when biological activity is predominant (algal mat and bioturbation). Human signature is also well marked inside the stratigraphic record. Clam harvesting is carried out within the lagoon, causing the physical disturbance and winnowing of the superficial sediment, thus inducing the local formation of graded beds and shell rehash

Measuring gait speed to better identify prodromal dementia

Abstract Slow gait speed has been shown to predict incident dementia and cognitive decline in older individuals. We aimed to summarize the evidence concerning the association of slow gait speed with cognitive decline and dementia, and discuss the possible shared pathways leading to cognitive and motor impairments, under the unifying hypothesis that body and mind are intimately connected. This is a scoping review supported by a systematic search of the literature, performed on PubMed and Web of Science. Longitudinal studies providing information on the role of gait speed in the prediction of cognitive decline and dementia in cognitively intact people and in those with initial cognitive impairment were eligible. Of 39 studies selected, including overall 57,456 participants, 33 reported a significant association between gait speed and cognitive outcomes, including dementia. Neurodegenerative pathology and cerebrovascular burden may damage cerebral areas involved in both cognitive functions and motor control. At the same time, systemic conditions, characterized by higher cardiorespiratory, and metabolic and inflammatory burden, can affect a number of organs and systems involved in motor functions, including the brain, having ultimately an impact on cognition. The interplay of body and mind seems relevant during the development of cognitive decline and dementia. The measurement of gait speed may improve the detection of prodromal dementia and cognitive impairment in individuals with and without initial cognitive deficits. The potential applicability of such a measure in both clinical and research settings points at the importance of expanding our knowledge about the common underlying mechanisms of cognitive and motor decline

All-Polymer Photonic Microcavities Doped with Perylene Bisimide J-Aggregates

Thanks to exciting chemical and optical features, perylene bisimide (PBI) J-aggregates are ideal candidates to be employed for high-performance plastic photonic devices. However, they generally tend to form - stacked H-aggregates that are unsuitable for implementation in polymer resonant cavities. In this work, we demonstrate the efficient compatibilization of a tailored perylene bisimide forming robust J-aggregated supramolecular polymers into amorphous polypropylene. The new nanocomposite was then implemented into an all-polymer planar microcavity which provides strong and directional spectral redistribution of the J-aggregate photoluminescence, owing to a strong modification of the photonic states. A systematic analysis of the photoemitting processes, including photoluminescence decay and quantum yields, shows that the optical confinement in the polymeric microcavity does not introduce any additional nonradiative de-excitation pathways to those already found in the J-aggregate nanocomposite film and pave the way to PBI-based high-performance plastic photonic devices

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

Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in Machine Learning (ML), such as Bayesian Learning (BL) and Machine Learning on Graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications