An Efficient Hybrid Planning Framework for In-Station Train Dispatching

In-station train dispatching is the problem of optimising the effective utilisation of available railway infrastructures for mitigating incidents and delays. This is a fundamental problem for the whole railway network efficiency, and in turn for the transportation of goods and passengers, given that stations are among the most critical points in networks since a high number of interconnections of trains’ routes holds therein. Despite such importance, nowadays in-station train dispatching is mainly managed manually by human operators. In this paper we present a framework for solving in-station train dispatching problems, to support human operators in dealing with such task. We employ automated planning languages and tools for solving the task: PDDL+ for the specification of the problem, and the ENHSP planning engine, enhanced by domain-specific techniques, for solving the problem. We carry out a in-depth analysis using real data of a station of the North West of Italy, that shows the effectiveness of our approach and the contribution that domain-specific techniques may have in efficiently solving the various instances of the problem. Finally, we also present a visualisation tool for graphically inspecting the generated plans

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

Many complex molecular systems owe their properties to local dynamic rearrangements or fluctuations that, despite the rise of machine learning (ML) and sophisticated structural descriptors, remain often difficult to detect. Here we show an ML framework based on a new descriptor, named Local Environments and Neighbors Shuffling (LENS), which allows identifying dynamic domains and detecting local fluctuations in a variety of systems via tracking how much the surrounding of each molecular unit changes over time in terms of neighbor individuals. Statistical analysis of the LENS time-series data allows to blindly detect different dynamic domains within various types of molecular systems with, e.g., liquid-like, solid-like, or diverse dynamics, and to track local fluctuations emerging within them in an efficient way. The approach is found robust, versatile, and, given the abstract definition of the LENS descriptor, capable of shedding light on the dynamic complexity of a variety of (not necessarily molecular) systems

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

In many complex molecular systems, the macroscopic ensemble's properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g., Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g., Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g., LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales

Metabolic aspects of cardiovascular diseases: Is FoxO1 a player or a target?

The O subfamily of forkhead (FoxO) 1 is a crucial regulator of cell metabolism in several tissues, including the heart, where it is involved in cardiac regulation of glucose and lipid metabolic pathways, and endothelium, controlling the levels of some relevant biomarkers in atherosclerotic process. Despite the growing understanding of FoxO1 biology, the metabolic consequences of FoxO1 modifications and its implication in CVD, atherosclerosis and T2DM are still not incompletely described. In this review we discuss how FoxO1 affects cardiovascular pathophysiology and which of its effects should be restrained or enhanced to preserve endothelial and heart functions

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra

Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase transitions, phase equilibria, nucleation events, and defect propagation, to mention a few. A detailed comprehension of local atomic environments and of their dynamic rearrangements is essential to understand such phenomena and also to draw structure-property relationships useful to unveil how to control complex molecular systems. Considerable progress in the development of advanced structural descriptors [e.g., Smooth Overlap of Atomic Position (SOAP), etc.] has certainly enhanced the representation of atomic-scale simulations data. However, despite such efforts, local dynamic environment rearrangements still remain difficult to elucidate. Here, exploiting the structurally rich description of atomic environments of SOAP and building on the concept of time-dependent local variations, we developed a SOAP-based descriptor, TimeSOAP (τSOAP), which essentially tracks time variations in local SOAP environments surrounding each molecule (i.e., each SOAP center) along ensemble trajectories. We demonstrate how analysis of the time-series τSOAP data and of their time derivatives allows us to detect dynamic domains and track instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems. The approach is simple and general, and we expect that it will help shed light on a variety of complex dynamical phenomena

A Two-Phase ASP Encoding for Solving Rehabilitation Scheduling

The rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g., patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation’s needs. In this paper, we present a solution to rehabilitation scheduling based on Answer Set Programming (ASP), which proved to be an effective tool for solving practical scheduling problems. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution

Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles

The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species

Diabetes influences cancer risk in patients with increased carotid atherosclerosis burden

Background and aims: Atherosclerosis and cancer share several risk factors suggesting that at least in part their pathogenesis is sustained by common mechanisms. To investigate this relation we followed a group of subjects with carotid atherosclerosis at baseline up for malignancy development.Methods and results: we carried out an observational study exploring cancer incidence (study endpoint) in subjects with known carotid atherosclerosis at baseline (n = 766) without previous cancer or carotid vascular procedures. During the follow-up (160 +/- 111 weeks) 24 cancer occurred, corresponding to an overall annual incidence rate of 0.11%. 10 diagnosis of cancer occurred in individuals with a carotid stenosis >50% (n = 90) whereas 14 in patients with a carotid stenosis <50% patients (n = 676) (p < 0.001). Respect to patients without cancer, diabetes was markedly more common in subjects with cancer diagnosis during the FU (37.3%vs75.0%, p < 0.001). After controlling for classic risk factors, carotid stenosis >50% (HR = 2.831, 95%CI = 1.034-5.714; p = 0.036) and diabetes (HR = 4.831, 95%CI = 1.506-15.501; p = 0.008) remained significantly associated with cancer diagnosis.Conclusions: to our knowledge this is the first study reporting a significant risk of cancer development in subjects with diabetes and high risk of cerebrovascular events, highlighting the need of a carefully clinical screening for cancer in diabetic patients with overt carotid atherosclerosis. (C) 2019 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved

Carbon Dioxide degassing at Latera caldera (Italy): evidence of geothermal reservoir and evaluation of its potential energy

In order to test the potentiality of soil CO2 diffuse degassing measurements for the study of underground mass and heat transfer in geothermal systems detailed surveys were performed at Latera Caldera which is an excellent test site, due to the abundant available subsurface data. Over 2500 measurements of soil CO2 flux revealed that endogenous CO2 at Latera Caldera concentrates on a NE-SW band coinciding with a structural high of fractured Mesozoic limestones hosting a water-dominated high-enthalpy geothermal reservoir. The total hydrothermal CO2 degassing from the structural high has been evaluated at 350 t d-1 from an area of 3.1 km2. It has been estimated that such a CO2 release would imply a geothermal liquid flux of 263 kg s-1, with a heat release of 239 MW. The chemical and isotopic composition of the gas indicates a provenance from the geothermal reservoir and that CO2 is partly originated by thermal metamorphic decarbonation in the hottest deepest parts of the system and partly has a likely mantle origin. The ratios of CO2, H2, CH2 and CO to Ar, were used to estimate the T-P conditions of the reservoir. Results cluster at T ~ 200-300°C and PCO2 ~ 100-200 bars, close to the actual well measurements. Finally the approach proved to be an excellent tool to investigate the presence of an active geothermal reservoir at depth and that the H2-CO2-CH4-CO-Ar gas composition is a useful T-P geochemical indicator for such CO2 rich geothermal systems