Virtual testing environment tools for railway vehicle certification

This paper describes the work performed in Work Package 6 of the European project DynoTRAIN. Its task was to investigate the effects that uncertainties present within the track and running conditions have on the simulated behaviour of a railway vehicle. Methodologies and frameworks for using virtual simulation and statistical tools, in order to reduce both the cost and time required for the certification of new or modified railway vehicles, were proposed. In particular, the project developed a virtual test track (VTT) toolkit that is capable of both generating a series of test tracks based on measurements, which can be used in vehicle virtual testing using computer simulation models, and also automatically handling the output results. The toolkit is compliant with prEN14363: 2013. The VTT was used as an experimental tool to analyse cross-correlations between track data (input) and matching vehicle response (output) based on data recorded using a test train. This paper discusses the issues encountered in the process and suggests avenues for future developments and potential use in the context of European cross-acceptance. The VTT offers benefits to the areas of design development and regulatory certification

A Deep Relational Event Additive Model for modeling patent citations

The patent citation network is a complex and dynamic system that reflects the diffusion of knowledge and innovation across different fields of technology. With this work, we aim to analyze such citation networks by developing a novel approach that leverages Relational Event Models (REMs) and Machine learning concepts. Overcoming the main limitations of REMs on analyzing large sparse networks, we propose a Deep Relational Event Additive Model (DREAM) that models the relationships between cited and citing patents as events that occur over time, capturing the dynamic nature of the patent citation network. Each predictor in the generative model is assumed to have a non-linear behavior, which has been modeled through a B-spline approach that allowed us to capture such smooth effects. By estimating the model through a stochastic gradient descent approach, we were able to efficiently estimate the parameters of the DREAM and identify the key factors that drive the network dynamics. Additionally, our spline approach allowed us to capture complex relationships between predictors through elaborate interaction effects, leading to a more accurate and comprehensive interpretation of the underlying mechanisms of the patent citation network. Our analysis revealed several interesting insights, such as the identification of time windows in which citations are more likely to happen and the relevancy of the increasing number of citations received per patent. Overall, our results demonstrate the potential of the DREAM in capturing complex dynamics that arise in a large sparse network, maintaining the features and the interpretability for which REMs are mostly famous

Modeling non-linear Effects with Neural Networks in Relational Event Models

Dynamic networks offer an insight of how relational systems evolve. However, modeling these networks efficiently remains a challenge, primarily due to computational constraints, especially as the number of observed events grows. This paper addresses this issue by introducing the Deep Relational Event Additive Model (DREAM) as a solution to the computational challenges presented by modeling non-linear effects in Relational Event Models (REMs). DREAM relies on Neural Additive Models to model non-linear effects, allowing each effect to be captured by an independent neural network. By strategically trading computational complexity for improved memory management and leveraging the computational capabilities of Graphic Processor Units (GPUs), DREAM efficiently captures complex non-linear relationships within data. This approach demonstrates the capability of DREAM in modeling dynamic networks and scaling to larger networks. Comparisons with traditional REM approaches showcase DREAM superior computational efficiency. The model potential is further demonstrated by an examination of the patent citation network, which contains nearly 8 million nodes and 100 million events

The impact of early aging on visual perception of space and time.

Visual perception of space and time has been shown to rely on context dependency, an inferential process by which the average magnitude of a series of stimuli previously experienced acts as a prior during perception. This article aims to investigate the presence and evolution of this phenomenon in early aging. Two groups of participants belonging to two different age ranges (Young Adults: average age 28.8 years old; Older Adults: average age 62.8 years old) participated in the study performing a discrimination and a reproduction task, both in a spatial and temporal conditions. In particular, they were asked to evaluate lengths in the spatial domain and interval durations in the temporal one. Early aging resulted to be associated to a general decline of the perceptual acuity, which is particularly evident in the temporal condition. The context dependency phenomenon was preserved also during aging, maintaining similar levels as those exhibited by the younger group in both space and time perception. However, the older group showed a greater variability in context dependency among participants, perhaps due to different strategies used to face a higher uncertainty in the perceptual process

Improving operation of a complex headworks system for municipal use and hydropower production by mathematical programming

The paper presents a Mixed Integer Non Linear Programming (MINLP) model of the water resources system supplying Genoa, in northern Italy. The system presently features five reservoirs, three main river intakes, and two well fields. The hydrological regime is typically Mediterranean; water availability is however relatively abundant, so that drought issues are limited, especially now that water demand from the supply sources has decreased due to reduced population, deindustrialization and to improvement in the operation and maintenance of the water distribution network. In this context, it is worthwhile considering the possibility to relax an over-conservative management of resources, justified by the experience of previous drought events, and to explore the viability of exploiting resources from reservoirs for hydropower production. The MINLP model expresses cost minimization over a 40 year time period on a monthly basis, subject to physical constraints. Costs include scarcity costs (the economic value of possible water deficits) and extraction costs from wells, minus hydropower production. The model has been written in GAMS and solved through the SBB (Simple Branch and Bound) solver. Results show that the system is able to meet demand over the 40 year hydrologic scenario with negligible water deficits and that hydropower production may be enhanced compared to present by increasing releases from reservoirs, which ultimately implies accepting keeping reservoirs emptier than presently done

Simultaneous conduction and valence band quantisation in ultra-shallow, high density doping profiles in semiconductors

We demonstrate simultaneous quantisation of conduction band (CB) and valence band (VB) states in silicon using ultra-shallow, high density, phosphorus doping profiles (so-called Si:P $\delta$-layers). We show that, in addition to the well known quantisation of CB states within the dopant plane, the confinement of VB-derived states between the sub-surface P dopant layer and the Si surface gives rise to a simultaneous quantisation of VB states in this narrow region. We also show that the VB quantisation can be explained using a simple particle-in-a-box model, and that the number and energy separation of the quantised VB states depend on the depth of the P dopant layer beneath the Si surface. Since the quantised CB states do not show a strong dependence on the dopant depth (but rather on the dopant density), it is straightforward to exhibit control over the properties of the quantised CB and VB states independently of each other by choosing the dopant density and depth accordingly, thus offering new possibilities for engineering quantum matter.Comment: 5 pages, 2 figures and supplementary materia

Drivers of the decrease of patent similarities from 1976 to 2021

The citation network of patents citing prior art arises from the legal obligation of patent applicants to properly disclose their invention. One way to study the relationship between current patents and their antecedents is by analyzing the similarity between the textual elements of patents. Patent similarity indicators have been constantly decreasing since the mid-70s. The aim of this work is to investigate the drivers of this downward trend through a general additive model and contextually propose a computationally efficient way to derive the similarity scores across pairs of patent citations leveraging on state-of-the-art tools in Natural Language Processing. We found that by using this non-linear modelling technique we are able to distinguish between distinct, temporally varying drivers of the patent similarity levels that accounts for more variation in the data ($R^2\sim 18\%$) in comparison to the previous literature. Moreover, with such corrections in place, we conclude that the trend in similarity shows a different pattern than the one presented in previous studies

Phenomenological memory-kernel master equations and time-dependent Markovian processes

Do phenomenological master equations with memory kernel always describe a non-Markovian quantum dynamics characterized by reverse flow of information? Is the integration over the past states of the system an unmistakable signature of non-Markovianity? We show by a counterexample that this is not always the case. We consider two commonly used phenomenological integro-differential master equations describing the dynamics of a spin 1/2 in a thermal bath. By using a recently introduced measure to quantify non-Markovianity [H.-P. Breuer, E.-M. Laine, and J. Piilo, Phys. Rev. Lett. 103, 210401 (2009)] we demonstrate that as far as the equations retain their physical sense, the key feature of non-Markovian behavior does not appear in the considered memory kernel master equations. Namely, there is no reverse flow of information from the environment to the open system. Therefore, the assumption that the integration over a memory kernel always leads to a non-Markovian dynamics turns out to be vulnerable to phenomenological approximations. Instead, the considered phenomenological equations are able to describe time-dependent and uni-directional information flow from the system to the reservoir associated to time-dependent Markovian processes.Comment: 5 pages, no figure