Thermalization processes induced by quantum monitoring in multilevel systems

We study the heat statistics of a multilevel N-dimensional quantum system monitored by a sequence of projective measurements. The late-time, asymptotic properties of the heat characteristic function are analyzed in the thermodynamic limit of a high, ideally infinite, number M of measurements (M→∞). In this context, the conditions allowing for an infinite-temperature thermalization (ITT), induced by the repeated monitoring of the quantum system, are discussed. We show that ITT is identified by the fixed point of a symmetric random matrix that models the stochastic process originated by the sequence of measurements. Such fixed point is independent on the nonequilibrium evolution of the system and its initial state. Exceptions to ITT, which we refer to as partial thermalization, take place when the observable of the intermediate measurements is commuting (or quasicommuting) with the Hamiltonian of the quantum system or when the time interval between measurements is smaller or comparable with the system energy scale (quantum Zeno regime). Results on the limit of infinite-dimensional Hilbert spaces (N→∞), describing continuous systems with a discrete spectrum, are also presented. We show that the order of the limits M→∞ and N→∞ matters: When N is fixed and M diverges, then ITT occurs. In the opposite case, the system becomes classical, so that the measurements are no longer effective in changing the state of the system. A nontrivial result is obtained fixing M/N2 where instead partial ITT occurs. Finally, an example of partial thermalization applicable to rotating two-dimensional gases is presented

Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets

Background and objectivesAutomated segmentation and tracking of surgical instruments and catheters under X-ray fluoroscopy hold the potential for enhanced image guidance in catheter-based endovascular procedures. This article presents a novel method for real-time segmentation of catheters and guidewires in 2d X-ray images. We employ Convolutional Neural Networks (CNNs) and propose a transfer learning approach, using synthetic fluoroscopic images, to develop a lightweight version of the U-Net architecture. Our strategy, requiring a small amount of manually annotated data, streamlines the training process and results in a U-Net model, which achieves comparable performance to the state-of-the-art segmentation, with a decreased number of trainable parameters. MethodsThe proposed transfer learning approach exploits high-fidelity synthetic images generated from real fluroscopic backgrounds. We implement a two-stage process, initial end-to-end training and fine-tuning, to develop two versions of our model, using synthetic and phantom fluoroscopic images independently. A small number of manually annotated in-vivo images is employed to fine-tune the deepest 7 layers of the U-Net architecture, producing a network specialized for pixel-wise catheter/guidewire segmentation. The network takes as input a single grayscale image and outputs the segmentation result as a binary mask against the background. ResultsEvaluation is carried out with images from in-vivo fluoroscopic video sequences from six endovascular procedures, with different surgical setups. We validate the effectiveness of developing the U-Net models using synthetic data, in tests where fine-tuning and testing in-vivo takes place both by dividing data from all procedures into independent fine-tuning/testing subsets as well as by using different in-vivo sequences. Accurate catheter/guidewire segmentation (average Dice coefficient of ~ 0.55, ~ 0.26 and ~ 0.17) is obtained with both U-Net models. Compared to the state-of-the-art CNN models, the proposed U-Net achieves comparable performance ( ± 5% average Dice coefficients) in terms of segmentation accuracy, while yielding a 84% reduction of the testing time. This adds flexibility for real-time operation and makes our network adaptable to increased input resolution. ConclusionsThis work presents a new approach in the development of CNN models for pixel-wise segmentation of surgical catheters in X-ray fluoroscopy, exploiting synthetic images and transfer learning. Our methodology reduces the need for manually annotating large volumes of data for training. This represents an important advantage, given that manual pixel-wise annotations is a key bottleneck in developing CNN segmentation models. Combined with a simplified U-Net model, our work yields significant advantages compared to current state-of-the-art solutions

Simulation-Based Design of Reconfigurable Moulds for Injection Overmoulding

The injection moulding process enables the production of complex shaped parts, thanks to the accurate kinematics and the tight tolerances of the mould. This process is suitable for large batch production, leading to reduced single part costs, but involves high initial investments. The life of a mould can be increased by exploiting reconfigurable cavity inserts. So, a design method has been conceived for reconfigurable injection moulds by integrating Design for Assembly and Computer Aided Engineering techniques. From the early phases of a systematic design approach, the simulation models are configured with the different geometries as requested by design specifications. The mould inserts are designed with standard features in order to be quickly changed. A case study on a reconfigurable mould for the overmoulding of polymer wheels to be produced in different sizes is presented. The simulations with Moldex3D software are finally compared with the experimental data from the actual production

Quantum-heat fluctuation relations in three-level systems under projective measurements

We study the statistics of energy fluctuations in a three-level quantum system subject to a sequence of projective quantum measurements. We check that, as expected, the quantum Jarzynski equality holds provided that the initial state is thermal. The latter condition is trivially satisfied for two-level systems, while this is generally no longer true for N-level systems, with N > 2. Focusing on three-level systems, we discuss the occurrence of a unique energy scale factor \u3b2eff that formally plays the role of an effective inverse temperature in the Jarzynski equality. To this aim, we introduce a suitable parametrization of the initial state in terms of a thermal and a non-thermal component. We determine the value of \u3b2eff for a large number of measurements and study its dependence on the initial state. Our predictions could be checked experimentally in quantum optics

Computer-Aided Tolerancing Analysis of a High-Performance Car Engine Assembly

This paper proposes the analysis of the tolerances (values, types, datum) and their effects on a mechanical assembly, as a high-performance car engine, by means of a Computer-Aided Tolerancing software. The 3D tolerance stack-ups are investigated to assess the fulfillment of the functional requirements as well as the performance specifications of the assembly. Moreover, after identifying the tolerances that mainly affect the product variability, we finally propose some corrective actions on the tolerances and assess their functional allocation, tightening or relaxing their values, ensuring assemblability and cost reduction

A Review of Automotive Spare-Part Reconstruction Based on Additive Manufacturing

In the Industry 4.0 scenario, additive manufacturing (AM) technologies play a fundamental role in the automotive field, even in more traditional sectors such as the restoration of vintage cars. Car manufacturers and restorers benefit from a digital production workflow to reproduce spare parts that are no longer available on the market, starting with original components, even if they are damaged. This review focuses on this market niche that, due to its growing importance in terms of applications and related industries, can be a significant demonstrator of future trends in the automotive supply chain. Through selected case studies and industrial applications, this study analyses the implications of AM from multiple perspectives. Firstly, various types of AM processes are used, although some are predominant due to their cost-effectiveness and, therefore, their better accessibility and wide diffusion. In some applications, AM is used as an intermediate process to develop production equipment (so-called rapid tooling), with further implications in the digitalisation of conventional primary technologies and the entire production process. Secondly, the additive process allows for on-demand, one-off, or small-batch production. Finally, the ever-growing variety of spare parts introduces new problems and challenges, generating constant opportunities to improve the finish and performance of parts, as well as the types of processes and materials, sometimes directly involving AM solution providers