Time-domain Fourier optics for polarization-modedispersion compensation

We report on a novel technique to compensate for all-order polarization-mode dispersion. By means of this technique, based on a suitable combination of phase modulation and group-velocity dispersion, we compensated for as much as 60 ps of differential group delay that affected a 10-Gbit/s return-to-zero data stream

Evaporative CO2 cooling using microchannels etched in silicon for the future LHCb vertex detector

The extreme radiation dose received by vertex detectors at the Large Hadron Collider dictates stringent requirements on their cooling systems. To be robust against radiation damage, sensors should be maintained below -20 degree C and at the same time, the considerable heat load generated in the readout chips and the sensors must be removed. Evaporative CO2 cooling using microchannels etched in a silicon plane in thermal contact with the readout chips is an attractive option. In this paper, we present the first results of microchannel prototypes with circulating, two-phase CO2 and compare them to simulations. We also discuss a practical design of upgraded VELO detector for the LHCb experiment employing this approach.Comment: 12 page

Development of interconnected silicon micro-evaporators for the on-detector electronics cooling of the future ITS detector in the ALICE experiment at LHC

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.The design of the future High Energy Physics (HEP) particle detectors for the upgrade of the LHC (Large Hadron Collider) experiments at CERN (European Organization for Nuclear Research) is pushing technological frontiers to the limit trying to reach unprecedented accuracy in particles identification and particle production dynamics in ultra-relativistic hadron collisions. The thermal management of the on-detector electronics and the development of low mass integrated cooling systems have become a crucial task in the design of silicon tracking detectors for HEP applications. In this paper, we present a novel concept of low mass interconnected silicon microchannel devices for the future Inner Tracking System of the ALICE (A Large Ion Collider Experiment) detector at LHC. This innovative design achieves the requirements of the detector while minimizing the total material budget

Dynamic control strategies for a solar-ORC system using first-law dynamic and data-driven machine learning models

In this study, we developed and assessed the potential of dynamic control strategies for a domestic scale 1-kW solar thermal power system based on a non-recuperated organic Rankine cycle (ORC) engine coupled to a solar energy system. Such solar-driven systems suffer from part-load performance deterioration due to diurnal and inter-seasonal fluctuations in solar irradiance and ambient temperature. Real-time control strategies for adjusting the operating parameters of these systems have shown great potential to optimise their transient response to time-varying conditions, thus allowing significant gains in the power output delivered by the system. Dynamic model predictive control strategies rely on the development of computationally efficient, fast-solving models. In contrast, traditional physics-based dynamic process models are often too complex to be used for real-time controls. Machine learning techniques (MLTs), especially deep learning artificial neural networks (ANN), have been applied successfully for controlling and optimising nonlinear dynamic systems. In this study, the solar system was controlled using a fuzzy logic controller with optimised decision parameters for maximum solar energy absorption. For the sake of obtaining the optimal ORC thermal efficiency at any instantaneous time, particularly during part-load operation, the first-law ORC model was first replaced by a fast-solving feedforward network model, which was then integrated with a multi-objective genetic algorithm, such that the optimal ORC operating parameters can be obtained. Despite the fact that the feedforward network model was trained using steady-state ORC performance data, it showed comparable results compared with the first-principle model in the dynamic context, with a mean absolute error of 3.3 percent for power prediction and 0.186 percentage points for efficiency prediction

Integration of New Technologies and Alternative Methods in Laboratory-Based Scenarios

In this study, we report a preliminary requirements analysis to recognize needs and possibilities for integrating new technologies and methods for lab-based learning in the field of Industry 4.0 and Internet of Things. To this aim, different scenarios, such as real, remote and virtual labs, are considered to be addressable within an integrated learning environment that focuses on alternative methods (i.e. Serious Games, Self-Regulated and Collaborative Learning) and new technologies (i.e. Open Badges, Mixed Reality and Learning Analytics). To support the design of the laboratory-based learning environment, qualitative interviews were conducted with both expert lecturers and relevant students in the field of engineering, to provide complementary perspectives. These interviews were carried out to analyze the requirements, and to identify possible benefits that relevant stakeholders expect by using these teaching and learning methods and technologies. A qualitative content analysis has been started on the interviews to define which is the perception of the new technologies and teaching methods. The different points of view about technologies and methods coming from expert lecturers’ and relevant students’ interviews are provided

The Impact of Digital Technologies and Sustainable Practices on Circular Supply Chain Management

Background: This study investigates how firms can enhance the functionality of their circular supply chains (CSCs) by adopting a portfolio of sustainable practices as well as digital technologies to increase performance. It analyzes the benefits that firms can obtain when investing in specific technologies to boost the impact of technologies and sustainable practices on CSCs, and further increase performance. Methods: We test several hypotheses by using structural equation modeling as well as multi-group analysis to verify whether CSCs can be achieved through sustainable practices and technologies and improve the firms' performance. Results: The empirical results partially support the research hypotheses. While the main research hypotheses are fully supported, the analysis of single digital technologies reveals that only a few solutions can contribute to both the management and the improvement of the CSC. Conclusions: Our findings demonstrate that the identification of green suppliers and ad hoc environmental regulations, combined with attention to the origin and provenance of raw materials, can promote a CSC. Moreover, transportation management systems (TMS) and the internet of things (IoT) are efficient technologies for managing transportation and product flow in the CSC. Furthermore, machine learning (ML) is effective in making positive green decisions, and 3D printing can extend product life

Preimplantation biopsy predicts delayed graft function, glomerular filtration rate and long-term graft survival of transplanted kidneys

Background The predictive value of preimplantation biopsies for long-term graft function is often limited by conflicting results. The aim of this study was to evaluate the influence of time-zero graft biopsy histological scores on early and late graft function, graft survival and patient survival, at different time points. Methods We retrospectively analyzed 284 preimplantation biopsies at a single center, in a cohort of recipients with grafts from live and deceased donors (standard and nonstandard), and their impact in posttransplant renal function after a mean follow-up of 7 years (range 1–16). Implantation biopsy score (IBS), a combination score derived from 4 histopathological aspects, was determined from each sample. The correlation with incidence of delayed graft function (DGF), creatinine clearance (1st, 3rd and 5th posttransplant year) and graft and patient survival at 1 and 5 years were evaluated. Results Preimplantation biopsies provided somewhat of a prognostic index of early function and outcome of the transplanted kidney in the short and long term. In the immediate posttransplantation period, the degree of arteriolosclerosis and interstitial fibrosis correlated better with the presence of DGF. IBS values between 4 and 6 were predictive of worst renal function at 1st and 3rd years posttransplant and 5-year graft survival. The most important histological finding, in effectively transplanted grafts, was the grade of interstitial fibrosis. Patient survival was not influenced by IBS. Conclusions Higher preimplantation biopsy scores predicted an increased risk of early graft losses, especially primary nonfunction. Graft survival (at 1st and 5th years after transplant) but not patient survival was predicted by IBS