Detection of Zak phases and topological invariants in a chiral quantum walk of twisted photons

Topological insulators are fascinating states of matter exhibiting protected edge states and robust quantized features in their bulk. Here, we propose and validate experimentally a method to detect topological properties in the bulk of one-dimensional chiral systems. We first introduce the mean chiral displacement, and we show that it rapidly approaches a multiple of the Zak phase in the long time limit. Then we measure the Zak phase in a photonic quantum walk, by direct observation of the mean chiral displacement in its bulk. Next, we measure the Zak phase in an alternative, inequivalent timeframe, and combine the two windings to characterize the full phase diagram of this Floquet system. Finally, we prove the robustness of the measure by introducing dynamical disorder in the system. This detection method is extremely general, as it can be applied to all one-dimensional platforms simulating static or Floquet chiral systems.Comment: 10 pages, 7 color figures (incl. appendices) Close to the published versio

Numerical fluid dynamics simulation for drones’ chemical detection

The risk associated with chemical, biological, radiological, nuclear, and explosive (CBRNe) threats in the last two decades has grown as a result of easier access to hazardous materials and agents, potentially increasing the chance for dangerous events. Consequently, early detection of a threat following a CBRNe event is a mandatory requirement for the safety and security of human operators involved in the management of the emergency. Drones are nowadays one of the most advanced and versatile tools available, and they have proven to be successfully used in many different application fields. The use of drones equipped with inexpensive and selective detectors could be both a solution to improve the early detection of threats and, at the same time, a solution for human operators to prevent dangerous situations. To maximize the drone’s capability of detecting dangerous volatile substances, fluid dynamics numerical simulations may be used to understand the optimal configuration of the detectors positioned on the drone. This study serves as a first step to investigate how the fluid dynamics of the drone propeller flow and the different sensors position on-board could affect the conditioning and acquisition of data. The first consequence of this approach may lead to optimizing the position of the detectors on the drone based not only on the specific technology of the sensor, but also on the type of chemical agent dispersed in the environment, eventually allowing to define a technological solution to enhance the detection process and ensure the safety and security of first responders

Relevance of the Operator’s Experience in Conditioning the Static Computer-Assisted Implantology: A Comparative In Vitro Study with Three Different Evaluation Methods

The present study aimed to evaluate the influence of manual expertise on static computer-aided implantology (s-CAI) in terms of accuracy and operative timings. After the cone-beam CT (CBCT) scanning of eleven mandibular models, a full-arch rehabilitation was planned, and two different skilled operators performed s-CAI. The distances between the virtual and actual implant positions were calculated considering the three spatial vectorial axes and the three-dimensional Euclidean value for the entry (E) and apical (A) points, along with the axis orientation differences (Ax). These values emerged from the overlapping of the pre-op CBCT to post-op CBCT data (method 1), from scanning the data from the laboratory scanner (method 2), and from the intra-oral scanner (method 3) and were correlated with the operators’ expertise and operative timings. The mean values for accuracy from the three methods were: E = 0.57 (0.8, 0.45, 0.47) mm, A = 0.6 (0.8, 0.48, 0.49) mm, and Ax 1.04 (1.05,1.03,1.05) ° for the expert operator; and E = 0.8 (0.9, 0.87, 0.77), A = 0.95 (1.02, 0.95, 0.89), and Ax =1.64 (1.78, 1.58, 1.58) for the novice. The mean value of the operative timings was statistically inferior for the expert operator (p < 0.05), with an improved accuracy over time for both operators. A significant difference (p < 0.05) emerged between method 1 and methods 2 and 3 for seven of the nine variables, without differences between the evaluations from the two scanners. The support from digital surgical guides does not eliminate the importance of manual expertise for the reliability and the shortening of the surgical procedure, and it requires a learning pathway over time

Lesson learned from the recovery of an orphan source inside a maritime cargo: analysis of the nuclear instrumentations used, and measures realized during the operations

In this paper, the authors analyze the case study of the recovery of an orphan source of 60Co inside a maritime cargo full of metal wastes in the Italian Harbor of Genova carried out by the Italian Fire Fighters. Orphan radioactive sources or Radiological Dispersal Devices are a critical security issue in large geographical areas, and they result in a safety concern for people who may become accidentally exposed to ionizing radiation. The abandonment of orphan sources can usually be related to three factors: human errors, cost reasons (in order to avoid the payment of disposal procedures), or malevolent purposes (like the production of dirty bombs). The present data concern the nuclear safety measures implemented during the recovery event and the pool of procedures carried out in order to reduce the risks for the involved harbor operators. Following data collection and analysis, an important lesson about the management of such events and scenarios can be learned

Enhancing radiation detection by drones through numerical fluid dynamics simulations

This study addresses the optimization of the location of a radioactive-particle sensor on a drone. Based on the analysis of the physical process and of the boundary conditions introduced in the model, computational fluid dynamics simulations were performed to analyze how the turbulence caused by drone propellers may influence the response of the sensors. Our initial focus was the detection of a small amount of radioactivity, such as that associated with a release of medical waste. Drones equipped with selective low-cost sensors could be quickly sent to dangerous areas that first responders might not have access to and be able to assess the level of danger in a few seconds, providing details about the source terms to Radiological-Nuclear (RN) advisors and decision-makers. Our ultimate application is the simulation of complex scenarios where fluid-dynamic instabilities are combined with elevated levels of radioactivity, as was the case during the Chernobyl and Fukushima nuclear power plant accidents. In similar circumstances, accurate mapping of the radioactive plume would provide invaluable input-data for the mathematical models that can predict the dispersion of radioactivity in time and space. This information could be used as input for predictive models and decision support systems (DSS) to get a full situational awareness. In particular, these models may be used either to guide the safe intervention of first responders or the later need to evacuate affected regions

Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations

This study addresses the optimization of the location of a radioactive-particle sensor on a drone. Based on the analysis of the physical process and of the boundary conditions introduced in the model, computational fluid dynamics simulations were performed to analyze how the turbulence caused by drone propellers may influence the response of the sensors. Our initial focus was the detection of a small amount of radioactivity, such as that associated with a release of medical waste. Drones equipped with selective low-cost sensors could be quickly sent to dangerous areas that first responders might not have access to and be able to assess the level of danger in a few seconds, providing details about the source terms to Radiological-Nuclear (RN) advisors and decision-makers. Our ultimate application is the simulation of complex scenarios where fluid-dynamic instabilities are combined with elevated levels of radioactivity, as was the case during the Chernobyl and Fukushima nuclear power plant accidents. In similar circumstances, accurate mapping of the radioactive plume would provide invaluable input-data for the mathematical models that can predict the dispersion of radioactivity in time and space. This information could be used as input for predictive models and decision support systems (DSS) to get a full situational awareness. In particular, these models may be used either to guide the safe intervention of first responders or the later need to evacuate affected regions

Ion diffusion modelling of Fricke-agarose dosemeter gels

In Fricke-agarose gels, an accurate determination of the spatial dose distribution is hindered by the diffusion of ferric ions. In this work, a model was developed to describe the diffusion process within gel samples of finite length and, thus, permit the reconstruction of the initial spatial distribution of the ferric ions. The temporal evolution of the ion concentration as a function of the initial concentration is derived by solving Fick's second law of diffusion in two dimensions with boundary reflections. The model was applied to magnetic resonance imaging data acquired at high spatial resolution (0.3 mm) and was found to describe accurately the observed diffusion effect

DTI and MR Volumetry of Hippocampus-PC/PCC Circuit: In Search of Early Micro- and Macrostructural Signs of Alzheimers's Disease

Hippocampal damage, by DTI or MR volumetry, and PET hypoperfusion of precuneus/posterior cingulate cortex (PC/PCC) were proposed as biomarkers of conversion from preclinical (MCI) to clinical stage of Alzheimer's disease (AD). This study evaluated structural damage, by DTI and MR volumetry, of hippocampi and tracts connecting hippocampus to PC/PCC (hipp-PC/PCC) in 10 AD, 10 MCI, and 18 healthy controls (CTRL). Normalized volumes, mean diffusivity (MD), and fractional anisotropy (FA) were obtained for grey matter (GM), white matter (WM), hippocampi, PC/PCC, and hipp-PC/PCC tracts. In hippocampi and hipp-PC/PCC tracts, decreased volumes and increased MD were found in AD versus CTRL (P < .001). The same results with lower significance (P < .05) were found in MCI versus CTRL. Verbal memory correlated (P < .05) in AD with left hippocampal and hipp-PC/PCC tract MD, and in MCI with FA of total WM. Both DTI and MR volumetry of hippocampi and hipp-PC/PCC tracts detect early signs of AD in MCI patients

Design of miniaturized sensors for a mission-oriented uav application: A new pathway for early warning

In recent decades, the increasing threats associated with Chemical and Radiological (CR) agents prompted the development of new tools to detect and collect samples without putting in danger first responders inside contaminated areas. A particularly promising branch of these technological developments relates to the integration of different detectors and sampling systems with Unmanned Aerial Vehicles (UAV). The adoption of this equipment may bring significant benefits for both military and civilian implementations. For instance, instrumented UAVs could be used in support of specialist military teams such as Sampling and Identification of Biological, Chemical and Radiological Agents (SIBCRA) team, tasked to perform sampling in contaminated areas, detecting the presence of CR substances in field and then confirming, collecting and evaluating the effective threats. Furthermore, instrumented UAVs may find dual-use application in the civil world in support of emergency teams during industrial accidents and in the monitoring activities of critical infrastructures. Small size drones equipped with different instruments for detection and collection of samples may enable, indeed, several applications, becoming a tool versatile and easy to use in different fields, and even featuring equipment normally utilized in manual operation. The authors hereby present the design of miniaturized sensors for a mission-oriented UAV application and the preliminary results from an experimental campaign performed in 2020

Epidemiology of intensive care unit-acquired sepsis in Italy: results of the SPIN-UTI network

BACKGROUND: Sepsis is the major cause of mortality from any infectious disease worldwide. Sepsis may be the result of a healthcare associated infection (HAI): the most frequent adverse events during care delivery especially in Intensive Care Units (ICUs). The main aim of the present study was to describe the epidemiology of ICU-acquired sepsis and related outcomes among patients enrolled in the framework of the Italian Nosocomial Infections Surveillance in ICUs - SPIN-UTI project. STUDY DESIGN: Prospective multicenter study. METHODS: The SPIN-UTI network adopted the European protocols for patient-based HAI surveillance. RESULTS: During the five editions of the SPIN-UTI project, from 2008 to 2017, 47.0% of HAIs has led to sepsis in 832 patients. Overall, 57.0% episodes were classified as sepsis, 20.5% as severe sepsis and 22.5% as septic shock. The most common isolated microorganisms from sepsis episodes were Acinetobacter baumannii, Klebsiella pneumoniae and Pseudomonas aeruginosa. The case fatality rate increased with the severity of sepsis and the mean length of ICU-stay was significantly higher in patients with ICU-acquired sepsis than in patients without. CONCLUSION: Our study provides evidence that ICU-acquired sepsis occurs frequently in Italian ICU patients and is associated with a high case fatality rate and increased length of stay. However, in order to explain these findings further analyses are needed in this population of ICU patient