Reducing parasitic resonances in particle accelerators components by broadband Higher Order Mode couplers

In particle accelerator components, parasitic resonances must be reduced because they heat up the equipment and cause beam instabilities. In this paper, a method for designing and characterizing Higher Order Mode (HOM) couplers for reducing such resonances in a broad bandwidth is proposed. A case study is considered for a specific component, called QuattroTank, showing geometrical discontinuities and thus causing significant electro-magnetic resonances. Results of numerical simulation and experimental emulation prove the capability of the proposed method to reduce the peaks and the $Q-factor of the resonances

Latest Advancements in SSVEPs Classification for Single-Channel, Extended Reality-based Brain-Computer Interfaces

This work details the latest advancements on a single-channel, reactive Brain-Computer Interfaces developed at the Interdepartmental Research Center in Health Management and Innovation in Healthcare (CIRMIS) of the University of Naples Federico II. The proposed instrumentation is based on Extended Reality (XR) and exploits the acquisition and classification of the Steady-State Visually Evoked Potentials (SSVEPs). In particular, an XR headset is employed for generating the flickering stimuli necessary to the SSVEP elicitation. The users brain signals are captured by means of a highly wearable and portable electroencephalografic acquisition unit, which is connected to a portable processing unit in charge of processing in real time the incoming data. In this way, a deeper interaction between users and external devices with respect to traditional architectures is guaranteed. The classification capability of the proposed instrument has been significantly improved over the years. Currently, in fact, a classification accuracy up to 90 % is obtained with at least 2 s of acquisition time

An Augmented Reality-Based Solution for Monitoring Patients Vitals in Surgical Procedures

In this work, an augmented reality (AR) system is proposed to monitor in real time the patient's vital parameters during surgical procedures. This system is characterised metrologically in terms of transmission error rates and latency. These specifications are relevant for ensuring real-time response. The proposed system automatically collects data from the equipment in the operating room (OR), and displays them in AR. The system was designed, implemented and validated through experimental tests carried out using a set of Epson Moverio BT-350 AR glasses to monitor the output of a respiratory ventilator and a patient monitor in the OR

Transport and noise properties of YBCO nanowire based nanoSQUIDs

The development of quantum limited magnetic flux sensors has recently gained a lot of attention for the possibility of detecting the magnetic moment of nanoscaled systems. Here, the ultimate goal is the observation of a single spin. Such sensors are of fundamental importance for applications, ranging from spintronics and spin-based quantum information processing, to fundamental studies of nano-magnetism in molecules and magnetic nanoclusters. A nano-scale superconducting quantum interference device (nanoSQUID) is indeed a promising candidate to reach this ambitious goal. Nanowires, fabricated of high critical temperature superconductors (HTS), have been shown to be a valid candidate for the realization of nanoSQUIDs. A crucial requirement to achieve the necessary flux sensitivity and spatial resolution, is a SQUID loop on the nanometer scale. Moreover, HTS nanowire-based SQUIDs in combination with large area pickup loops or flux transformers might become instrumental in magnetometer applications, such as magneto encephalography and low field magnetic resonance imaging, where low intrinsic magnetic field noise is required. In this review we will give a survey on the state of the art of YBa2Cu3O7-δ thin film nanowires and their implementation in low noise nanoSQUIDs and magnetometers

Preliminary impedance spectroscopy study for carious lesions detection

This preliminary study proposes the use of impedance spectroscopy as additional diagnostic method in clinical practice to assess carious lesions. The carious process leads to a characteristic loss of mineral and a subsequent increased porosity, which results in a higher liquid content than sound tissue. The absorbed liquid contains different ions coming from the oral environment which, together with the increased porosity, contribute to change the impedance of the tissue. Impedance measurement is able to detect such tissue modifications and, therefore, it can be a suitable approach for assessing the presence and the status of carious processes on teeth. Moreover, compared to other diagnostic techniques it is more promising, also for the development of in-vivo measurements, owing to its safety, reliability, simplicity, rapid response, cost-effective, robust, and adequate detection limit. This study compares impedance spectroscopy measurements collected by using two different types of probes for monitoring teeth with and without carious lesions. The authors used a Ni-Cr wire electrode with a diameter of 0.5 mm, and a hydrogel agar probe with a diameter of 5 mm. Impedance measurements were carried out in-vitro by means of the Ivium-n-Stat potentiostat with a two-electrode setup, on the occlusal surfaces of teeth with and without carious lesions; then, the impedance spectra were recorded and analyzed. The preliminary results highlight that both experimental probes allow detecting a shift in impedance phase spectra, which happens at different frequencies and can be correlated to healthy teeth and the carious lesions, respectively

Composition of Arthropod Species Assemblages in Bt-expressing and Near Isogenic Eggplants in Experimental Fields

The environmental impact of genetically modified (GM) plants in experimental fields has been examined in several ways, in particular with respect to the dynamics of specific nontarget organisms. The approach of sampling for biodiversity in agroecosystems to compare complex patterns could also be useful in studying potential disruptions caused by GM crops. In this study, we set up replicated field plots of Bt-expressing eggplants and near isogenic untransformed eggplants as a control. We monitored the presence and abundance of herbivore and predator arthropods in weekly visual samplings of the plant canopy for three growing seasons (2001-2003). Insect species were pooled in organismal taxonomic units (OTUs); three multivariate methods were used to compare species assemblage as an estimate of insect biodiversity. This multistep statistical approach proved to be efficient in recognizing association patterns, as evidenced by the data for the target species Leptinotarsa decemlineata Say (Coleoptera: Chrysomelidae) clearly showing a significant association with the control plots. All the analyses indicate a comparable species assemblage between transgenic and near isogenic eggplant areas. Our results suggest that some taxa may warrant more specific study. For example, Alticinae beetles (Coleoptera: Chrysomelidae) were alternatively more abundant in either of the two treatments, and their overall abundance was significantly higher on transgenic eggplants. In light of these results and because of their taxonomic proximity to the target species, these herbivores may represent an important nontarget group to be further studied. Moreover, some sap feeders (e.g., Homoptera: Cicadellidae) were more abundant on Bt-expressing plants in some samples in all 3 y

Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems

Editorial: Advances and Challenges of RNAi Based Technologies for Plants—Volume 2

Editorial on the Research Topic: Advances and Challenges of RNAi Based Technologies for Plants—Volume

A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces

In this paper, the adoption of Machine Learning (ML) classifiers is addressed to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces (BCIs). The proposed BCI is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In this setup, Augmented Reality Smart Glasses are used to generate and display the flickering stimuli for the SSVEP elicitation. An experimental campaign was conducted on 20 adult volunteers. Successively, a Leave-One-Subject-Out Cross Validation was performed to validate the proposed algorithm. The obtained experimental results demonstrate that suitable ML-based processing strategies outperform the state-of-the-art techniques in terms of classification accuracy. Furthermore, it was also shown that the adoption of an inter-subjective model successfully led to a decrease in the 3-σ uncertainty: this can facilitate future developments of ready-to-use systems

Dynamical charge density fluctuations pervading the phase diagram of a Cu-based high-Tc superconductor

Charge density waves are a common occurrence in all families of high critical temperature superconducting cuprates. Although consistently observed in the underdoped region of the phase diagram and at relatively low temperatures, it is still unclear to what extent they influence the unusual properties of these systems. Using resonant x-ray scattering we carefully determined the temperature dependence of charge density modulations in (Y,Nd)Ba$_2$Cu$_3$O$_{7-{\delta}}$ for three doping levels. We discovered short-range dynamical charge density fluctuations besides the previously known quasi-critical charge density waves. They persist up to well above the pseudogap temperature T*, are characterized by energies of few meV and pervade a large area of the phase diagram, so that they can play a key role in shaping the peculiar normal-state properties of cuprates.Comment: 34 pages, 4 figures, 11 supplementary figure