An in-line coaxial-to-waveguide transition for q-band single-feed-per-beam antenna systems

An in-line transition between a coaxial cable and rectangular waveguide operating in Q-band (33–50 GHz) is presented. The aim of the work is to minimize the modifications in the waveguide to the strictly necessary to overcome the manufacturing issues due to the high frequencies involved. In addition, the transition is compact and it does not increase the space occupation on the transverse section, this suggests its application in horn antennas clusters arrangement. The operating principle consists of both a modal conversion and an impedance matching between the devices. The modal conversion is realized in an intermediate region, where the coaxial penetrates in the waveguide: the device geometry is designed so that the electric field in the transition is a trade-off between the TEM mode of the coaxial and the TE10 of the guide. A shaped waveguide backshort and a reactive air gap in the coaxial cable co-participate to achieve the matching. An optimized Chebyshev stepped transformer completes the transition to fulfil the impedance mismatch with the full waveguide. The design issues and technological aspects are considered. The influences of the feeding pin misalignment, the presence of groove is included in the analysis and these practical aspects are discussed and numerically validated via the scattering parameters analysis of the proposed design. The return loss is higher than 25 dB over the whole Q-band

Study Abroad for Preservice Teachers: A Critical Literature Review with Considerations for Research and Practice

This article applies a postcolonial analytical framework to critically review empirical literature on study abroad for preservice teachers (PSTs). Our systematic search of scholarly databases identified 47 empirical studies of study abroad programs for PSTs in the 2000-2019 time period. Our analysis of these 47 studies is driven by the objectives to (a) understand geographic patterns in study abroad of PSTs, (b) examine the topics, conceptual frames, and implementation of study abroad of PSTs, (c) explore how study abroad for PSTs is currently being conceptualized and studied, and (d) critically analyze how these geographic patterns and study abroad programmatic and research trends are situated within broader North-South relations (Major & Santoro, 2016). Using geovisualizations we illustrate patterns in the countries of origin of PSTs and the countries in which they study abroad. We find that the majority of PSTs are from the United States and are traveling to countries in the North. When examining the content and programming of study abroad, we find many programs focus on cultivating professional skills for PSTs such as language fluency for foreign language teachers and intercultural competence. After establishing these patterns, we pay particular attention to the 23 studies in our sample that examine PSTs traveling to regions in the Global South. We conclude by offering considerations for future research and highlighting practices for program design that encourage PSTs to reflect upon global power differentials and complexities

Hybrid Models for Learning to Branch

A recent Graph Neural Network (GNN) approach for learning to branch has been shown to successfully reduce the running time of branch-and-bound algorithms for Mixed Integer Linear Programming (MILP). While the GNN relies on a GPU for inference, MILP solvers are purely CPU-based. This severely limits its application as many practitioners may not have access to high-end GPUs. In this work, we ask two key questions. First, in a more realistic setting where only a CPU is available, is the GNN model still competitive? Second, can we devise an alternate computationally inexpensive model that retains the predictive power of the GNN architecture? We answer the first question in the negative, and address the second question by proposing a new hybrid architecture for efficient branching on CPU machines. The proposed architecture combines the expressive power of GNNs with computationally inexpensive multi-linear perceptrons (MLP) for branching. We evaluate our methods on four classes of MILP problems, and show that they lead to up to 26% reduction in solver running time compared to state-of-the-art methods without a GPU, while extrapolating to harder problems than it was trained on.Comment: Preprint. Under revie

WSN hardware for automotive applications: Preliminary results for the case of public transportation

The ubiquitous nature and great potential of Wireless Sensors Network has not yet been fully exploited in automotive applications. This work deals with the choice of the cost-effective hardware required to face the challenges and issues proposed by the new trend in the development of intelligent transportation systems. With this aim, a preliminary WSN architecture is proposed. Several commercially available open-source platforms are compared and the Raspberry Pi stood out as a suitable and viable solution. The sensing layer is designed with two goals. Firstly, accelerometric, temperature, and relative humidity sensors were integrated on a dedicated PCB to test if mechanical or environmental stresses during bus rides could be harmful to the device or to its performances. The physical quantities are monitored automatically to alert the driver, thus improving the quality of service. Then, the rationale and functioning of the management and service layer is presented. The proposed cost-effective WSN node was employed and tested to transmit messages and videos, while investigating if any quantitative relationship exists between these operations and the environmental and operative conditions experienced by the hardware

Non-Destructive Characterization of Magnetic Polymeric Scaffolds using Terahertz Time-of-Flight Imaging

Magnetic Scaffolds MagS are 3D composite materials, in which magnetic nanoparticles (MNPs) are used to load a polymeric matrix. Due to their wide use in various medical applications, there is an increasing demand of advanced techniques for non-destructive quality assessment procedures aimed at verifying the absence of defects and, more generally, dedicated to the characterization of MagS. In this framework, the use of TeraHertz (THz) waves for the non-destructive characterization of multifunctional scaffolds represents an open challenge for the scientific community. This paper deals with an approach for the characterization of MagS by means of a THz time-domain system used in reflection mode. THz analyses are performed on poly($\epsilon$ - capprolactone) (PCL) scaffolds magnetized with iron oxide (Fe $_{3}$ O$_{4}$) MNPs through a drop-casting deposition and tuned to obtain different distributions of MNP in the biomaterial. The proposed data processing approach allows a quantitative characterization MagS, in terms of their (estimated) thickness and refractive index. Moreover, the proposed procedure allows to identify the areas of the scaffold wherein MNP are mainly concentrated and thus, it gives us information about MNP spatial distribution

Connectome-Based Neurosurgery in Primary Intra-Axial Neoplasms: Beyond the Traditional Modular Conception of Brain Architecture for the Preservation of Major Neurological Domains and Higher-Order Cognitive Functions.

Despite the therapeutical advancements in the surgical treatment of primary intra-axial neoplasms, which determined both a significative improvement in OS and QoL and a reduction in the incidence of surgery-induced major neurological deficits, nowadays patients continue to manifest subtle post-operative neurocognitive impairments, preventing them from a full reintegration back into social life and into the workforce. The birth of connectomics paved the way for a profound reappraisal of the traditional conception of brain architecture, in favour of a model based on large-scale structural and functional interactions of a complex mosaic of cortical areas organized in a fluid network interconnected by subcortical bundles. Thanks to these advancements, neurosurgery is facing a new era of connectome-based resections, in which the core principle is still represented by the achievement of an ideal onco-functional balance, but with a closer eye on whole-brain circuitry, which constitutes the foundations of both major neurological functions, to be intended as motricity; language and visuospatial function; and higher-order cognitive functions such as cognition, conation, emotion and adaptive behaviour. Indeed, the achievement of an ideal balance between the radicality of tumoral resection and the preservation, as far as possible, of the integrity of local and global brain networks stands as a mandatory goal to be fulfilled to allow patients to resume their previous life and to make neurosurgery tailored and gentler to their individual needs

Constructionist Attempts at Supporting the Learning of Computer Programming: A Survey

Although programming is often seen as a key element of constructionist approaches, the research on learning to program through a constructionist strategy is somewhat limited, mostly focusing on how to bring the abstract and formal nature of programming languages into \u201cconcrete\u201d, possibly tangible objects, graspable even by children with limited abstraction power. We survey the literature in programming education and analyse some programming languages designed to help novices from a constructionist perspective

A wireless sensors network for monitoring the Carasau bread manufacturing process

This work copes with the design and implementation of a wireless sensors network architecture to automatically and continuously monitor, for the first time, the manufacturing process of Sardinian Carasau bread. The case of a traditional bakery company facing the challenge of the Food-Industry 4.0 competitiveness is investigated. The process was analyzed to identify the most relevant variables to be monitored during the product manufacturing. Then, a heterogeneous, multi-tier wireless sensors network was designed and realized to allow the real-time control and the data collection during the critical steps of dough production, sheeting, cutting and leavening. Commercial on-the-shelf and cost-effective integrated electronics were employed, making the proposed approach of interest for many practical cases. Finally, a user-friendly interface was provided to enhance the understanding, control and to favor the process monitoring. With the wireless senors network (WSN) we designed, it is possible to monitor environmental parameters (temperature, relative humidity, gas concentrations); cinematic quantities of the belts; and, through a dedicated image processing system, the morphological characteristics of the bread before the baking. The functioning of the WSN was demonstrated and a statistical analysis was performed on the variables monitored during different seasons

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread

In this paper, we address the problem of automatic image segmentation methods applied to the partial automation of the production process of a traditional Sardinian flatbread called pane Carasau for assuring quality control. The study focuses on one of the most critical activities for obtaining an efficient degree of automation: the estimation of the size and shape of the bread sheets during the production phase, to study the shape variations undergone by the sheet depending on some environmental and production variables. The knowledge can thus be used to create a system capable of predicting the quality of the shape of the dough produced and empower the production process. We implemented an image acquisition system and created an efficient machine learning algorithm, based on support vector machines, for the segmentation and estimation of image measurements for Carasau bread. Experiments demonstrated that the method can successfully achieve accurate segmentation of bread sheets images, ensuring that the dimensions extracted are representative of the sheets coming from the production process. The algorithm proved to be fast and accurate in estimating the size of the bread sheets in various scenarios that occurred over a year of acquisitions. The maximum error committed by the algorithm is equal to the 2.2% of the pixel size in the worst scenario and to 1.2% elsewhere

A Periodic Transmission Line Model for Body Channel Communication

Body channel communication (BCC) is a technique for data transmission exploiting the human body as communication channel. Even though it was pioneered about 25 years ago, the identification of a good electrical model behind its functioning is still an open research question. The proposed distributed model can then serve as a supporting tool for the design, allowing to enhance the performances of any BCC system. A novel finite periodic transmission line model was developed to describe the human body as transmission medium. According to this model, for the first time, the parasitic capacitance between the transmitter and the receiver is assumed to depend on their distance. The parameters related to the body and electrodes are acquired experimentally by fitting the bio-impedentiometric measurements, in the range of frequencies from 1 kHz to 1 MHz, obtaining a mean absolute error lower than 4° and 30 $Omega$ for the phase angle and impedance modulus, respectively. The proposed mathematical framework has been successfully validated by describing a ground-referred and low-complexity system called Live Wire, suitable as supporting tool for visually impaired people, and finding good agreement between the measured and the calculated data, marking a ±3% error for communication distances ranging from 20 to 150 cm. In this work we introduced a new circuital approach, for capacitive-coupling systems, based on finite periodic transmission line, capable to describe and model BCC systems allowing to optimize the performances of similar systems