Introduction Much has been written and spoken about telemedicine since about two decades including an article in this journal at the start of the pandemic. It took a global catastrophe to enforce its usage across the world in various medical specialties. Telemedicine however remains unstructured, unregulated and lacks uniformity. Discussion This article highlights the practical learnings and opinions of the authors who provided over two thousand video consults and asynchronous telemedicine services through the entire pandemic. It includes lessons learnt from emerging economies where pediatric rheumatologists are scarce. Pediatric rheumatology, which relies heavily on history, musculoskeletal and skin examination is aptly suited to exploit telemedicine in its synchronous and asynchronous forms. Pediatric tele rheumatology could temporarily address the shortage and uneven distribution of specialists in vast parts of the globe, besides serving as a method of triage and shared care with the primary physician. Reduction of direct and indirect costs and family/primary physician education are additional benefits. There also exist challenges for all stakeholders and it is important to address the latter. Conclusion The learnings of the pandemic suggest a vital role for telemedicine in the practice of pediatric rheumatology. This is a fertile area for research and consensus building by international and national pediatric societies and issue position statements like some adult bodies already have. The authors speculate a hybrid system of care in the not-so-distant future.

Objectives This study aimed to determine the status of training of adult congenital heart disease (ACHD) cardiologists in Europe. Methods A questionnaire was sent to ACHD cardiologists from 34 European countries. Results Representatives from 31 of 34 countries (91%) responded. ACHD cardiology was recognised by the respective ministry of Health in two countries (7%) as a subspecialty. Two countries (7%) have formally recognised ACHD training programmes, 15 (48%) have informal (neither accredited nor certified) training and 14 (45%) have very limited or no programme. Twenty-five countries (81%) described training ACHD doctors ‘on the job’. The median number of ACHD centres per country was 4 (range 0–28), median number of ACHD surgical centres was 3 (0–26) and the median number of ACHD training centres was 2 (range 0–28). An established exit examination in ACHD was conducted in only one country (3%) and formal certification provided by two countries (7%). ACHD cardiologist number versus gross domestic product Pearson correlation coefficient=0.789 (p<0.001). Conclusion Formal or accredited training in ACHD is rare among European countries. Many countries have very limited or no training and resort to ‘train people on the job’. Few countries provide either an exit examination or certification. Efforts to harmonise training and establish standards in exit examination and certification may improve training and consequently promote the alignment of high-quality patient care.

Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication. Radio frequency fingerprint (RFF)-based SEI is to distinguish one emitter from each other by immutable RF characteristics from electronic components. Due to the powerful ability of deep learning (DL) to extract hidden features and perform classification, it can extract highly separative features from massive signal samples, thus enabling SEI. Considering the condition of limited training samples, we propose a novel few-shot SEI (FS-SEI) method based on interpolative metric learning (InterML) which gets rid of the dependence on auxiliary dataset. Specifically, InterML is designed to mine more implicit samples in the sample space to improve generalization, and constrain the feature distance in the feature space to improve discriminability. The proposed InterML-based FS-SEI method is evaluated on a real-world Wi-Fi dataset. The simulation results show that the proposed method achieves better identification performance, higher feature discriminability and more stable performance than five latest FS-SEI methods. In the 10 shot scenario, the identification accuracy of InterML is 91.48%, compared to the comparison methods, the accuracy is improved by 0.62%–31.29%.

To establish more intelligent cellular networks for future ubiquitous access and heterogeneous devices, we need to obtain channel state information (CSI) in a more agile and economical manner, especially for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) architectures. Unlike conventional CSI feedback or limited feedback methods, we can predict downlink CSI by leveraging channel reciprocity between uplink and downlink. The downlink CSI prediction can be formulated as a data-driven deep learning task, however, there exist isolated data silos and online adaptation problem for the offline trained neural network-based models. In this article, we propose an interacting federated and transfer learning (IFTL) based framework for downlink CSI prediction and online update, where several factors including asynchrony of different clients are considered, and light heterogeneity of diverse cells can be tolerated. Both model-level and link-level simulations are conducted under standardized FDD massive MIMO scenarios. The results outline promising prospect and potential of the utilization of federated learning and transfer learning in physical layer of wireless communications.

In this article, we propose a few-shot indoor position method based on Triplet Matchnet, which transforms coordinate positioning into channel state information (CSI) similarity matching problem. Triplet loss is designed to train and learn hidden correspondence between CSI features and physical space positions, with emphasis on minimizing distance or angle-based triplet loss. Then, according to pre-trained network with best similarity match, a similarity score map of CSI with unknown coordinates is constructed to predict position precisely. Experimental results show that angle-based triplet loss can obtain more accurate CSI fingerprint similarity matching accuracy. Compared with existing methods, experiment results confirm that our proposed method can achieve excellent positioning performance with few-shot datasets.

To provide seamless wireless coverage, the air-to-ground (A2G) heterogeneous wireless network is considered as one of the most promising solutions. In this article, we develop a novel A2G communication-caching-charging (3C) integrated network based on non-orthogonal multiple access (NOMA). As a significant participant of A2G network, unmanned aerial vehicle (UAV), which harvests energy from the base station (BS) with the aid of wireless power transfer (WPT), is utilized as content server to cache files and serve users. To be specific, we first propose a resource allocation strategy to enhance the quality of service (QoS) of ground users. The goal is to minimize the transmission latency of ground users, which is decomposed into sub-problems, such as user pairing, files' power allocation and users' power allocation. Firstly, we propose a NOMA user pairing algorithm based on K-means to deploy UAVs and pair users. Then, the closed-form solution for files' power allocation with the goal of maximizing the duration for energy harvesting is formulated. Finally, we apply the genetic algorithm (GA) to obtain power allocation factors to increase users' rate and the reminder time of content delivery phase is utilized for energy harvesting. Simulation results validate the advantage of the proposed strategy in reducing user delay than benchmark schemes.

Computer graphics, robotics, and physics are one of the many domains where interpolation on the unit sphere S n (often called a unit hypersphere or unit n-sphere) plays a crucial role. In this paper, we introduce a novel approach for achieving smooth and precise interpolation on the unit sphere S n−1 using the n-dimensional generalized Kuramoto model. The proposed algorithm finds the shortest and most direct path between two points on that non-Euclidean manifold. Our simulation results demonstrate that it achieves performance comparable to that of a Spherical Linear Interpolation algorithm. Also, the paper proposes the application of our algorithm in the interpolation of rotations that are presented in the form of four-dimensional data.

Although the technology to automatically score multiple-choice tests has been around for several decades, it is still not as widely available or affordable, especially for paper-based test processing. The main reasons that hinder these processes are the lack of software systems capable of working with contents that are not filled optimally and do not require expensive scanners or other costly equipment. In this study, we present a software system for the automatic reading, storing, and evaluation of scanned assessment sheets. This software system offers a solution that only requires the usual scanned Evaluation Sheets in the form of a not high-resolution image and with the help of specific markers, the system performs reading, storage, and scoring. The user interface is designed to read and display the data from the Evaluation Sheets in detail so that they are very understandable to the user and allow him to quickly spot any errors. The tool has been validated over six years of use and has been continuously improved throughout that period. Thus, this software system achieves a high level of reliability and sensitivity to different levels of the quality of filling out Evaluation Sheets by students.

Averaging data on the unit sphere S d (also called a unit hypersphere) is a common problem in computer vision, robotics and other fields, with applications ranging from motion planning to DNA modelling. In this paper, we introduce a new method for averaging data represented as points on the unit sphere S d−1 using the d-dimensional generalized Kuramoto model. Our method is verified on a range of benchmark data sets and compared with common data averaging algorithms. Also, we showcase the applicability of this method for solving rotation averaging problem.

Im vorliegenden Beitrag werden im Rahmen einer politolin­guistischen Analyse Twitter als soziale Plattform und die Twi­tter-Aktivität des ehemaligen US-Präsidenten Donald Trump zwischen dem 8. Dezember 2020 und dem 8. Januar 2021 un­tersucht. Im genannten Zeitraum ist die Frequenz von Trump versendeter Tweets mit dem näher rückenden Datum der 59. Präsidentschaftswahl in der USA und danach ständig gestiegen, bis zu dem Tag, an dem Twitter Inc. sein Twitter-Konto sperr­te. Im Korpus von 317 versendeten Tweets werden bestimmte sprachliche Merkmale einer Analyse auf mehreren Ebenen un­terzogen, ausgehend von zwei Annahmen: (1) es gibt sprachli­che Faktoren, die Populismus auf Twitter begünstigen und (2) die Vorstellung von der Politikvermittlung auf Twitter hängt von den Plattformnutzern selbst und ihrem Sprachverhalten ab. Am Beispiel von Trumps Tweets soll der Artikel veranschau­lichen, (i) wie politisch motivierte Themen und Interessen auf Twitter strategisch kommuniziert werden und (ii) dass Trumps Politikvermittlung auf Twitter die Züge eines populistischen Stils aufweist. Die Analyseergebnisse zeigen, dass seiner beson­deren Twitter-Aktivität komplexe Kommunikationsstrategien zugrunde liegen, um die Annahmen über die zu verändernden politischen Verhältnisse und den zu erreichenden Zielzustand zu schaffen.

The surveillance system has been utilized over the years on public property in order to provide security and prevent unauthorized entrance. Lately, more private homeowners are choosing to implement security systems. There are many problems with the video surveillance system. These disadvantages are the indistinctiveness of the pictures/video and the need for a lot of storage space to save surveillance information. This paper describes the design and implementation of a low-cost Web Monitoring System based on Raspberry Pi. Also, a web application is designed for the purposes of controlling the camera and for live streaming. The live stream from a web camera can be viewed from any web browser, even mobile, in real-time. Controlling the camera is possible with the web application using four commands (up/down, left/right). Also, the camera moves depending on the detection of an object located in the visible area of the PIR Motion sensor, thereby issuing a warning message about object detection.