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.

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.

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%.

Previous research has established that during all phases of a crisis, people resort to different means of communication in order to get more information (McIntyre et al., 2012, Nelson et al., 2009, Lachlan et al., 2009), in order to reduce uncertainty ( Lachlan et al., 2010), and to gain a sense of control over the situation (Lachlan et al., 2016). At the beginning of the 21st century, mass communication is taking on new forms. The exponential growth and affirmation of the Internet as a very important channel for communication has minimized the influence of traditional media. Digitization processes, interactivity, multimedia, connection and networking of a large number of people and expediency in the dissemination of information enabled the wide use of social networks in times of crisis. In the first part of the paper, previous research on the use of social networks in crisis communication was synthesized, through the presentation of best practices for effective communication. The second part of the paper provides a detailed analysis of the use of social networks on the example of the war in Ukraine, answering two important questions: 1. how are social networks used to spread competing national narratives and disinformation in times of crisis? and 2. what is the role of social media owners and government policies in limiting disinformation?

Bats are a natural host for a number of viruses, many of which are zoonotic and thus present a threat to human health. RNA viruses of the family Filoviridae, many of which cause disease in humans, have been associated with specific bat hosts. Lloviu virus is a Filovirus which has been connected to mass mortality events in Miniopterus schreibersii colonies in Spain and Hungary, and some studies have indicated its immense zoonotic potential. A die-off has been recorded among Miniopterus schreibersii in eastern Bosnia and Herzegovina for the first time, prompting the investigation to determine the causative agent. Bat carcasses were collected and subjected to pathological examination, after which the lung samples with notable histopathological changes, lung samples with no changes and guano were analyzed using metagenomic sequencing and RT-PCR. A partial Lloviu virus genome was sequenced from lung samples with histopathological changes and found to be closely related to Hungarian and Italian virus sequences. Further accumulation of mutations on the GP gene, coding the glycoprotein responsible for cell tropism and host preference, enhances the need for further characterization and monitoring of this virus to prevent spillover events and protect human health.

Radar is an effective tool for continuous monitoring and quantification of aerial bird movement and used to study migration and local flight behaviour. However, systems with automated tracking algorithms do not provide the level of processing sufficient to guarantee reliable data. Therefore, post‐processing such radar data is required but often non‐trivial, especially in challenging environments such as open sea. We present a post‐processing framework that implements knowledge of the radar system and bird biology to filter the data and retrieve reliable, high‐quality tracking data. The framework is split into three modules, each with a specific aim: (I) sub‐setting based on prior knowledge of the radar system and bird flight, (II) improving bird track quality and (III) detecting and removing spatio‐temporal sections of data that have a clear bias for false observations. The effectiveness of the framework is demonstrated with a case study comparing track densities inside and outside an offshore wind farm, and by applying the workflow to a dataset of visually validated radar tracks. Application of Module I resulted in a dataset of 520.894 bird tracks (19.5% of total data) within a 10.4 km2 area. Additionally, 18.734 tracks were corrected for geometric errors in Module II, and Module III identified 236 of 719 observation hours and an area of 1.55 km2 as unreliable for spatio‐temporal analysis. No difference in track densities was found between the area inside and outside the wind farm when using the post‐processed data, whereas using the unprocessed bird tracks, lower track densities were observed outside the wind farm. Of the visually validated radar tracks, the framework removed 85% of false positive bird tracks, while retaining 80% of true positive bird tracks. The framework provides a logical workflow to increase the reliability and quality of a bird radar dataset while being adaptable to the radar system and its surroundings. This is a first step towards standardising the post‐processing methodology for automated bird radar systems, which can facilitate comparative analyses of bird movement in space and time and improve the quality of ecological impact assessments.

: Phenolic aldehydes and their derivatives found in nature are well-known for their potential biological activity. In this study, four 1-substituted 1,2,3,4-tetrahydroisoquinolines (THIQs) derived from phenolic aldehydes were synthesized by phosphate buffer mediated Pictet-Spengler reaction. All derivatives were chemically and structurally characterized by elemental CHN analysis and spectroscopic methods (IR, HR-ESI-MS, 1 H-and 13 C-NMR). 1-Substituted THIQs derived from 3,4-dihydroxybenzaldehyde and 4-hydroxy-3-methoxybenzaldehyde were described for the first time. In order to cover the diversity of the mechanistic approach, but also to establish the relationship between structure and activity, antioxidant activity was examined by five different in vitro methods, namely: neutralization and reduction of stable free radicals 2,2-diphenyl-1-picrylhydrazyl and radical cation derived from [(2,2´-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)], ferric reducing antioxidant power, oxygen radical absorbance capacity, and ability to chelate Fe(II) ions. In vitro inhibition of acetylcholinesterase (AChE) was examined by the Ellman's colorimetric method, while computer-simulated docking was used to reveal the preferred binding site and major interaction between AChE and THIQs. Antibacterial testing was examined using the agar well method and results were presented in the form of zones of inhibition (mm).

Triterpenes are very important secondary metabolites with wide structural diversity and significant role in pharmacy and medicine.In the present research, a comparative study of pharamacological activities of the triterpene fractions obtained from several plant species belonging to Lamiaceae family, was carried out. In-vitro anti-proliferative activity was performed using a standardproliferation assay based on tetrazolium salts. In vitro anti-inflammatory activity of triterpene fractions was determined by an assay of inhibition of albumin denaturation. In general, the triterpene fractions obtained from plant species belonging to Lamiaceae family showed a strong anti-proliferative activity and anti-inflammatory activity.The triterpene fraction of Rosmarini folium showed the strongest anti-proliferative activity (GI50range from 4 to 37 μg/ml) and the strongest anti-inflammatory activity in the range from 57.27% to 80.69%. This comparative study provides scientific evidence to support the traditional use of Lamiacae plant species for medical purposes as anti-inflammatory and anti-proliferative medicines.

The appearance of asymmetric loading in the low voltage power distribution network has a negative effect on the voltage profile and power quality. In order to successfully analyze the conditions of the low voltage power supply, this paper presents simulated and analyzed voltage disturbances along the distribution network radial lines for the occurrence of different three-phase power system loading. In the simulation, the influence of asymmetric loading, section length and character of loads on the measured values is presented. The effects of distributed or concentrated loads at individual points of the power lines in terms of the voltage conditions improving were specifically considered.