Location Map Research Articles

Colloquium: Advances in automation of quantum dot devices control.

Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, a comprehensive overview of the recent progress in the automation of QD device control is presented, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.

Odd-even discrimination-based reversible data hiding technique in encrypted images

Here, we describe a new method for reversible data hiding in encrypted images (RDHEI) that aims to improve the embedding capacity (EC) and visual quality of the stego image. Most of the existing techniques for RDHEI need location maps to discriminate embedded and non-embedded pixels, which increases computational overhead and affects EC. The proposed method reserves room after encryption and converts prediction errors (PE) to odd and even numbers to discriminate between embedded and non-embedded pixels. This avoids the computational overhead caused by the use of a location map. The proposed scheme also employs double-layer encryption using random permutation and a stream cipher to secure the cover images. The method is separable, which means that the secret data and cover image can be independently extracted and recovered, depending on the availability of the data hiding and decryption keys. The scheme uses block-wise reference pixels for the calculation of PE, that enhances the EC. Experimental results indicate that the proposed scheme is more effective than existing state-of-the-art works.

Proactive control model for safety prediction in tailing dam management: Applying graph depth learning optimization

Accurate and rapid prediction of monitoring parameters is an important means of proactively preventing safety accidents in tailings dams. Traditional monitoring model uses a single-point time-domain prediction method through feature selection, ignoring the correlation between monitoring point data. Multiple raw monitoring time series were used as direct input to the GCN-AGRU prediction model to estimate response changes in tailings dam operations. Firstly, the spatial location map of sensors in the tailings dam monitoring system was constructed to describe the sensor data in a multi-point arrangement. Then a graph neural network was used to capture the complex spatial dependencies of the sensor location map to obtain the overall safety level of the tailings dam. Secondly, the spatial features extracted from the graph convolutional neural network at different moments were fused with time series and input into a gated recurrent unit (GRU). The purpose was to use the fused features to capture the spatio-temporal correlation between time series of different monitoring locations. Finally, the temporal attention mechanism was combined to predict the dam monitoring data. Based on real data, the algorithm was compared with other algorithms to verify its accuracy and generalizability, which indicates that the model has great significance for improving the risk prevention and control of tailings dams.

ANALISIS DESKRIPTIF SISTEM PAYMENT PAJAK KENDARAAN BERMOTOR PADA APLIKASI SAMSAT MOBILE JAWA BARAT (SAMBARA)

the purpose of this study is to identify public service innovations, namely the SAMBARA (West Java Mobile Samsat) application in paying motor vehicle taxes. This research seeks to explore the benefits of this Sambara innovation for the people of West Java. This study used a qualitative research method with a literature study approach. Some of the findings from this research are that the Sambara innovation can make payments online, can find out a map of the Samsat location, can check vehicle data, can verify vehicle tax using the NIK and vehicle frame number. Other findings are supporting factors, namely cooperation and support from various parties as well as the position of the private sector which contributes to operational provision. However, in making tax payments the Sambara application still has problems and there is still low understanding and lack of information to the public about the Sambara application.

Space distribution of the seismic source energy between 1-1-2000 and 3-7-2022 at Campi Flegrei caldera

The spatial distribution of seismic sources is commonly visualized through location maps, where a symbol (commonly a dot or a cross) shows where the earthquake source is located inside the area under study. In the present note I propose a different visualization approach, based on the isoline representation of the Energy Space Density (ESD), which is conceptually similar to approaches already used in literature to represent the pattern of space distribution of strong earthquakes or to spatially identify earthquake swarms. Visualizing such a pattern jointly with the hypocentral distribution map, favors the conceptual association of the Seismic Energy Release to the known geological structures in the area. A test is carried out using the Campi Flegrei seismic catalogue between 2000 and, 2022, period in which the seismic activity in the area resumed after a long calm of several years. The catalogue reports >4000 seismic events with magnitude ranging from 0.5 to 3.5, all located inside a very small volume (5 × 5 × 5 km) and associated with the phenomenon called ”bradiseism”, a slow but intense ground uplift (>80 cm in the 22-year interval spanned by the catalogue). Results indicate that the ESD pattern enhances the visualization of the rock fracture density associated with the ground deformation, in agreement with other studies in the same area, producing a picture of the most fractured rock volumes, possibly associated with fluid storage under the active areas of Solfatara and Pisciarelli.

Alignment, segmentation and neighborhood analysis in cyclic immunohistochemistry data using CASSATT.

Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. Additionally, cycIHC benefits from the speed and simplicity of brightfield microscopy, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the log odds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact-or do not interact-at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT is a fully open source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

Response of the soft scale insect Parthenolecanium corni and its natural enemies on honeylocust trees to urban conditions

Urban habitats can alter tree physiology in ways that make trees more susceptible to insect pests. We examined how urban conditions (e.g., impervious surfaces and warming) affected the soft scale insect, European fruit lecanium, Parthenolecanium corni Bouché (Hemiptera: Coccidae) and the health of thornless honeylocust trees (Gleditsia tricanthos L. var. inermis) in Indianapolis, Indiana, USA. Hot and cool study sites were selected based on surface air temperatures obtained from thermal LANDSAT-8 images overlaid against a map of honeylocust tree locations. Based on temperature probe data, study trees located in hot sites had mean average daily temperatures up to 1.56 °C and 1.79 °C warmer than cool sites in 2019 and 2020, respectively. We hypothesized that abundance and fecundity of scale insects would be higher on trees in hot sites and those surrounded by more impervious surface cover within a 20 m radius. Although our data supported these hypotheses, impervious surface cover predicted more of the variation in abundance and fecundity of scale insects in hot sites compared to cool sites. Rates of parasitism were not affected by impervious surface cover or whether the site was from a hot or cool location. However, we found strong density dependence in parasitism of scale insects in both hot and cool sites when scale densities were at or below the levels present in cool sites. This suggests that lower densities of scale insects were associated with a positive numerical response of parasitoids, a necessary prerequisite for population regulation by top-down effects. In contrast, this numerical response was lacking at the higher densities of scales found on trees located in hot sites where, presumably bottom-up factors like water-stress increased the fecundity of scale insects. These findings add to a growing body of evidence linking rising global temperatures with increased pest abundance in ways that cannot be mitigated by local populations of natural enemies.

MT-nCov-Net: A Multitask Deep-Learning Framework for Efficient Diagnosis of COVID-19 Using Tomography Scans.

The localization and segmentation of the novel coronavirus disease of 2019 (COVID-19) lesions from computerized tomography (CT) scans are of great significance for developing an efficient computer-aided diagnosis system. Deep learning (DL) has emerged as one of the best choices for developing such a system. However, several challenges limit the efficiency of DL approaches, including data heterogeneity, considerable variety in the shape and size of the lesions, lesion imbalance, and scarce annotation. In this article, a novel multitask regression network for segmenting COVID-19 lesions is proposed to address these challenges. We name the framework MT-nCov-Net. We formulate lesion segmentation as a multitask shape regression problem that enables partaking the poor-, intermediate-, and high-quality features between various tasks. A multiscale feature learning (MFL) module is presented to capture the multiscale semantic information, which helps to efficiently learn small and large lesion features while reducing the semantic gap between different scale representations. In addition, a fine-grained lesion localization (FLL) module is introduced to detect infection lesions using an adaptive dual-attention mechanism. The generated location map and the fused multiscale representations are subsequently passed to the lesion regression (LR) module to segment the infection lesions. MT-nCov-Net enables learning complete lesion properties to accurately segment the COVID-19 lesion by regressing its shape. MT-nCov-Net is experimentally evaluated on two public multisource datasets, and the overall performance validates its superiority over the current cutting-edge approaches and demonstrates its effectiveness in tackling the problems facing the diagnosis of COVID-19.

Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery.

Flowering is a crucial developing stage for rapeseed (Brassica napus L.) plants. Flowers develop on the main and branch inflorescences of rapeseed plants and then grow into siliques. The seed yield of rapeseed heavily depends on the total flower numbers per area throughout the whole flowering period. The number of rapeseed inflorescences can reflect the richness of rapeseed flowers and provide useful information for yield prediction. To count rapeseed inflorescences automatically, we transferred the counting problem to a detection task. Then, we developed a low-cost approach for counting rapeseed inflorescences using YOLOv5 with the Convolutional Block Attention Module (CBAM) based on unmanned aerial vehicle (UAV) Red-Green-Blue (RGB) imagery. Moreover, we constructed a Rapeseed Inflorescence Benchmark (RIB) to verify the effectiveness of our model. The RIB dataset captured by DJI Phantom 4 Pro V2.0, including 165 plot images and 60,000 manual labels, is to be released. Experimental results showed that indicators R2 for counting and the mean Average Precision (mAP) for location were over 0.96 and 92%, respectively. Compared with Faster R-CNN, YOLOv4, CenterNet, and TasselNetV2+, the proposed method achieved state-of-the-art counting performance on RIB and had advantages in location accuracy. The counting results revealed a quantitative dynamic change in the number of rapeseed inflorescences in the time dimension. Furthermore, a significant positive correlation between the actual crop yield and the automatically obtained rapeseed inflorescence total number on a field plot level was identified. Thus, a set of UAV- assisted methods for better determination of the flower richness was developed, which can greatly support the breeding of high-yield rapeseed varieties.

Analysis of Location and Spatial Distribution of Elderly Women Victims of Gender Violence

Little is known about abuse and violence against elderly women, as well as prevention and intervention strategies. We present the results of an investigation that has as its objective the elaboration of the Location Map of elderly women victims of gender violence, analyzing their territorial distribution in the specific case of the Autonomous Community of Galicia, Spain, and its relationship with environmental, social, and territorial variables. The results of our research on the location and distribution of the rates of elderly women victims of gender violence show its direct relationship with low demographic density, aging, and dependency, which is associated with disabled people. The mapping resulting can facilitate the territorial planning of social and health services aimed at elderly women in rural areas. The interquartile classification makes it possible to delimit areas of intervention at a spatial level, differentiating those municipalities with the highest and lowest prevalence.

Intelligent Diagnosis of Multiple Peripheral Retinal Lesions in Ultra-widefield Fundus Images Based on Deep Learning.

Compared with traditional fundus examination techniques, ultra-widefield fundus (UWF) images provide 200° panoramic images of the retina, which allows better detection of peripheral retinal lesions. The advent of UWF provides effective solutions only for detection but still lacks efficient diagnostic capabilities. This study proposed a retinal lesion detection model to automatically locate and identify six relatively typical and high-incidence peripheral retinal lesions from UWF images which will enable early screening and rapid diagnosis. A total of 24,602 augmented ultra-widefield fundus images with labels corresponding to 6 peripheral retinal lesions and normal manifestation labelled by 5 ophthalmologists were included in this study. An object detection model named You Only Look Once X (YOLOX) was modified and trained to locate and classify the six peripheral retinal lesions including rhegmatogenous retinal detachment (RRD), retinal breaks (RB), white without pressure (WWOP), cystic retinal tuft (CRT), lattice degeneration (LD), and paving-stone degeneration (PSD). We applied coordinate attention block and generalized intersection over union (GIOU) loss to YOLOX and evaluated it for accuracy, sensitivity, specificity, precision, F1 score, and average precision (AP). This model was able to show the exact location and saliency map of the retinal lesions detected by the model thus contributing to efficient screening and diagnosis. The model reached an average accuracy of 96.64%, sensitivity of 87.97%, specificity of 98.04%, precision of 87.01%, F1 score of 87.39%, and mAP of 86.03% on test dataset 1 including 248 UWF images and reached an average accuracy of 95.04%, sensitivity of 83.90%, specificity of 96.70%, precision of 78.73%, F1 score of 81.96%, and mAP of 80.59% on external test dataset 2 including 586 UWF images, showing this system performs well in distinguishing the six peripheral retinal lesions. Focusing on peripheral retinal lesions, this work proposed a deep learning model, which automatically recognized multiple peripheral retinal lesions from UWF images and localized exact positions of lesions. Therefore, it has certain potential for early screening and intelligent diagnosis of peripheral retinal lesions.

Application of Religious Tourism Places in Province Banten, West Java

Technological developments and the need for information lead to increasingly complex information that must be processed and presented quickly and accurately. Religious tourism is a new trend that is of interest to the public. This is evidenced by the many activities of religious managers, ranging from tour fleet providers to general public figures. The aim of the research is to produce an interactive knowledge application that can be used by the community as a method of knowledge about religious tourism in Banten Province, West Java by providing information from several religious tourist attractions accompanied by a location map. The research method used by the author is the Research and Development (R&D) method, which is a research method used to produce a particular product and test the effectiveness of the method so that it can be used to develop or validate products used in education and learning. The selection of application programs is made using the Android programming language with an easy-to-understand user interface. Based on the research results, the authors conclude that this application can be used as an effective and communicative method of knowledge for the community

Implementasi Modul ILO-SCORE UMKM Warung Sembako ‎Razka Dalam Aspek Tata Letak dan Kebersihan (5S) dan Aspek ‎Pemasaran di Daerah Perbatasan, Kalimantan Barat ‎

The community service initiative, in conjunction with ILO-Score, aims to support MSME players in the Razka basic food shop. These business players are still simple in terms of governance, layout, and public advertising. The condition that exists is that the things being traded are still placed randomly and are unknown to the surrounding population. The work program implemented is the 5S idea and location mapping on Google maps for the early phases of digital marketing. The participation of students in this support creates new behaviors that business players have not before exercised. Business actors may use their understanding of net production to improve their operations.

TFRegNCI: Interpretable Noncovalent Interaction Correction Multimodal Based on Transformer Encoder Fusion.

The interpretability is an important issue for end-to-end learning models. Motivated by computer vision algorithms, an interpretable noncovalent interaction (NCI) correction multimodal (TFRegNCI) is proposed for NCI prediction. TFRegNCI is based on RegNet feature extraction and a transformer encoder fusion strategy. RegNet is a network design paradigm that mainly focuses on local features. Meanwhile, the Vision Transformer is also leveraged for feature extraction, because it can capture global features better than RegNet while lowering the computational cost. Using a transformer encoder as the fusion strategy rather than multilayer perceptron can enhance model performance, due to its emphasis on important features with less parameters. Therefore, the proposed TFRegNCI achieved high accurate prediction (mean absolute error of ∼0.1 kcal/mol) comparing with the coupled cluster single double (triple) (CCSD(T)) benchmark. To further improve the model efficiency, TFRegNCI applies two-dimensional (2D) inputs transformed from three-dimensional (3D) electron density cubes, which saves time (30%), while the model accuracy remains. To improve model interpretability, a visualization module, Gradient-weighted Regression Activation Mapping (Grad-RAM) has been embedded. Grad-RAM is promoted from the classification algorithm, Gradient-weighted Class Activation Mapping, to perform feature visualization for the regression task. With Grad-RAM, the visual location map for features in deep learning models can be displayed. The feature map visualizations suggest that the 2D model has the similar performance as the 3D model, because of equally effective feature extractions from electron density. Moreover, the valid feature region on the location map by the 3D model is consistent with the NCIPLOT NCI isosurface. It is confirmed that the model does extract significant features related to the NCI interaction. The interpretable analyses are carried out through molecular orbital contribution on effective features. Thereby, the proposed model is likely to be a promising tool to reveal some essential information on NCIs, with regard to the level of electronic theory.

Single-Molecule Imaging in Commercial Stationary Phase Particles Using Highly Inclined and Laminated Optical Sheet Microscopy.

We resolve the three-dimensional, nanoscale locations of single-molecule analytes within commercial stationary phase materials using highly inclined and laminated optical sheet (HILO) microscopy. Single-molecule fluorescence microscopy of chromatography can reveal the molecular heterogeneities that lead to peak broadening, but past work has focused on surfaces designed to mimic stationary phases, which have different physical and chemical properties than the three-dimensional materials used in real columns and membranes. To extend single-molecule measurements to commercial stationary phases, we immobilize individual stationary phase particles and modify our microscope for imaging at further depths with HILO, a method which was originally developed to resolve single molecules in cells of comparable size to column packing materials (∼5-10 μm). We describe and characterize how to change the angle of incidence to achieve HILO so that other researchers can easily incorporate this method onto their existing epi- or total internal reflection fluorescence microscopes. We show improvements up to a 32% in signal-to-background ratio and 118% in the number of single molecules detected within stationary phase particles when using HILO compared to epifluorescence. By controlling the objective position relative to the sample, we produce three-dimensional maps of molecule locations throughout entire stationary phase particles at nanoscale lateral and axial resolutions. The number of localized molecules remains constant axially throughout isolated stationary phase particles and between different particles, indicating that heterogeneity in a separation would not be caused by such affinity differences at microscales but instead kinetic differences at nanoscales on identifiable and distinct adsorption sites.

Segmentação do rio Apodi-Mossoró e alterações pedogenéticas em solos de ambientes aluviais

ABSTRACT The Apodi-Mossoró River Basin is important for the west region of the state of Rio Grande do Norte (RN), Brazil. The rapid increase in population and consequent need for space has resulted in anthropogenic actions, such as the segmentation of the river course within the municipality of Mossoró, RN. The objective of this study was to assess the effects of this segmentation on soils in alluvial environments. Soil collection points were defined in natural and anthropized areas in the Mossoró urban segment; soil profiles were described and collected; and samples of the soil 0-10 and 10-20 cm were collected. The samples were subjected to physical and chemical analyses to determine granulometry, density, aggregates, pH in water, available phosphorus (P), exchangeable bases, potential acidity, total organic carbon, nitrogen, and micronutrients (Cu and Zn). Maps of location and land use and cover were developed. The soil found in the natural area was classified as Neossolo Fluvico Psamitico eutrico, presenting typical physical and chemical characteristics of this soil class, with variable texture and P contents along the soil profile. The soil in the anthropized area was classified as Planossolo Haplico Carbonatico vertissolico, with Ap Horizon presenting a sandy loam texture, since it is a horizon formed due to anthropogenic activity in the environment. All horizons and profiles presented high pH. The evaluated areas formed different groups regarding soil physical and chemical attributes, with overlapping points that can be explained by anthropogenic actions, even in the environment considered natural.

VWR-SLAM: Tightly Coupled SLAM System Based on Visible Light Positioning Landmark, Wheel Odometer, and RGB-D Camera

Visible light positioning (VLP) is a promising technology since it can provide high-accuracy indoor localization based on the existing lighting infrastructure. Most VLP systems require a prior light-emitting diode (LED) location map, termed a VLP-landmark map in this article, for which manual surveys are costly in practical deployment at scale. What is more, the existing approaches also require dense LED deployments. In this work, we proposed a multisensor fusion framework, termed VWR-simultaneous localization and mapping (SLAM), which tightly fused the VLP, wheel odometer, and red green dlue-depth map (RGB-D) camera to achieve SLAM. Our VWR-SLAM can provide accurate and robust robot localization and navigation in LED shortage/outage situations, meanwhile, constructing the 3-D sparse environment map and the 3-D VLP-landmark map without tedious manual measurements. The experimental results show that our proposed scheme can provide an average robot positioning accuracy of 1.81 cm and an LED mapping accuracy of 3.01 cm.

On the Question of the Size and Location of the Chinese Diaspora in the World (1949 — present)

The article is devoted to the issues of the size of the Chinese diaspora (overseas Chinese or huaqiao-huaren — Chinese emigrants with PRC citizenship and ethnic Chinese with foreign citizenship), its location in the world and the degree of its self-organization. These issues are quite complex. There is still no generally accepted methodology for studying them. Changes in the size and geographical location of the Chinese diaspora are shown in a historical perspective, with an emphasis on 1949 (the creation of the PRC), 1978 (the beginning of the policy of reform and opening-up in the PRC) and 2000-2021 (suppositive present stage). The author analyzes international, Chinese (PRC and Taiwan), American and Russian estimates of the size of the Chinese diaspora and determines the range of the number of huaqiao-huaren at the present stage at 50-80 million people. She identifies 189 countries and regions of residence of overseas Chinese (earlier, a figure of 150-160 countries and regions appeared in national and foreign literature), and creates two maps of the geographical location of the Chinese diaspora by countries and regions of the world. One of them shows the actual number of overseas Chinese, the other shows the share of huaqiao-huaren in the population of countries and regions of the world. The paper also touches upon the topic of overseas Chinese communities. It studies the statistical data of the PRC and Taiwan regarding the size and types of activities of these communities. It also gives a brief overview of the economic situation of the Chinese diaspora in host countries. The author concludes that the Chinese diaspora has a high degree of self-organization and developed economic ties with China.

Анализ структурной организации сообщества крупных хищных птиц Ремдовского заказника

Анализ структурной организации сообщества крупных хищных птиц Ремдовского заказника

Фентезі в Україні – жива культурна традиція

Fantasy as a metagenre exists in many (art and non-art) forms, due to which scholars refer to it as a symbol of broad modern cultural development (O. Manakhov). Fantasy enhances the magicization of the real world and creates the secondary, non-existing one; it pushes numerous amateurs toward artistic self-implementation, unites the experts in various fields in their shared attempt to explore and create fantasy worlds, to arrange their life space. Fantasy as an in-action cultural tradition has not yet been interpretated as separate research; however, certain issues are regularly raised. Modern culture of fantasy penetrates literature, films, movie series, comic strips, manga, videogames in a way that makes fantasy one of the most important artistic styles of today, and is embodied in three dimensions – cultural, economic, and transmedia (K. Maj). The scholars refer to comicons, videogames, artificial intelligence that will create animated 3D maps of fantasy locations (O. Manakhov) as well as to various types of fan-art (Ye. Kanchura, Ya. Yukhymuk), fanzines activity (S. Legeza), K. Kaczor). All these works have one common trait: each of them emphasizes the aspect connected with the main aim of the research and places a smaller focus on the fact that this is an in-action process – not only intertextual but also multiple media, often extraart, as it covers different areas of the life of a modern individual who wishes to take an active part in this process rather than to be a mere consumer of the cultural product. The research seeks to prove that modern fantasy, particularly in Ukraine, is an in-action literary and, in a broader sense, cultural tradition, a multidirectional process affected by all the components of fantasy and making it impossible to clearly distinguish their positions; all taken together, they build the magic fantasy world, which combines an artistic illusion with the present. Fantasy in Ukraine is indeed an in-action cultural tradition, which is actively spreading beyond literature and targets both writers, and artists involved in numerous types of art (comics, live action etc.), as well as critics and fan community. Moreover, fantasy is a part of applied arts, street art and leisure activities (videogames, board games, festivals). The development of any fantasy-related activities testifies to the transgressive crossing of any borders – between experts and amateurs (fantasy created and explored by fans, fan-art, field-specific journals and fanzines, competitions and workshops run by editorial houses and fanzines, special projects and amateur online platforms, festivals and comicons), between art and routine life (wall art, paintings, fantasy décor), between high and pop culture (from art-house, indie-projects to merch). This trend is intrinsic not only to Ukrainian fantasy. Ukrainian culture is actively entering the global cultural process. The future focus of the research will be placed on Ukrainian fantasy, which is actively growing and becoming a part of the global literary and non-literary practice namely as an in-action cultural tradition, which is built under the influence of the fan community and embodied in various media and cultural forms thus testifying to the development of fantasy discourse of modern society. Key words: fantasy, tradition, cultural product, fan-art, globalization.