Individual Channels Research Articles

Online e-sports event viewers' intention to visit the destination

The main aim of this study is to elucidate the impact of hedonic motivations and habits of e-sports viewers on their intention to visit. The second one is to test the mediating role of individuals' attitudes on this stated impact. The data were collected through face-to-face and online between May 26, 2022, and November 15, 2023. At the end of the data collection process, 438 survey forms were obtained. As a result of the conducted analyses, it was found that hedonic motivation and habit variables have a significant and positive impact on attitude and the intention to visit. Moreover, attitudes mediate the relationship between the intention to visit and both hedonic motivation and habit. In the context of the obtained findings suggested to emphasize aspects that bring fun, pleasure, and enjoyment in e-sports activities. Another recommendation is to provide information to e-sports viewers, especially through individual or social media channels, about e-sports events and the destinations where the events are organized in promotional activities.

62 Prognostic biomarker development using deep learning and spatial proteomics in papillary renal cell carcinoma

Abstract Background Papillary renal cell carcinoma (pRCC) is a prevalent but understudied kidney cancer pathology, constituting 15-20% of cases. While many patients achieve curative therapy using nephrectomy, others go on to develop progressive, metastatic disease with poor overall prognosis. In order to improve the current standards of care for pRCC patients, it is critical to identify new methods to identify high-risk patients likely to require more intense and risk-modulated follow-up. Our goals in this project are two-fold: (1) to identify prognostic patient clusters based on spatial architecture of tumor tissue, and (2) to identify a minimal subset of markers to accurately predict the defined clusters for clinical translation. Methods A tissue microarray of 100 pRCC patients was assayed using multiplex immunofluorescence with a 31-antibody panel with immune- and cancer-related proteins. We have developed novel algorithms to identify spatial neighborhoods and features. These features are then used to identify unsupervised clusters of patients. This new approach to classifying patients allows for a more specific and comprehensive description of each patient tumor’s spatial composition and association with diverse clinical outcomes. Next, we developed attention-based deep learning models capable of predicting these spatially-defined clusters directly from the underlying immunofluorescence images and subsequently profiled these for interpretability by retraining based on individual channels. We assessed each model iteratively on a validation set and a held-out test set for final model selection. We focussed on developing an accurate model with the minimal number of channels to enable development of a cost-efficient biomarker assay for the clinic. Results We performed a systematic analysis of the cellular and spatial phenotypes for each patient in our cohort and identified six (6) major spatial clusters. These clusters are based on the spatial neighborhood composition of the tumors (Fig 1A) and describe the composite effects of cell-cell interactions within a patient tumor. We identified one cluster in particular (cluster #6, mainly associated with spatial interaction of CD163+ macrophages) with a significantly worse prognosis than the other clusters. Using our advanced deep learning models, we have been able to achieve excellent validation accuracy in predicting a variety of clinical phenotypes and spatial features, including patient clusters and histological grade. We wished to then reduce the set of required features and began our efforts in predicting tumor vs. normal based on individual immunofluorescence channels and identified 6 channels with >75% individual predictive accuracy (Fig. 1B). Compositing all 6 markers further improves our accuracy to 90%. This success paves the way for exciting future prospects as we work to extend these efforts to identify a minimal clinically-applicable subset of protein markers. Conclusions The combination of computational spatial proteomics and deep learning models has the potential to identify new biomarkers specific to the spatial organization of patient tumors. We have identified a particular patient cluster with multiple associations with CD163+ macrophages demonstrating poor prognosis; selecting these patients may allow for tailored and more informed patient care. We are also working to enhance our deep learning models to circumvent the need for stepwise spatial analysis and, instead, allow for direct-from-image assessment of patients for particular spatial features indicative of various prognostic indicators. Our identified set of 6 markers serves as a basis for further refinements as we work to identify a minimal set of relevant markers able to predict various clinical outcomes from immunofluorescence imaging, offering a promising new avenue in the field of cancer pathology and biomarker development. DOD CDMRP Funding: yes

Моделирование течения воздуха в упруго-деформируемой пористой среде, аппроксимирующей легкие человека: структура модели, ее основные уравнения и разрешающие соотношения

This paper is devoted to refining the mathematical model of the human respiratory system designed to predict the occurrence of respiratory pathologies caused by the negative effects of atmospheric air pollution. It is difficult to identify individual air channels in the human lungs, consisting of small respiratory tracts, called alveoli, through which oxygen from the air passes into blood. Therefore, the human lungs are modeled as a two-phase elastically deformable saturated porous medium, in which one phase is the deformable skeleton of the medium (lung tissue) described by the model of deformable solid body, and the other phase is gas filling the porous space. We construct the model of air flow in the human lungs, which consists of the sub-model of elastic deformation of the porous lung medium, experiencing large displacement gradients, and the sub-model of air filtration through the deformable porous medium. The model takes into account the relationships between sub-models of the respiratory system, i.e. we consider the coupled problems of deformation and filtration in differential form. Since at present the analytical solution of the stated essentially nonlinear problem is unfeasible, the problem is formulated in a generalized weak form to allow subsequent application of numerical methods using the Galerkin method. For the numerical solution of the nonlinear problem of deformation of the two-phase lung medium, it is suggested to use the finite element method, for the filtration problem - the finite volume method. The resolving relations needed to solve the formulated sub-problems are derived. To solve the nonlinear problem, we developed algorithms and a set of programs. The description of these computational tools, as well as analysis of the results obtained will be presented in our the next paper.

SOFTWARE ARCHITECTURE SYSTEM DESIGN FOR THE MASS SERVICE SYSTEMS MODELING TO BE IMPLEMENTED IN GO PROGRAMMING LANGUAGE

The subject of the article is the methods and approaches to organizing the architecture of software implementation designed for modeling the behavior of mass service systems. The goal of the work is to design a software architecture for implementation in Go language, intended to replicate the behavior of various types of mass service systems, without considering the failure of individual service channels, using parallel computing. The article addresses the following tasks: consider the basis for designing the architecture and conclude its appropriateness; develop requirements for the future software product for more effective resource use and clear definition of successful completion; analyze the approaches to organizing software architecture and make a justified decision on the application of one of them; design a general algorithm scheme taking into account all requirements; identify the components of the modeled system and their interactions; build process diagrams considering the specifics of the Go programming language; define the method and contracts of interaction with the software. The research will utilize the following methods: Go programming language, concurrency, architectural UML diagrams, C4 diagrams, process diagrams. The following results were obtained: the requirements for the software for modeling mass service operations (SMO) were defined; common approaches to organizing architecture were considered and a comparative analysis was conducted; the structure of the future program was developed at the necessary levels of abstraction; for the first time, an architecture of the software product for modeling various mass service systems using parallel computing and the concurrency approach under the implementation in the Go programming language was proposed.

A model reduction framework of flow maldistribution and mitigation strategy for parallel flow field based polymer exchange membrane fuel cell

Mitigating flow maldistribution plays a crucial role in next-generation industry-used parallel-based flow field design for polymer exchange membrane fuel cells (PEMFCs), especially for larger-size cells and higher current density. In this study, a model reduction framework is proposed to alleviate flow maldistribution among parallel gas flow channels and associated computational burden. The gas flow features and electrochemical kinetics of four mitigation strategies including expanded manifold, partially porous media, dot matrix, and partially narrow structures are investigated in detail. The results indicate that the fuel cell model and a fluid flow model without the reactions show similar maldistribution features among parallel channels. The observed flow maldistribution can be effectively reduced by mitigating designs. Expanding the manifold space significantly alleviates the vortex impeding the gas entry to individual channel inlet, and the inlet exhibits better flow uniformity and pressure drop in the direction perpendicular to the channel than in the parallel to. The fully-inserted and manifold-inserted porous media methods present symmetrical quadratic-shaped flow patterns among channels, while that of the partially-inserted porous media inside channels is unsymmetrical. Additionally, the concentration loss with a partially narrowed design decreases by 44.7%, 12.9%, and 24.2% compared with the expanded manifold, partial porous media, and dot matrix designs at 1.8 A cm−2 owing to better flow uniformity and mass transport. These model framework and mitigation strategies are valuable for designing future high-performance industrial PEMFCs.

Microlens arrays for multichannel laser-to-waveguide coupling

An optical multichannel coupling system for coupling laser arrays to waveguide arrays is developed. Based on a microlens array, the system enables coupling of nine individual optical channels, with one aspheric microlens per channel at a lateral channel pitch of 100 µm. The design process criteria for the proposed microlenses, with 97 µm diameter and working distances from laser to lens and lens to waveguide of 150 µm and 275 µm, respectively, are described. The microlens array is fabricated on a 4mm×2mm×0.41mm fused silica chip and contains an orthogonal grid with 32×16 microlenses, of which a row of nine adjacent microlenses is used for coupling. Uniform coupling over all channels can be achieved, as well as specific coupling for each channel individually with less than −13.5dB crosstalk. The coupling system is designed for optical neural stimulators.

Thermal performance of a two-pass microchannel evaporator

Microchannel evaporator is one of the key components in many thermal systems such as heat exchanger of HAVC and refrigeration system. The evaporation heat transfer performance of microchannel evaporator, which usually has many flow paths, is highly affected by the distribution of liquid flow that will seriously deteriorate the flow boiling performance due to the lack of liquid supply, local dryout, etc. In this study, a novel two-pass microchannel evaporator (L × W × H = 103 × 64 × 20 mm) is designed and fabricated. The width, height and length of each individual channel are 0.6, 1 and 65 mm, respectively. To significantly improve liquid redistribution after the first-pass, evenly distributed spacers with different diameter holes were placed in the connecting flow path. Additionally, the channel number ratio between the first and second pass plays an important role. Therefore, three different channel ratios of 1:1, 1:2, and 3:5 were selected in this study. The heat transfer and flow characteristics of the two-pass microchannel evaporator using working fluid HFE-7100 were experimentally studied for flow rate varying from 30 ml/min to 110 ml/min. The inlet subcooling keeps about 30 K. Also, visualization studies were conducted for the explanations. The experimental results indicate that the thermal performance is superior in microchannel evaporator with channel ratio of 1:2. By considering Reynolds number of each channel in the first pass, microchannel evaporator with channel ratio of 1:1 outperforms other two evaporators. Furthermore, the wall temperature uniformity of this evaporator is better than others. The achieved heat flux of 20 W/cm2 is much higher than the reported studies. The overall two-phase HTC of this microchannel evaporator is about 4.5 kW/m2K.

Nematocida displodere mechanosensitive ion channel of small conductance 2 assembles into a unique 6-channel super-structure in vitro.

Mechanosensitive ion channels play an essential role in reacting to environmental signals and sustaining cell integrity by facilitating ion flux across membranes. For obligate intracellular pathogens like microsporidia, adapting to changes in the host environment is crucial for survival and propagation. Despite representing a eukaryote of extreme genome reduction, microsporidia have expanded the gene family of mechanosensitive ion channels of small conductance (mscS) through repeated gene duplication and horizontal gene transfer. All microsporidian genomes characterized to date contain mscS genes of both eukaryotic and bacterial origin. Here, we investigated the cryo-electron microscopy structure of the bacterially derived mechanosensitive ion channel of small conductance 2 (MscS2) from Nematocida displodere, an intracellular pathogen of Caenorhabditis elegans. MscS2 is the most compact MscS-like channel known and assembles into a unique superstructure in vitro with six heptameric MscS2 channels. Individual MscS2 channels are oriented in a heterogeneous manner to one another, resembling an asymmetric, flexible six-way cross joint. Finally, we show that microsporidian MscS2 still forms a heptameric membrane channel, however the extreme compaction suggests a potential new function of this MscS-like protein.

New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacity

In NOMA achieving fairness in data transmission for all users is one of the key challenges. It implies a fair allocation of resources among users according to a given criterion. NOMA systems are based on the discreteness of the data source, since the specific nature of data allows to extract information in overloaded systems. Well-known C.E. Shannon capacity equation does not take into account the discrete nature of NOMA group signals, considering them as Gaussian source of information. Therefore, it does not take into account the characteristics of discrete signals used in NOMA systems, which makes it difficult to solve the optimization problem, especially for types of NOMA that use code division. For this task it is necessary to use DCMC capacity. When optimizing total capacity of DCMC, individual properties of users are not taken into account, as a result of which the allocation of resources with such optimization might not be fair. This paper proposes an approach to optimizing NOMA based on the analysis of an-individual mutual information (capacity). The proposed optimization approach allows analyzing the individual characteristics of each user in order to improve NOMA system performance by optimizing NOMA signals parameters.

Experimental and numerical investigations on the bending capacity of cold-formed steel box headers

The study investigates the bending strength of tracks of box headers beyond AISI, which considers the capacity of individual channels alone. Both experimental and FEM are used, and the results are compared to AISI. The findings highlight tracks' significant role in the overall bending capacity. AISI is found to be conservative by 34% to 152%. Failure mode is different from code theoretical expectations for a single channel. Fastener close spacing marginally improves the capacity, while side fasteners offer significant enhancement, but track widening limits this enhancement. A modification to AISI is proposed considering track strength, with outcomes showing good accuracy.

A Lightweight Neural Network for the Real-Time Dehazing of Tidal Flat UAV Images Using a Contrastive Learning Strategy

In the maritime environment, particularly within tidal flats, the frequent occurrence of sea fog significantly impairs the quality of images captured by unmanned aerial vehicles (UAVs). This degradation manifests as a loss of detail, diminished contrast, and altered color profiles, which directly impact the accuracy and effectiveness of the monitoring data and result in delays in the execution and response speed of monitoring tasks. Traditional physics-based dehazing algorithms have limitations in terms of detail recovery and color restoration, while neural network algorithms are limited in their real-time application on devices with constrained resources due to their model size. To address the above challenges, in the following study, an advanced dehazing algorithm specifically designed for images captured by UAVs over tidal flats is introduced. The algorithm integrates dense convolutional blocks to enhance feature propagation while significantly reducing the number of network parameters, thereby improving the timeliness of the dehazing process. Additionally, an attention mechanism is introduced to assign variable weights to individual channels and pixels, enhancing the network’s ability to perform detail processing. Furthermore, inspired by contrastive learning, the algorithm employs a hybrid loss function that combines mean squared error loss with contrastive regularization. This function plays a crucial role in enhancing the contrast and color saturation of the dehazed images. Our experimental results indicate that, compared to existing methods, the proposed algorithm has a model parameter size of only 0.005 M and a latency of 0.523 ms. When applied to the real tidal flat image dataset, the algorithm achieved a peak signal-to-noise ratio (PSNR) improvement of 2.75 and a mean squared error (MSE) reduction of 9.72. During qualitative analysis, the algorithm generated high-quality dehazing results, characterized by a natural enhancement in color saturation and contrast. These findings confirm that the algorithm performs exceptionally well in real-time fog removal from UAV-captured tidal flat images, enabling the effective and timely monitoring of these environments.

Using Support Vector Machine to Detect Data Hiding in Color Images

Background: A steganography investigation model that uses improved dim scale List of capabilities, for identifying information inside uncompressed RGB pictures. Materials and Methods: in this paper, a bunch of 2000 RGB pictures was made utilizing normal tones, in BMP design with a size of 512 x 512 pixels. Clean pictures are Inserted secret picture information, utilizing two Payload plans, 2 pieces for every channel (BPC) and 4 pieces for each channel. The arrangement of chosen highlights comprises of 24 elements for every variety channel, and 72 highlights for each variety channel The picture, incorporates dim-level co-event grid highlights, entropy Elements, and factual proportions of variety. The list of capabilities components is determined as Individual channels, joined into picture vector highlights. The cycle depends on steganography examination On AI, utilizing Paired vector machine (SVM) support Execute the exercise manual in MATLAB. Results: The outcomes showed that the new method had overcome the benchmark with more than 80% identification accuracy Conclusion : a new Steganalysis model was introduced by using SVM. The model can recognize the presence of stowed-away information inside RGB variety pictures, removing the highlights from datasets of perfect and messy pictures and ordering utilizing the support Vector Machine calculation. the results were compared with the DNA algorithm.

A deep learning approach for high‐resolution mapping of Scottish peatland degradation

AbstractPeat makes up approximately a quarter of Scotland's soil by area. Healthy, undisturbed, peatland habitats are critical to providing resilient biodiversity and habitat support, water management, and carbon sequestration. A high and stable water table is a prerequisite to maintain carbon sink function; any drainage turns this major terrestrial carbon store into a source that feeds back further to global climate change. Drainage and erosion features are crucial indicators of peatland condition and are key for estimating national greenhouse gas emissions. Previous work on mapping peat depth and condition in Scotland has provided maps with reasonable accuracy at 100‐m resolution, allowing land managers and policymakers to both plan and manage these soils and to work towards identifying priority peat sites for restoration. However, the spatial variability of the surface condition is much finer than this scale, limiting the ability to inventory greenhouse gas emissions or develop site‐specific restoration and management plans. This work involves an updated set of mapping using high‐resolution (25 cm) aerial imagery, which provides the ability to identify and segment individual drainage channels and erosion features. Combining this imagery with a classical deep learning‐based segmentation model enables high spatial resolution, national scale mapping to be carried out allowing for a deeper understanding of Scotland's peatland resource and which will enable various future analyses using these data.

A modular optically pumped magnetometer system

To address the demands in healthcare and industrial settings for spatially resolved magnetic imaging, we present a modular optically pumped magnetometer (OPM) system comprising a multi-sensor array of highly sensitive quantum magnetometers. This system is designed and built to facilitate fast prototyping and testing of new measurement schemes by enabling quick reconfiguration of the self-contained laser and sensor modules as well as allowing for the construction of various array layouts with a shared light source. The modularity of this system facilitates the development of methods for managing high-density arrays for magnetic imaging. The magnetometer sensitivity and bandwidth are first characterised in both individual channel and differential gradiometer configurations before testing in a real-world magnetoencephalography environment by measuring alpha rhythms from the brain of a human participant. We demonstrate the OPM system in a first-order axial gradiometer configuration with a magnetic field gradient sensitivity of 10fT/cm/Hz at a baseline of 4.5 cm. Single-channel operation achieved a sensitivity of 65fT/Hz . Bandwidths exceeding 200Hz were achieved for two independent modules. The system’s increased temporal resolution allows for the measurement of spinal cord signals, which we demonstrate by using phantom signal trials and comparing with an existing commercial sensor.

The clock in growing hyphae and their synchronization in Neurospora crassa

Utilizing a microfluidic chip with serpentine channels, we inoculated the chip with an agar plug with Neurospora crassa mycelium and successfully captured individual hyphae in channels. For the first time, we report the presence of an autonomous clock in hyphae. Fluorescence of a mCherry reporter gene driven by a clock-controlled gene-2 promoter (ccg-2p) was measured simultaneously along hyphae every half an hour for at least 6 days. We entrained single hyphae to light over a wide range of day lengths, including 6,12, 24, and 36 h days. Hyphae tracked in individual serpentine channels were highly synchronized (K = 0.60-0.78). Furthermore, hyphae also displayed temperature compensation properties, where the oscillation period was stable over a physiological range of temperatures from 24 °C to 30 °C (Q10 = 1.00-1.10). A Clock Tube Model developed could mimic hyphal growth observed in the serpentine chip and provides a mechanism for the stable banding patterns seen in race tubes at the macroscopic scale and synchronization through molecules riding the growth wave in the device.

A demosaicking method based on an inter-channel correlation model for DoFP polarimeter

Division of Focal Plane (DoFP) polarimeters have demonstrated significant potential due to their ability to perform real-time imaging coupled with a simple structure. However, their sparse polarization sampling introduces instantaneous Field of View (IFoV) errors and challenges for image reconstruction quality. Existing demosaicking methods primarily consider individual polarization channels, limiting reconstruction precision. In this work, we propose a novel demosaicking approach leveraging correlations between polarization channels. Moreover, a direction criterion based on joint-channel-gradient is incorporated to reconstruct more accurate boundary features. Our method, as a balancing strategy, can be easily combined with other algorithms for further optimization. Evaluations using a publicly available database and actual DoFP polarimetric data demonstrate significant quality improvements over other advanced methods. Reconstructed images exhibit a noticeable enhancement of polarization-dependent details.

Putting stored hydrogen to work without consuming it: A flexible system for energy conversion and water desalination

We report a flow battery that utilises hydrogen as a charge carrier in both the cathode and anode and makes use of the energy released in acid-base neutralisation to desalinate seawater and generate electricity, based on cheap and relatively safe electrolytes stored externally. We demonstrate desalination of simulated seawater from 0.6 to 0.009 ± 0.005 M NaCl and successful removal of Na+, K+, Mg2+, and Ca2+ from real seawater to potable levels. The battery can also be used for osmotic energy conversion through reverse electrodialysis (RED) if the acid and base are substituted by neutral diluted aqueous solution (e.g., freshwater), reaching power densities similar to state-of-the-art systems while using a much more environmentally friendly redox charge carrier, namely hydrogen, than those common in RED systems. The most important characteristics of the reported system are (i) its flexibility, which allows easy tuning to favour either energy generation or degree of desalination by changing the flow rates and volumes of each individual channel and/or the discharge current and (ii) the possibility of putting hydrogen to work without consuming it while stored for later shipment, thereby producing a profit that can contribute to decreasing the cost of green hydrogen.

Synergy effects of pH and thermal localization on membrane-based salinity gradient energy harvesting

Salinity gradient power generation receives increasing attention due to its availability in nature. However, membrane surface charge is not fixed value in isolation but strongly dependent of various conditions. Here, a coupled ion transport model is proposed to study the synergy effects of pH and thermal localization on membrane-based salinity gradient energy conversion. The research illustrates that the surface charge is regulated by solution environment, membrane properties and thermal conditions. Specially, the surface charge sign can be even reversed at certain case that induces distinct ion transports behaviors. Consequently, Al2O3 membrane achieves the better energy conversion performance at low pH with negative temperature difference. While SiO2 membrane obtains the best output performance at high pH with negative temperature difference. Such is ascribed to pH-regulated surface charge and thermal up-diffusion enhancement. The maximum output power of 10 pW with efficiency of 16 % is achieved for individual membrane channel. As a proof of concept, it shows significant deviations between the single and coupling model on power generation. The findings of this study can help for better understanding the coupling mechanisms of solution, membrane and thermal conditions during the energy conversions, thereby providing a reasonable design route for membrane-based energy devices.

Effect of number of channels on the fluctuations and distribution of humid-nitrogen condensing flow in a parallel flow field

Pressure drop and flow rate fluctuations of condensation-induced gas–liquid two-phase flow through a single microchannel have been widely recognized, which are quite different from that through parallel-connected multiple microchannels, as in the flow field of proton exchange membrane fuel cells and heat exchangers. This work investigates the effects of the number of channels on the fluctuations of the humid-nitrogen condensing flow and the uniformity of the flow rate distribution across the channels. A parallel flow field with eight channels is designed; the arrangement of these channels is axisymmetric, ensuring identical flow routes and thermal boundary conditions among individual channels. The number of open channels in the flow field could be adjusted from one to eight. The observed pressure drop fluctuated between 0.20 kPa and 0.68 kPa in the single-channel flow field, compared to 0.22 kPa to 0.43 kPa in the two-channel flow field and 0.28 kPa to 0.48 kPa in the eight-channel flow field. This indicates that the amplitude of pressure drop fluctuations decreases as the number of channels increases. In contrast, both the flow rate fluctuation and maldistribution are most significant in the eight-channel arrangement. It is also found that the effect of channel number on condensing flow dynamics is noticeable when both the velocity and humidity of the inlet gas are low.

State Information Policy of Ukraine in the Conditions of a Full-Scale Military Invasion of Russian Federation: Social Mobilization Potential and Efficiency

The article is devoted to determining the social mobilization potential of the state information policy of Ukraine in the conditions of a full-scale military invasion of the Russian Federation. The dependence of the state information policy of Ukraine on the course of events at the front, the successful implementation of mobilization measures and the nature of the organization of countermeasures against enemy informational and psychological operations has been revealed. It is found that during years of full-scale Russian aggression against Ukraine, signs of internal political struggle and populism within the implementation of the state information policy became increasingly evident, as the authorities aimed to portray desired narratives as factual. It is justified that the decrease in trust in the messages broadcast by the 24-hour marathon „Unified News #UAtogether” is attributed to its perception as a pro-government information source and doubts about the veracity of some of its content. It is proven that centralized communication models in the conditions of accelerated development of the information society gave way to social networks, which contributed to the growth of the influence of influencers. Distrust towards official information sources led to the proliferation of alternative content sources on social media, video hosting platforms, and streaming channels. It was determined that the government's plan to spread important information through individual Telegram channels was gradually losing its effectiveness. The reason for this can be called the absence of comprehensive situation analysis and overt positioning of some Telegram channels as pro-government. Furthermore, Ukraine's state information policy emerged as a significant tool in highlighting Russian war crimes and crimes against humanity. The author defined that successful enemy operations aimed at undermining trust in Ukraine's government institutions and fostering unrealistic expectations were noted, which proved unattainable in practice. It was determined that the state information policy should contribute to creating an economic basis for media development. To enhance media project outreach, they should be visually and audibly represented and disseminated digitally, taking into account the increasing influence of artificial intelligence.