Information Decoding Research Articles

Multifunctional hydrogels simultaneously featuring ultrahigh stretchability, remarkable tearing resistance, and reusable fluorescence for information encoding–decoding and smart anticounterfeiting

Fluorescent hydrogels with fluorescent dyes are widely used for anti-counterfeiting encryption. However, developing highly stretchable and remarkable tear-resistant hydrogels with reusable fluorescence for anti-counterfeiting purposes remains challenging. In this study, we fabricated and characterized double network (DN) hydrogels comprising polyvinyl alcohol-borax (PVA-borax) as the first network and poly(methacrylamide-co-polyethylene glycol-co-pyrenylmethyl acrylate) (P(MAM-co-PEGMA-co-PyMA)) as the second network with varying MAM to PEGMA ratios. SEM images showed the hydrogels had 3D porous structures. FT-IR and thermal properties analysis (DSC and TGA) indicated the presence of hydrogen bonds between PVA-borax and P(MAM-co-PEGMA-co-PyMA) without any chemical bonds. The red shift in characteristic absorption bands at 841 and 710 cm−1 further confirmed the presence of π-π* interactions between pyrene moieties. XRD analysis revealed no significant changes in crystalline phase of the hydrogels, which facilitated the formation of a 3D network due to abundant hydrogen bond presence. When the MAM to PEGMA ratio was adjusted to 9:2, the resulting hydrogels exhibited remarkable properties such as ultra-high stretchability (strain over 3300 %), excellent notch-insensitivity, and exceptional tear resistance (tearing energy over 3.25 × 105 J/m2). Importantly, the hydrogel's fluorescence intensity could be reversibly changed by alternatively adding Fe3+ and EDTA-2Na. It could be reused more than 10 times while maintaining a fluorescence intensity of around 80 %, demonstrating its reliability as a versatile hydrogel probe for Fe3+ detection with “ON-OFF-ON” fluorescence behavior. Furthermore, the hydrogels demonstrated exceptional 2D encryption and decryption capabilities, highlighting their potential applications in information storage and data security. To improve the level of anti-counterfeiting, multiple encryption approaches such as QR codes and ASCII binary codes were rationally designed. Overall, this study successfully developed a universal DN hydrogel platform with ultra-high stretchability and remarkable tear resistance, capable of rapid encoding and decoding of complex information for smart anti-counterfeiting.

An efficient multiscale integrated attention method combined with hyperspectral system to identify the quality of rice with different storage periods and humidity

As an essential food, the quality of rice is critical to human health. The quality of rice varies significantly under different storage conditions. Using spectral analysis technology, a non-destructive testing method can quickly identify rice with quality, but an effective data analysis method directly affects its detection performance. Aiming at the characteristics of strong correlation and high dimensionality of rice spectral information, this paper proposes an effective feature extraction method to mine the spectral information for quality identification of rice with different storage periods and humidity. Firstly, the hyperspectral system is employed to obtain the spectral information of rice during six storage periods with different storage humidity. Secondly, a multiscale integrated attention block (MSIA) is proposed, which consists of multiscale feature extraction and spatial-channel attention mechanisms that can realize deep decoding of the spectral information. Finally, MSIA is combined with classical convolutional neural networks (CNN) to detect the quality of rice at different storage periods and humidity. The method’s effectiveness is demonstrated in comparison with other advanced spectral information recognition algorithms. Specifically, the integration of ShuffleNetV1 and MSIA achieves the best overall classification performance with an accuracy of 99.69%, precision of 99.72%, recall of 99.69%, and F1-score of 99.69%. These results indicate that MSIA is an efficient spectral feature extraction technique. The combination of ShuffleNetV1 + MSIA and the hyperspectral system presents a novel approach for rice quality assessment with different storage periods and humidity.

Impact of mobility on the downlink performance of cell-free massive MIMO systems

In this paper, we analyze the achievable downlink spectral efficiency of cell-free massive multiple input multiple output (CF-mMIMO) systems, accounting for the effects of channel aging (caused by user mobility) and pilot contamination. We consider two cases, one where user equipments (UEs) rely on downlink pilots beamformed by the access points (APs) to estimate downlink channel, and another where UEs utilize statistical channel state information (CSI) for data decoding. For comparison, we also consider cellular mMIMO and derive its achievable spectral efficiency with channel aging and pilot contamination in the above two cases. Our results show that, in CF-mMIMO, downlink training is preferable over statistical CSI when the length of the data sequence is chosen optimally to maximize the spectral efficiency. In cellular mMIMO, however, either one of the two schemes may be better depending on whether user fairness or sum spectral efficiency is prioritized. Furthermore, the CF-mMIMO system generally outperforms cellular mMIMO even after accounting for the effects of channel aging and pilot contamination. Through numerical results, we illustrate the effect of various system parameters such as the maximum user velocity, uplink/downlink pilot lengths, data duration, network densification, and provide interesting insights into the key differences between cell-free and cellular mMIMO systems.

Reducing cost in DNA-based data storage by sequence analysis-aided soft information decoding of variable-length reads.

DNA-based data storage is one of the most attractive research areas for future archival storage. However, it faces the problems of high writing and reading costs for practical use. There have been many efforts to resolve this problem, but existing schemes are not fully suitable for DNA-based data storage, and more cost reduction is needed. We propose whole encoding and decoding procedures for DNA storage. The encoding procedure consists of a carefully designed single low-density parity-check code as an inter-oligo code, which corrects errors and dropouts efficiently. We apply new clustering and alignment methods that operate on variable-length reads to aid the decoding performance. We use edit distance and quality scores during the sequence analysis-aided decoding procedure, which can discard abnormal reads and utilize high-quality soft information. We store 548.83 KB of an image file in DNA oligos and achieve a writing cost reduction of 7.46% and a significant reading cost reduction of 26.57% and 19.41% compared with the two previous works. Data and codes for all the algorithms proposed in this study are available at: https://github.com/sjpark0905/DNA-LDPC-codes.

Application of Incoherent Multi-Frequency Signals for Information Transmission in a Nonstationary Hydroacoustic Environment

A class of digital incoherent methods of information transmission based on multi-frequency signals for mobile underwater complexes is presented, which does not require regular and accurate estimation of channel parameters and is a priori resistant to various types of interference in nonstationary hydroacoustic communication channels, and is oriented to solving communication and navigation problems for underwater robotic complexes. The limitation of spectral efficiency of such multi-frequency systems of information transmission by the value of 0.5 bits/s/Hz in various frequency ranges, nonstationary hydrological conditions, with achievement of the maximum range at acceptable levels of error probability during information decoding is shown. The operability of the proposed class of multi-frequency multiplexing methods was confirmed by numerical and in situ sea experiments on the shelf at distances from 2.5 to 7 km with the mutual drifting of ships and with sea waves of up to 3 points.

SWIPT Aided Cooperative Communications With Energy Harvesting-Based Selective-Decode-and-Forward Protocol: Benefiting From Channel Aging Effect

Simultaneous wireless information and power transfer (SWIPT) in radio-frequency (RF) bands enables flexible deployment of battery-powered relays for extending communication coverage. Relays receive downlink RF signals emitted by a source for information decoding and energy harvesting, while the harvested energy is consumed for both information decoding and information forwarding to a destination. An energy harvesting based selective-decode-and-forward (EH-SDF) protocol is proposed, where only the relays having information correctly decoded are activated for information forwarding, while others harvest and store energy for the future use. By considering the channel aging effect, we propose a joint relay selection, power allocation, transmit beamforming and signal splitting design in order to maximise the end-to-end (e2e) throughput of this EH-SDF aided cooperative communication system. Two scenarios with/without direct link between the source and the destination are studied, respectively. The original formulated non-convex optimisation problems with coupled variables are decoupled into three subproblems which are solved by an iterative optimisation algorithm. Numerical results demonstrate that our design with the EH-SDF protocol achieves a higher e2e throughput than the traditional decode-and-forward (DF) counterpart. Moreover, the impact of the channel aging effect on the e2e throughput is also evaluated.

Delay-Optimal Multi-User Wireless-Powered Relay Networks With Protection Against the Risk of Distributed Algorithm Manipulation

We consider simultaneous-wireless-information-and-power-transfer (SWIPT) in a relay network, where multiple users with stochastic traffic flows are serviced by a common wireless-powered relay. A two-phase forwarding protocol is deployed along with power splitting. In the first phase, the relay splits its received signal from sources into two parts: one for information decoding and the other for energy harvesting. In the second phase, the relay allocates the total harvested energy from the first phase to forward each user’s data. To optimize the system’s long-run delay performance, we formulate the relay’s power control problem as a Markov decision process (MDP) with per-stage (energy) budget constraints. We realize that the Bellman’s equations associated with the formulated MDP have a weakly-coupled structure, and assuming that users act as price-takers, the global optimum can be computed distributively via Lagrangian decomposition. We then abandon this assumption to face the more realistic case where the users are price-anticipating and may manipulate the rules of the distributed algorithm to favor themselves. To protect against the risk of unfaithful implementation, we design a dynamic economic mechanism that builds on the notions of transfer payments and periodic ex-post Nash equilibrium to disarm the users’ self-interest and ensure adherence to the algorithm.

Sex-dependent noradrenergic modulation of premotor cortex during decision making.

Rodent premotor cortex (M2) integrates information from sensory and cognitive networks for action planning during goal-directed decision making. M2 function is regulated by cortical inputs and ascending neuromodulators, including norepinephrine (NE) released from the locus coeruleus (LC). LC-NE has been shown to modulate the signal to noise ratio of neural representations in target cortical regions, increasing the salience of relevant stimuli. Using rats performing a two-alternative forced choice task after administration of a β noradrenergic antagonist (propranolol), we show that β noradrenergic signaling is necessary for effective action plan signals in anterior M2. Loss of β noradrenergic signaling results in failure to suppress irrelevant action plans in anterior M2 disrupting decoding of cue related information, delaying decision times, and increasing trial omissions, particularly in females. Furthermore, we identify a potential mechanism for the sex bias in behavioral and neural changes after propranolol administration via differential expression of β2 noradrenergic receptor RNA across sexes in anterior M2, particularly on local inhibitory neurons. Overall, we show a critical role for β noradrenergic signaling in anterior M2 during decision making by suppressing irrelevant information to enable efficient action planning and decision making.

Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-Free Massive Random Access

In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. Recently, grant-free random access has emerged as a promising mechanism for this challenging scenario, but its potential has not been fully unleashed. In particular, the available auxiliary information has not been fully exploited, including the common sparsity pattern in the received pilot and data signal, as well as the channel decoding information. This paper develops advanced receivers in a holistic manner to improve the massive access performance by jointly designing activity detection, channel estimation, and data decoding. To tackle the algorithmic and computational challenges, a turbo structure is adopted at the joint receiver. For performance enhancement, all the received symbols are utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information (SI)-aided receiver is also proposed, where the channel decoder provides side information to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, supporting twice as many active users compared with a separate design that disregards the common sparsity. In addition, the SI-aided receiver notably outperforms the conventional methods with a relatively low complexity.

Enhancement of Information Carrying and Decoding for Visual Cryptography with Error Correction

Recently, three visual cryptography schemes with t -error-correcting capability (VCSs- t EC) were introduced for preventing the shadows carrying additional information from being corrupted by noise interference. However, the concerns on VCS- t EC, such as the average amount of carrying information, decoding of information from shadows, and the demonstration way of a secret, should be considered and improved. In this paper, two schemes, namely the ( k , n ) probabilistic VCS- t EC (PVCS- t EC) and the (2, n ) deterministic VCS- t EC (DVCS- t EC), are proposed. The concept of probabilistic VCS is combined with the Bose-Chaudhuri-Hocquenghem code (BCH code) for designing the ( k , n ) PVCS- t EC. Further, some constant-weight BCH codewords are adopted to build the (2, n )-DVCS- t EC. Comprehensive results and experiments are demonstrated to clarify the enhancement of information-carrying and decoding by the two proposed methods.

All‐In‐One Optoelectronic Neuristor Based on Full‐vdW Two‐Terminal Ferroelectric p–n Heterojunction

AbstractPhotoelectric synaptic devices with optical sensing capability are of great importance in simulating human vision systems. Especially realizing all‐in‐one vision neuristor on silicon based new materials and principles is being pursued. 2D van der Waals (vdW) materials have the unique advantage of arbitrary stacking on demand. Herein, a full‐vdW two‐terminal 2D ferroelectric α‐In2Se3/SnSe p–n heterojunction is proposed to construct an optoelectronic neuristor to simulate visual synaptic functions. Implementation of these simulations is attributed to the co‐modulation of the electrical polarization reconfigurable built‐in electric field caused by p–n junction and photo‐inducing ferroelectric polarization switching. These functions include ultra‐high paired‐pulse facilitation index (457%), short synaptic plasticity, long synaptic plasticity, and retina‐like optical adaptations. The high PPF is crucial for high‐precision decoding and processing of visual information. Meanwhile, the classical Pavlovian conditioned reflexes associated with associative learning are also emulated showing the ability of the device to handle complex electrical and optical inputs. This study demonstrates that two‐terminal ferroelectric p–n heterojunctions have great potential in high‐precision multifunctional optoelectronic visual synaptic devices.

Multi-agent deep reinforcement learning based resource management in SWIPT enabled cellular networks with H2H/M2M co-existence

Machine-to-Machine (M2M) communication is crucial in developing Internet of Things (IoT). As it is well known that cellular networks have been considered as the primary infrastructure for M2M communications, there are several key issues to be addressed in order to deploy M2M communications over cellular networks. Notably, the rapid growth of M2M traffic dramatically increases energy consumption, as well as degrades the performance of existing Human-to-Human (H2H) traffic. Sustainable operation technology and resource management are efficacious ways for solving these issues. In this paper, we investigate a resource management problem in cellular networks with H2H/M2M coexistence. First, considering the energy-constrained nature of machine type communication devices (MTCDs), we propose a novel network model enabled by simultaneous wireless information and power transfer (SWIPT), which empowers MTCDs with the ability to simultaneously perform energy harvesting (EH) and information decoding. Given the diverse characteristics of IoT devices, we subdivide MTCDs into critical and tolerable types, further formulating the resource management problem as an energy efficiency (EE) maximization problem under divers Quality-of-Service (QoS) constraints. Then, we develop a multi-agent deep reinforcement learning (DRL) based scheme to solve this problem. It provides optimal spectrum, transmit power and power splitting (PS) ratio allocation policies, along with efficient model training under designed behavior-tracking based state space and common reward function. Finally, we verify that with a reasonable training mechanism, multiple M2M agents successfully work cooperatively in a distributed way, resulting in network performance that outperforms other intelligence approaches in terms of convergence speed and meeting the EE and QoS requirements.

Extracellular matrix sensing via modulation of orientational order of integrins and F-actin in focal adhesions.

Specificity of cellular responses to distinct cues from the ECM requires precise and sensitive decoding of physical information. However, how known mechanisms of mechanosensing like force-dependent catch bonds and conformational changes in FA proteins can confer that this sensitivity is not known. Using polarization microscopy and computational modeling, we identify dynamic changes in an orientational order of FA proteins as a molecular organizational mechanism that can fine-tune cell sensitivity to the ECM. We find that αV integrins and F-actin show precise changes in the orientational order in an ECM-mediated integrin activation-dependent manner. These changes are sensitive to ECM density and are regulated independent of myosin-II activity though contractility can enhance this sensitivity. A molecular-clutch model demonstrates that the orientational order of integrin-ECM binding coupled to directional catch bonds can capture cellular responses to changes in ECM density. This mechanism also captures decoupling of ECM density sensing from stiffness sensing thus elucidating specificity. Taken together, our results suggest relative geometric organization of FA molecules as an important molecular architectural feature and regulator of mechanotransduction.

Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay.

Digital immunoassays with multiplexed capacity, ultrahigh sensitivity, and broad affordability are urgently required in clinical diagnosis, food safety, and environmental monitoring. In this work, a multidimensional digital immunoassay has been developed through microparticle-based encoding and artificial intelligence-based decoding, enabling multiplexed detection with high sensitivity and convenient operation. The information encoded in the features of microspheres, including their size, number, and color, allows for the simultaneous identification and accurate quantification of multiple targets. Computer vision-based artificial intelligence can analyze the microscopy images for information decoding and output identification results visually. Moreover, the optical microscopy imaging can be well integrated with the microfluidic platform, allowing for encoding-decoding through the computer vision-based artificial intelligence. This microfluidic digital immunoassay can simultaneously analyze multiple inflammatory markers and antibiotics within 30 min with high sensitivity and a broad detection range from pg/mL to μg/mL, which holds great promise as an intelligent bioassay for next-generation multiplexed biosensing.

Multi-wavelength imaging with extended depth of field using coded apertures and radial quartic phase functions

This study demonstrates a new multi-wavelength imaging technique with an extended depth of field. The spatial and spectral information of the objects is encoded into a single bipolar function of sparse dots using coded phase masks (CPMs) and a radial quartic phase function (RQPF). Each wavelength produces an intensity response of sparse dots with a unique set of distances between the dots, enabling the decoding of multispectral information. The RQPF is introduced to extend the depth of focus for different wavelengths. A library of point spread holograms (PSHs) is prerecorded with uniquely designed CPMs by placing the point objects of multiple wavelengths at the system input. For reconstruction, the object hologram is cross-correlated with a PSH corresponding to the wavelength of the object. The results are compared with direct imaging and with the conventional technique of interferenceless coded aperture correlation holography. The method is unique because the number of camera shots required for multi-wavelength imaging is the same as for monochromatic imaging.

On the application of neural networks for service life prediction of steel fibre-reinforced concrete

While physics-based modelling entails the capability to describe the mechanisms and physics of a system across multiple scales, these models often are decoupled from external measurements available for specific systems of interest. On the other hand, data analysis methods allow the decoding of information on particular systems, yet such mathematical analyses struggle with context of the domain physics under study. Although it is common to have both methods positioned as antagonizing, the real challenge is to combine them, capitalizing on their strength and information. Proper integration between physics-based models and data-driven inference will enable understanding, optimising, and predicting complex physical systems around us. This study exemplifies the application of machine learning to predict corrosion in fibre-reinforced concrete. Focus is placed on current limitations concerning the application of machine learning to predict the underlying mechanisms for fibre corrosion and opportunities to integrate physics-based modelling and data-driven interference truly. Results indicate that it is possible to train neural networks with high accuracy through training examples (>99% classification accuracy), while a loss in the ability to generalize patterns and thus predict the degree of fibre corrosion is observed for samples beyond the training set (<60% classification accuracy). The absence of the ability to generalize is mainly caused by the loss of physics, such as material history dependency, physical invariance, and conservation laws, which are not naturally resolved in the current machine learning models.

Single-Gate In-Transistor Readout of Current Superposition and Collapse Utilizing Quantum Tunneling and Ferroelectric Switching.

In nanostructure assemblies, the superposition of current paths forms microscopic electric circuits, and different circuit networks produce varying results, particularly when utilized as transistor channels for computing applications. However, the intricate nature of assembly networks and the winding paths of commensurate currents hinder standard circuit modeling. Inspired by the quantum collapse of superposition states for information decoding in quantum circuits, the implementation of analogous current path collapse to facilitate the detection of microscopic circuits by modifying their network topology is explored. Here, the superposition and collapse of current paths in gate-all-around polysilicon nanosheet arrays are demonstrated to enrich the computational resources within transistors by engineering the channel length and quantity. Switching the ferroelectric polarization of Hf0.5 Zr0.5 O2 gate dielectric, which drives these transistors out-of-equilibrium, decodes the output polymorphism through circuit topological modifications. Furthermore, a protocol for the single-electron readout of ferroelectric polarization is presented with tailoring the channel coherence. The introduction of lateral path superposition results into intriguing metal-to-insulator transitions due to transient behavior of ferroelectric switching. This ability to adjust the current networks within transistors and their interaction with ferroelectric polarization in polycrystalline nanostructures lays the groundwork for generating diverse current characteristics as potential physical databases for optimization-based computing.

A Segmented-Edit Error-Correcting Code With Re-Synchronization Function for DNA-Based Storage Systems

As a powerful tool for storing digital information in chemically synthesized molecules, DNA-based data storage has undergone continuous development and received increasingly more attention. Efficiently recovering information from large-scale DNA strands that suffer from insertions, deletions, and substitution errors (collectively referred to as edit errors), is one of the major bottlenecks in DNA-based storage systems. To cope with this challenge, in this paper, we provide a segmented-edit error-correcting code with the re-synchronization function, termed the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DNA-LM</i> code. Compared with the previous segmented-error-correcting codes, it has a systematic structure and does not require the endpoint of the received segment as pre-requisite information for decoding. In the case that the number of edit errors exceeds the edit error-correcting capability of a segment, it can easily regain synchronization to ensure that the subsequent decoding continues. Both encoding and decoding complexity is linear in the codeword length. The redundancy of each segment is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lceil \log k\rceil +6$</tex-math></inline-formula> quaternary symbols, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the length of the message segment. We further generalize the decoding algorithm to deal with duplicated DNA strands, whereas it still maintains linear time complexity in the codeword length and the number of duplications. Simulations under a stochastic edit errors model show that, at a low raw error rate of the “next-gen” sequencing, our code can enable error-free decoding by concatenating with the (255,223) RS code.

Orbital angular momentum holographic multicasting for switchable and secure wireless optical communication links.

The physical dimension of orbital angular momentum (OAM) states of light has been successfully implemented as information carrier in wireless optical communication (WOC) links. However, the current OAM data coding strategies in WOC are mainly limited to the temporal domain, rarely involving the degree of freedom of spatial domain to transmit an image directly. Here, we apply OAM holographic multiplexing technology for spatial information encoding in WOC links. Further, we demonstrate the new concept of OAM holographic multicasting, wherein a beam-steering grating has been utilized for information decoding. To distribute the OAM multiplexing information appropriately in the receiving terminal, the beam-steering grating with controllable topological charges and amplitude weighting coefficients of each diffraction order in the spatial frequency domain has been designed. An iterative algorithm has been introduced to obtain the intensity uniformity >98% at target diffraction orders. As such, this scheme experimentally allows four separate users to receive independent images, which can be switched by modulating the topological charges of the beam-steering gratings at each diffraction order. In addition, this leads to a beam-steering grating-encrypted WOC links, wherein the information can only be decoded by the grating phase with 7 pre-set spatial frequency components. Our results mark a new parallel decoding paradigm of OAM multiplexing holography, which opens up the door for future high-capacity and high-security all-optical holographic communications.

ПАРЕМІОЛОГІЙНІ ОДИНИЦІ В СУЧАСНОМУ ГАЗЕТНОМУ ДИСКУРСІ УКРАЇНИ ВОЄННОГО ПЕРІОДУ: ПРАГМАТИЧНИЙ ЕФЕКТ

There is no doubt about the relevance of the study of language units of paremia in the Ukrainian-language newspaper discourse, because this topic is little studied in the scientific works of linguists. In our study, we interpret newspaper discourse as a complex mental and sociocultural phenomenon, pragmatic in its expressive representations, complex in content and construction structure due to the need to reflect information about the full-scale invasion of the Russian Federation on the territory of Ukraine. The modern newspaper text of the war period is a vivid representative of the use of paremiological units, which testify to an extremely expressive atmosphere in the information space and explain the national potentials and originality of the native language. A significant feature of the activation of paremia in journalism is that the modern newspaper text becomes more readable, harmonious, ethnically marked, attractive, with bright expressive semantic and stylistic shades of appreciation, relies on the popular phraseological trends of Ukrainians to express reality. In our study, paremias are understood as linguistic units with a pragmatic effect, which form a functionally productive source of expressions for uncontrollable emotions, excitement and upheavals, most fully reflect the tragedy of modern realities of war, express a positive or negative social/authorial assessment of the depicted events. Paremiological units are powerful repeaters of national culture, they contain the genetic code of the people, ethnic group, nation, so journalists use paremiological material as an effective way of rhetorical argumentation, an authoritative source of folk wisdom, to finally convince the audience, emotionally impress them, make them believe and trust the author. We have proven that the array of paremias in newspaper texts ensures the success of the communicative act, because it contributes to the process of mental encoding and decoding of information, the levelling of communication barriers, the popularization of Ukrainian culture, and the expression of intellectual thought. The newspaper discourse of the war period appears as a vivid representative of the mentality and a powerful source of broadcasting the national code of the Ukrainian ethnos.