Information Transmission Research Articles

Two-Factor Authentication Inspired Optical Security of Information Enabled by Silica Nanofibers.

Two-factor authentication (2FA) is widely used in informatics for identity verification and information encryption, yet its applications in materials science remain largely underexplored. In this study, a composite film composed of silica nanofibers (NFs) and fluorescent nanofibers (FNFs) is presented, offering a unique material-based approach to 2FA encryption. NFs and FNFs are synthesized via water-in-oil emulsions, resulting in films that can be both liquid- and light-responsive. NFs assembly exhibits intriguing light-scattering characteristics, rendering it opaque under normal conditions but transparent when wetted, functioning as a liquid-triggered optically shielding material. Embedded FNFs remain concealed within NF matrix, becoming fluorescently visible only under ultraviolet A (UVA) light illumination of specific wavelength. The encryption system requires two decryption keys - liquid and UVA light, which must be applied sequentially to successfully access the encoded information. Upon liquid exposure, the film transitions from opaque to transparent, allowing light transmission. Subsequent UVA irradiation reveals the hidden fluorescent patterns labeled by FNFs in film. This sequential encryption-decryption mechanism mimics the principles of 2FA in digital data systems, providing a promising new paradigm for secure information storage and transmission for patterns using material-based strategies.

Establishment of mud crab (Scylla paramamosain) spermatogonial stem cell line: A potential tool for immunological research.

Establishment of mud crab (Scylla paramamosain) spermatogonial stem cell line: A potential tool for immunological research.

A Facile Strategy to Restore the Optic Nerve Functionality Using an Injectable Conducting Hydrogel.

This study introduces a novel injectable conductive polymer hydrogel from poly(3,4-ethylenedioxythiophene) (PEDOT). This hydrogel is designed to facilitate the recovery of electrophysiological function in injured optic nerves. The hydrogel can be injected directly at the injury site and spontaneously gel in place. These findings indicate a remarkable restoration of electrophysiological function, with a two to fourfold increase in amplitude (N1-P1 wavelet) of flash visual evoked potentials. Electroretinography results further support that both a-wave and b-wave amplitudes obtained under various adaptation conditions in the PEDOT hydrogel-treated animal models with optic nerve crush are comparable to those in the control group. The visual cliff testing results further supports the enhanced visual functionality after the hydrogel treatment. This improvement signifies enhanced responsiveness to light stimulation and a substantial boost in axonal transmission of visual information. Additionally, the injection of PEDOT hydrogel significantly mitigatesthe apoptosis of retinal ganglion cells, resulting in a fourfold increase in their survival rate. The ease of preparation and outstanding performance in vivo make it a promising bioengineered material for optic nerve research. This advancement offers new hope for patients with traumatic optic neuropathy, potentially leading to improved treatments and outcomes in optic nerve regeneration.

Spatio‐chemical and spatio‐temporal processes of hierarchical multicompartments: Diffusion‐controlled communication between nano‐ and microcompartments

: Exploring the synergy of feedback behavior and molecular communication between micro‐ and nanocompartments is of great implication for the development of advanced hierarchical living‐like materials. Non‐covalent interactions are the driving forces for dynamic and temporal events in biomimetic structures. Herein, pH‐responsive hierarchical multicompartments (HMC) are constructed via hydrophobic‐hydrophobic interactions between azobenzene units and phospholipid layers through the integration of two distinct structural units: phospholipid‐membranized coacervates (Coa@DMPC) and azobenzene‐functionalized polymersomes (Azo‐Psomes). This enables to study spatio‐temporal signal pathways for biomimetic pH homeostasis and the triggering of feedback‐controlled peroxidase‐like behavior of Azo‐Psomes within HMC. Compared with undocking systems, the information transmission process within HMC shows a high efficiency. Besides the continuous addition of nutrients, the synchronization of two different biomimetic reactions in HMC requires the spatial loading of glucose oxidase and L‐phenylalanine ammonia lyase in coacervates and of L‐phenylalanine or beta‐cyclodextrin/hemin complexes in Azo‐Psomes. Azo‐Psomes exhibit pH‐responsive feedback‐controlled behavior. The pH‐responsive membrane of Azo‐Psomes is responsible for the spatio‐temporal peroxidase‐like activity of lumen‐integrated beta‐cyclodextrin/hemin complexes in Azo‐Psomes. Finally, this strategy provides a new approach to constructing more complex biomimetic systems by interconnecting at least two membrane‐containing compartments to further explore the synergistic mechanisms and feedback behaviors among artificial cell communities.

Spatio-chemical and spatio-temporal processes of hierarchical multicompartments: Diffusion-controlled communication between nano- and microcompartments.

: Exploring the synergy of feedback behavior and molecular communication between micro- and nanocompartments is of great implication for the development of advanced hierarchical living-like materials. Non-covalent interactions are the driving forces for dynamic and temporal events in biomimetic structures. Herein, pH-responsive hierarchical multicompartments (HMC) are constructed via hydrophobic-hydrophobic interactions between azobenzene units and phospholipid layers through the integration of two distinct structural units: phospholipid-membranized coacervates (Coa@DMPC) and azobenzene-functionalized polymersomes (Azo-Psomes). This enables to study spatio-temporal signal pathways for biomimetic pH homeostasis and the triggering of feedback-controlled peroxidase-like behavior of Azo-Psomes within HMC. Compared with undocking systems, the information transmission process within HMC shows a high efficiency. Besides the continuous addition of nutrients, the synchronization of two different biomimetic reactions in HMC requires the spatial loading of glucose oxidase and L-phenylalanine ammonia lyase in coacervates and of L-phenylalanine or beta-cyclodextrin/hemin complexes in Azo-Psomes. Azo-Psomes exhibit pH-responsive feedback-controlled behavior. The pH-responsive membrane of Azo-Psomes is responsible for the spatio-temporal peroxidase-like activity of lumen-integrated beta-cyclodextrin/hemin complexes in Azo-Psomes. Finally, this strategy provides a new approach to constructing more complex biomimetic systems by interconnecting at least two membrane-containing compartments to further explore the synergistic mechanisms and feedback behaviors among artificial cell communities.

Understanding in-situ complexities: a scoping review on the trends of qualitative method design and practice in coach–athlete dialogue studies

ABSTRACT Sport pedagogue–learner interactions encompass complex dynamics of (who, what, when, where, why, and how) speaking and listening acts. As discursive practice normatively frames embodied, pedagogic, and socio-cultural values/biases beyond information transmission, coach–athlete dialogues (CADs) mediate their way of being-in-the-world across multi-dimensions. Recognising this significance, a growing body of qualitative coaching research has applied methodological creativity to seize the in-situ moments of CADs and grasp their emergent, contextual, and nuanced nature, instead of repeating the prominent tendency to manufacture CAD into half-stories of coaches’ behaviours as speakers or athletes’ perceptions as listeners. Nonetheless, the methodological trajectories of such qualitative research (i.e. extant progress, potential limitations, and possible future directions) have seldom been scrutinised. Therefore, this article presents a scoping review on the design and practice of qualitative methods used in 41 CAD studies published from 2000 to 2024. The analysis reveals four major themes: (i) instructor-centred perspective; (ii) space before place; (iii) insufficient attention to non-verbal language-use-in-interaction; and (iv) imbalance of emic–etic approaches. The findings are expected to provide qualitative coaching researchers with reflexive sources for their own and participants’ CAD-related reality (ontology) and knowledge (epistemology), especially when crafting methods to better uncover the in-situ complexities of sport coaching.

Reversible Control of RNA Guided Nucleic Acid Cleavage Based on the Inverse Electron Demand Diels‐Alder Reaction†

Comprehensive SummaryRNA plays a pivotal role in genetic information transmission, gene expression regulation, and key biological processes, making its functional regulation critical. In this study, we introduce an iedDA‐based strategy for post‐synthetic RNA modification, enabling controlled nucleic acid cleavage via the CRISPR‐Cas system. By modifying RNA with trans‐cyclooctene (TCO), its function is paused, and reactivation is achieved using the repair agent dimethyl‐tetrazine (Me2Tz), triggering the iedDA reaction. We demonstrate reversible on/off switching of CRISPR‐Cas activity in vitro and further validate the strategy's applicability to other RNA systems, highlighting its potential for gene editing in cells.

Frontal-auditory cortical interactions and sensory prediction during vocal production in marmoset monkeys.

Frontal-auditory cortical interactions and sensory prediction during vocal production in marmoset monkeys.

MINT: A toolbox for the analysis of multivariate neural information coding and transmission.

Information theory has deeply influenced the conceptualization of brain information processing and is a mainstream framework for analyzing how neural networks in the brain process information to generate behavior. Information theory tools have been initially conceived and used to study how information about sensory variables is encoded by the activity of small neural populations. However, recent multivariate information theoretic advances have enabled addressing how information is exchanged across areas and used to inform behavior. Moreover, its integration with dimensionality-reduction techniques has enabled addressing information encoding and communication by the activity of large neural populations or many brain areas, as recorded by multichannel activity measurements in functional imaging and electrophysiology. Here, we provide a Multivariate Information in Neuroscience Toolbox (MINT) that combines these new methods with statistical tools for robust estimation from limited-size empirical datasets. We demonstrate the capabilities of MINT by applying it to both simulated and real neural data recorded with electrophysiology or calcium imaging, but all MINT functions are equally applicable to other brain-activity measurement modalities. We highlight the synergistic opportunities that combining its methods afford for reverse engineering of specific information processing and flow between neural populations or areas, and for discovering how information processing functions emerge from interactions between neurons or areas. MINT works on Linux, Windows and macOS operating systems, is written in MATLAB (requires MATLAB version 2018b or newer) and depends on 4 native MATLAB toolboxes. The calculation of one possible way to compute information redundancy requires the installation and compilation of C files (made available by us also as pre-compiled files). MINT is freely available at https://github.com/panzerilab/MINT with DOI doi.org/10.5281/zenodo.13998526 and operates under a GNU GPLv3 license.

Assessment of Noise Immunity of Information Transmission System under Multipath Fading Conditions in the Channel

Assessment of Noise Immunity of Information Transmission System under Multipath Fading Conditions in the Channel

A High-Efficiency Wireless Information and Energy Co-Transmission System Based on Self-Compensating Inductive Temperature Sensitivity Error

To address the stability issues of energy and information transmission in wireless power and information transfer system operating over a wide temperature range, this paper establishes a mathematical model of the resonant frequency of an electromagnetic coupling system under varying temperature conditions. Simulations and experiments are conducted to analyze the impact of temperature on resonance characteristics. The results show that within the temperature range of −40 °C to 50 °C, frequency deviation leads to a reduction in the power deviation coefficient to 35.93%. To mitigate this issue, a real-time frequency compensation method based on Direct Digital Synthesis (DDS) is proposed, which dynamically adjusts the operating frequency to ensure that the system remains in optimal resonance. The experimental results demonstrate that the proposed method reduces the system’s operating frequency error from 3 kHz to within 0.2 kHz (a 93.33% reduction), restoring the power deviation coefficient to 0.54% and significantly improving system stability and reliability. This study provides theoretical support and engineering insights for the optimization of electromagnetic coupling wireless power and the information transfer system under wide temperature conditions.

DIGITAL TECHNOLOGIES IN SUPPLY CHAIN MANAGEMENT

The article examines the impact of digital technologies on supply chain management in the modern global economy, which is rapidly transforming under the influence of digitalization. It examines key areas of digital transformation, particularly the implementation of artificial intelligence (AI), the Internet of Things (IoT), and blockchain, and their impact on the efficiency of logistics processes, optimization of operating costs, and improvement of interaction between supply chain participants. It analyzes the advantages of using AI for demand forecasting, optimizing inventory levels, reducing excess stock or shortages of goods, and accurately predicting transportation needs. It examines the capabilities of AI in automating warehouse operations, including picking, packing, and sorting, which reduces labor costs and improves order fulfillment accuracy. It explores the application of IoT technologies for monitoring freight transportation in real-time, which allows for prompt response to deviations from the planned route or delivery schedule and for improving after-sales service by monitoring the condition of products and timely transmission of information to the manufacturer or service center. The possibilities of blockchain for ensuring transparency, data protection, and document flow optimization in logistics are explored, which provides continuous access to information about the movement of goods in real-time, process transparency for all participants in the supply chain, and guaranteed data immutability, which eliminates the possibility of falsification. Examples of successful implementation of digital technologies in supply chains by leading companies, such as Amazon, Walmart and Nike, are given, which use these technologies to increase the efficiency of logistics processes and improve customer experience. The conclusion is made about the need to integrate innovative technologies to increase the competitiveness and resilience of companies in the context of global digitalization, the transition from traditional paper documents to digital solutions, and addressing sustainable development issues. The integration of advanced solutions will contribute to increasing the efficiency of operations, reducing costs and meeting the requirements of sustainable development, which are key priorities of the modern logistics industry.

COMMUNICATION STRATEGY OF UKRAINIAN BUSINESS EXPORTERS IN THE CONTEXT OF GEOPOLITICAL CHALLENGES

The article provides a theoretical and methodological grounds for the communication strategy for exporters as an integral component of business promotion in the international market; systematizes the main determinants which influence the formation of a communication strategy, namely, target audience segmentation and profiling, optimal means of information transmission selection, key messages, assessment of the communication and marketing strategy effectiveness, including a set of measures ensuring clear, consistent and attractive communication. Besides, it analyzes the challenges and problems of Ukrainian exporters in the international market, including the imperfection of infrastructure, insufficient development of financial mechanisms to support exporters, the dependence of the dynamics of Ukrainian exports on changes in the world market conditions and high import dependence, currency fluctuations, political situation and changes in international trade agreements; presents analytical information on Ukrainian commodity exports in conditions of instability in the world markets of agricultural and metallurgical products. A certain set of tools for implementing an international communication strategy is proposed, in particular, participation in exhibitions, digital and content marketing, PR companies to build a positive image and of national product brands which emphasize USP and competitiveness in the foreign market. The article concludes that the communication strategy of Ukrainian business exporters requires a detailed study of local conditions, cultural, demographic, psychological and economic factors which give opportunity for a more accurate determination of international consumers needs and priorities, in other words, a strategic rethinking of export orientation policy and its adaptation in the context of geopolitical challenges.

On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems

A common problem in Message-Passing Neural Networks is oversquashing -- the limited ability to facilitate effective information flow between distant nodes. Oversquashing is attributed to the exponential decay in information transmission as node distances increase. This paper introduces a novel perspective to address oversquashing, leveraging dynamical systems properties of global and local non-dissipativity, that enable the maintenance of a constant information flow rate. We present SWAN, a uniquely parameterized GNN model with antisymmetry both in space and weight domains, as a means to obtain non-dissipativity. Our theoretical analysis asserts that by implementing these properties, SWAN offers an enhanced ability to transmit information over extended distances. Empirical evaluations on synthetic and real-world benchmarks that emphasize long-range interactions validate the theoretical understanding of SWAN, and its ability to mitigate oversquashing.

From Theory to Practice: Intelligent Signal Processing in Emergency Communication Systems

Emergency communication systems play a crucial role in responding to various unexpected incidents by ensuring timely transmission and exchange of information, thereby providing strong support for rescue operations and other emergency actions. As a key technology in emergency communication systems, intelligent signal processing has gradually evolved from theoretical research to practical application, significantly enhancing the performance and reliability of these systems. This paper focuses on intelligent signal processing in emergency communication systems and systematically outlines its development from theory to practice.On the theoretical side, the paper explores the theoretical application of intelligent signal processing in emergency communications, including how intelligent algorithms can improve anti-interference capabilities and enhance signal transmission efficiency.On the practical side, the paper analyzes the current application status of intelligent signal processing technologies in emergency communication systems. Through case studies, it demonstrates the application effectiveness of these technologies in various emergency scenarios, such as natural disasters and public safety incidents. It presents concrete outcomes of intelligent signal processing in enhancing system performance, including optimizing signal quality, achieving fast and accurate signal transmission, and improving system stability.In addition, this paper examines the challenges faced in applying intelligent signal processing to emergency communication systems, such as the complexity of technological implementation, system compatibility issues, and environmental adaptability in real-world scenarios. Corresponding solutions and development suggestions are proposed to address these challenges. By bridging theory and practice, this study seeks to provide a reference for the further development and application of intelligent signal processing in emergency communication systems, supporting continuous improvement and upgrades to better meet actual emergency needs.

A Novel Framework for Agricultural Futures Price Prediction With BERT‐Based Topic Identification and Sentiment Analysis

ABSTRACTIn China's financial and economic system, the agricultural futures market plays an important role in guiding the market to self regulate and providing efficient information transmission for regulators. The effective prediction of futures prices can assist in guiding agricultural production, monitoring operational risks arising from significant price fluctuations, and enhancing the predictability and pertinence of the country's macroeconomic regulation policies. This study investigates the main variety of grain futures—soybean futures, taking into account complex market and non‐market influencing factors. Using historical market data and related news headlines of soybean futures as source data and integrating topic identification and sentiment analysis techniques, a novel framework for predicting agricultural futures prices that integrates topic sentiment is constructed. This model uses BERTopic to extract topic information from agricultural news texts, then integrates FinBERT to construct topic‐based sentiment features, fuses them with structured market features, and constructs LSTM price prediction model with multi‐feature inputs. In order to better model the short‐term features and state transfer patterns of the time series, hidden Markov model (HMM) is further used to extract the hidden states, which are deeply fused with the LSTM model. The empirical results show that the model fusing topic and sentiment features significantly improves the forecasting accuracy in all lags, LSTM works best in short‐term forecasting, and the combination of HMM and LSTM exhibits significant performance advantages in medium‐ and long‐term forecasting. Compared with the baseline model that relies only on market features, topic sentiment features provide important incremental information for price forecasting, and the contribution of each topic sentiment feature calculated based on the PI metric is close to 50%. In addition, deep learning–based prediction model performs better than baseline machine learning models in dealing with extreme external shocks such as climate disasters, the COVID‐19 pandemic, and the Russia–Ukraine conflict.

Classification and pragmatic linguistic characteristics of the short vertical video genre in German-speaking educational discourse

The article considers a new video format that has become extremely popular due to its perfect alignment with mosaic thinking. Although these videos are widely spread on social media, they remain understudied from a linguistic perspective. Despite their recent appearance, short vertical videos within the modern educational discourse exhibit characteristics of an established genre, showcasing users’ creativity and their ability to adapt the format for educational purposes. The author identifies genre-defining (formal) and genre-forming (functional) parameters of VK Clips and proposes their classification. According to specific communicative goals, the article distinguishes between entertainment-educational, expert (methodological, cultural, linguistic), and presentation-educational (featuring language material and speech situations) short vertical videos. The author views the subgenres of VK Clips both multimodal texts and analyzing their features through the lens of various semiotic codes and information transmission channels, employing the method of discourse analysis, and elements of the multimodal transcription method by A. Baldry and P. Thibault, and the comprehensive polysemiotic approach by Y. Gambier. Entertainment-educational videos serve an advertising function. They are few in number and lack common characteristic features, aside from formal ones. The expert videos focus on the blogger as the central figure, whose role is to hold the audience’s attention through the message and their public speaking skills. Special emphasis is placed on verbal linguistic and non-verbal paralinguistic codes, while other codes are used minimally. In contrast, presentation-educational VK Clips actively use a variety of codes and modalities, but the material is presented without in-depth explanation.

Extending continuous flow models of immediate decision reports to delayed decision reports.

Continuous flow and evidence accumulation models have recently been combined to provide an integrated account of decision and motor mechanisms engaged in choice reaction time tasks. According to this account, muscle activation is essentially determined by the evidence accumulation decision variable through a continuous decision-to-motor transmission of information. However, it remains unclear whether and how this framework can be extended to situations that impose a time lag between the commitment to a choice and the expression of that choice through actions. Such situations have been studied using response signal (RS) decision tasks featuring a short delay between the offset of the stimulus and a signal to respond. The present work integrates recent developments in decision-making, working memory, and motor control research to extend models of immediate decision reports to delayed decision reports. We assumed that the evidence accumulation decision variable transitions to sustained activity after hitting a threshold to achieve the short-term maintenance of the selected choice. The level of sustained activity then constitutes the starting point for a second phase of accumulation, in which subjects sample evidence from the RS to activate the muscles. We tested predictions from the theory at the behavioral and muscle activation levels in three RS decision tasks featuring manipulations of stimulus duration, delay duration, and foreknowledge of the stimulus-response mapping. Muscle activation was measured using electromyography. The theory provided a unified account of empirical effects. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Tourism demand forecasting: a novel multi-channel imaging model

PurposeThis study aims to introduce an innovative multi-channel imaging technique aimed at mitigating deep learning overfitting and facilitating the automatic extraction of features from limited 1D data.Design/methodology/approachThe proposed framework consists of five key component: dimensionality reduction, sequence image generation, image stitching, feature extraction and model training. It converts 1D multi-temporal data into multiple 2D images utilizing Markov transition field, Gramian angular field and recurrence plot. These single-channel images are stitched into a larger n-channel image, which is processed by a convolutional neural network for feature extraction and forecasted using a long short-term memory network.FindingsThe results demonstrate that the proposed multi-channel imaging technique outperforms all benchmark models. This conversion captures underlying patterns and enhances information transmission. Additionally, models with multi-time series configurations perform better than single-time series setups, highlighting that data are more crucial than advanced models in forecasting.Originality/valueThis pioneering study explores the role of non-economic variables in tourism forecasting. The proposed multi-channel time series imaging model not only applies to tourism but also offers potential for interdisciplinary applications.

Disrupted coordination between primary and high-order cognitive networks in Parkinson's disease based on morphological and functional analysis.

Patients with Parkinson's disease (PD) exhibit structural and functional alterations in both primary and high-order cognitive networks, but the interactions within aberrant functional networks and relevant structural foundation remains unexplored. In this study, the functional networks (FN) and the morphometric similarity networks (MSN) were constructed respectively based on the time-series data and gray matter volume from the MRI data of PD patients and controls. The efficiency, average controllability and k-shell values of the FN and MSN were calculated to evaluate their ability of information transmission and identify structural and functional abnormalities in PD. The abnormal regions were categorized into five types: regions with MSN abnormalities, regions with FN abnormalities, regions with both MSN and FN abnormalities, regions with abnormalities only in MSN but not in FN and regions with abnormalities only in FN but not in MSN. Further, the dynamic causal model (DCM) was used to evaluate the causal relationship of information flow between the identified regions. In the network property analysis of the FN, PD patients showed decreased global efficiency and connectivity in the visual network (VIS) and increased global efficiency in higher-order cognitive networks, including the ventral attention network (VAN), default mode network (DMN), and the limbic network (LIM) but no difference in MSN. In the DCM analysis of the regions, PD patients exhibited increased excitatory transition from the visual areas to the superior frontal gyrus, whereas had disturbed information flow from the visual areas to the insula and the orbitofrontal cortex. These findings suggest changes in structural and functional brain of PD patients, and advance our understanding of PD pathogenesis from different neural dimensions.