Accuracy Of Information Transmission Research Articles

Enhancing Communication in CPS Using Graph-Based Reply Relationship Identification in Multi-Party Conversations

To enhance communication and collaborative work efficiency in cyber–physical systems (CPSs) within the Industry 4.0 environment, this study investigates a graph-based machine learning approach aimed at optimizing information interaction during multi-party conversations. Devices within CPSs must efficiently exchange information in real time to synchronize operations and responses. This research treats these interactions as intricate graph structures and uses graph learning techniques to accurately identify communication links and dependencies among devices. This improvement leads to more accurate decision-making and smoother operations. Our methodology involves a real-time analysis of structural patterns and node attributes within conversations, improving information flow and comprehension. The empirical findings demonstrate that this approach significantly enhances production efficiency, system adaptability, and minimizes delays attributed to communication misunderstandings. Our method can effectively identify the communication relationships between devices, significantly improving the efficiency and accuracy of information transmission. This improved communication capability leads to an enhanced production efficiency of the entire system.

Trusted and secure communication system using intelligent devices for individuals with disabilities

Disability has traditionally posed a significant barrier for millions of individuals who are unable to benefit from current technologies such as digital interfaces, the internet, and personal computers. Nowadays, developments in information communication and technology (ICT) are transforming the way we handle and interact with our environment, making it easier and more convenient. However, individuals with disabilities face numerous restrictions and challenges, hindering their full involvement and access to the devices. This paper proposes an efficient and trusted communication mechanism that utilizes knowledge factors and friendship similarity schemes to improve the accessibility and accuracy of information transmission in a networked environment for disabled people. The proposed mechanism ensures the involvement of trusted devices in the network for accuracy and immediate decisions for disabled individual. The proposed approach is validated and compared to existing methods using various security metrics, including social trust, objective trust, experienced trust, and recommended trust. Through this research, we aim to address the barriers faced by individuals with disabilities and provide them with equal opportunities to benefit from modern technological advancements.

НАТУРАЛІСТИЧНІ ОЗНАКИ У ТВОРЧОСТІ КОСТЯНТИНА СРОКОВСЬКОГО (НА МАТЕРІАЛІ НОВЕЛИ «ДВІ ЦЕГЛИ»)

In the article, a literary analysis of the content and form of the novella "Two Bricks" by the Ukrainian writer Kostyantyn Srokovsky was carried out. The main literary techniques and tools used by naturalists in their works and their influence on the clear presentation and accuracy of information transmission about contemporary events are considered. The duration of the existence of the naturalism style in Ukrainian and European literature is determined. In the story "Two Bricks" is analyzed the writer’s image of the picture of a terrible and impressive incident from the life of ordinary Ukrainian peasants – the murder of a man's brother-in-law for two bricks that he stole from him. It was determined the writer's use of naturalistic means of image, which gave the author the opportunity to reproduce the depicted picture in accordance with real life. They are: 1) depicting the life of the characters under the influence of the physiological state of a person, as well as under the influence of environment; 2) documentary reproduction of the event, transmission of information as in a document (protocol); 3) the objectivity, or depicting reality while avoiding the author's presence; 4) photographic accuracy of the depicted events, the writer's attention to the smallest details of what happened; 5) truthful reproduction of events according to life and the emergence of factography on this basis, but at the same time recording these events without their explanation and penetrating into the essence of the happened events (the writer does not explain the reasons for the murder of the hero’s brother-in-law, but only states this event). As a result of the analysis, it is proposed to consider the Ukrainian writer Kostyantyn Srokovsky as an artist who followed the requirements of the naturalism style initiated by E. Zola in the second half of the 19th century.

Application of hybrid encryption algorithm in physical layer software control

In order to address the risk of information leakage in physical layer software, the author proposes the application of hybrid encryption algorithms in physical layer software control. First of all, on the basis of the successful construction of the hardware platform, select the baseband chip of the encryption terminal, calculate the block cipher in the symmetric encryption algorithm, and establish a RAS cryptosystem to encrypt the transmission system in a mixed way, so as to realize the loss prevention transmission of user information data. The experimental results showed that the accuracy of the first transmission experiment reached 98.5%. After multiple experiments, it was concluded that the accuracy of network transmission systems using hybrid encryption algorithms is relatively higher than traditional methods in the process of information and data transmission. In the previous experiment, the accuracy could still reach 98%, which proves that the accuracy of information transmission has significantly improved compared to traditional methods. It has been proven that compared with traditional network transmission systems, the transmission accuracy of the designed transmission system is significantly improved, effectively avoiding the loss of important data information for users.

Accuracy Enhancing Interface Treatment Algorithm: The Back and Forth Error Compensation and Correction Method

The accuracy of information transmission while solving domain decomposed problems is crucial to the smooth transition of a solution around the interface/overlapping region. This paper describes a systematical study on an accuracy-enhancing interface treatment algorithm based on the back and forth error compensation and correction method (BFECC). By repetitively employing a low order interpolation technique (usually local 2nd order) 3 times, this algorithm achieves local 3rd order accuracy. Analytical derivation for any dimensions is made, and the “superconvergence” phenomenon (4th order accuracy) is found for specific positioning of the source and target grids. Its limitation has also been studied. With rotated grids or grids of different sizes, this method can still reduce the error but fails to improve the order of the underlying interpolation technique. A series of numerical experiments on meshes with various translations, rotations, spacing ratios, and perturbations have been tested. Different interface treatments applied to cavity flow are compared. The 3D flow example shows that the BFECC method positively impacts the convergence of the domain decomposed problem.

An improved DECPSOHDV-Hop algorithm for node location of WSN in Cyber–Physical–Social-System

Cyber-Physical Social System (CPSS) refers to the interaction between cyber-physical systems and social networks. The accuracy of information transmission in Cyber-Physical System is very important, and the information transmission of WSN node positioning occupies an important position. Aiming at the ubiquitous positioning accuracy and resource allocation problems in the node positioning, we propose an improved Chaotic Particle Swarm Optimization Hybrid Distance Vector Hopping Algorithm Based on Differential Evolution (DECPSOHDV-Hop) algorithm. This algorithm uses an improved algorithm parallel mechanism in the WSN node positioning, which can be more accurately positioned unknown node through dynamic optimization. In addition, when solving high-dimensional function optimization problems, particle swarm optimization (PSO) algorithms tend to converge prematurely and fall into local extremes. Therefore, based on the (DE)/current-to-best/1 operator, this paper proposes chaotic PSO algorithms of hybrid and parallel. For particle optimization in the difference operator, Gaussian/normal distribution is used for balance optimization. The hybrid method selects the basic particle swarm update strategy according to the probability, simplified the particle swarm update strategy or differential optimization to update the individual. The parallel method uses the improved model to update the Gaussian mutation operator of DE to update the entire population. In the simulation experiment results, the results of testing 20 high-dimensional benchmark functions show that compared with PSO and CPSO, the above two improved algorithms have better search accuracy and higher convergence speed, as well as the optimization results of the test function; In WSN node positioning, the positioning accuracy of the DECPSOHDV-Hop algorithm is as high as 90%, and the excellent positioning stability of the algorithm is verified from three different standards.

Research on disaster information dissemination based on social sensor networks

Sudden disaster events are usually unpredictable and uncontrollable, and how to achieve efficient and accurate disaster information dissemination is an important topic for society security. At present, social sensor networks which integrate human mobile sensors and traditional physical sensors are widely used in dealing with emergencies. Previous studies mainly focused on the impact of human mobility patterns on social sensor networks. In this article, based on the inherent autonomy property of human individuals, we propose a social sensor information dissemination model, which mainly focuses on the impact of the individual characteristics, social characteristics, and group information dissemination mode on social sensor networks. Specifically, the human sensor model is first constructed based on the inherent social and psychological attributes of human autonomy. Then, various information dissemination models such as one-to-one, one-to-many, and peer-to-peer are proposed by considering different transmission media and human interaction preferences. We simulate the environment of information dissemination in disaster events based on the NetLogo platform. Evaluation matrix is applied to test the performance of social sensor information dissemination model, such as event dissemination coverage, event delivery time, and event delivery rate. With the comparisons to epidemic model, social sensor information dissemination model shows excellent performance in improving the efficiency and accuracy of information transmission in disaster events.

Establishment, Analysis, and Application of the Information Transmission and Response Model for Students of Vocational Colleges under Public Health Emergencies.

To provide a basic quantitative mathematical model for data analysis, decision-making support, and application of information systems oriented to emergency research, this paper established an information transmission response model for school students under such system mathematically based on actual school information transmission data during COVID-19 prevention. This paper proposes an emergency information management method—a two-step emergency information management method. It can be referenced for promotion of the development of IT-based school management, enhancement of IT application in school emergency information management, and improvement of the speed and accuracy of information transmission.

Superresolution Reconstruction of Remote Sensing Image Based on Middle-Level Supervised Convolutional Neural Network

Image has become one of the important carriers of visual information because of its large amount of information, easy to spread and store, and strong sense of sense. At the same time, the quality of image is also related to the completeness and accuracy of information transmission. This research mainly discusses the superresolution reconstruction of remote sensing images based on the middle layer supervised convolutional neural network. This paper designs a convolutional neural network with middle layer supervision. There are 16 layers in total, and the seventh layer is designed as an intermediate supervision layer. At present, there are many researches on traditional superresolution reconstruction algorithms and convolutional neural networks, but there are few researches that combine the two together. Convolutional neural network can obtain the high-frequency features of the image and strengthen the detailed information; so, it is necessary to study its application in image reconstruction. This article will separately describe the current research status of image superresolution reconstruction and convolutional neural networks. The middle supervision layer defines the error function of the supervision layer, which is used to optimize the error back propagation mechanism of the convolutional neural network to improve the disappearance of the gradient of the deep convolutional neural network. The algorithm training is mainly divided into four stages: the original remote sensing image preprocessing, the remote sensing image temporal feature extraction stage, the remote sensing image spatial feature extraction stage, and the remote sensing image reconstruction output layer. The last layer of the network draws on the single-frame remote sensing image SRCNN algorithm. The output layer overlaps and adds the remote sensing images of the previous layer, averages the overlapped blocks, eliminates the block effect, and finally obtains high-resolution remote sensing images, which is also equivalent to filter operation. In order to allow users to compare the superresolution effect of remote sensing images more clearly, this paper uses the Qt5 interface library to implement the user interface of the remote sensing image superresolution software platform and uses the intermediate layer convolutional neural network and the remote sensing image superresolution reconstruction algorithm proposed in this paper. When the training epoch reaches 35 times, the network has converged. At this time, the loss function converges to 0.017, and the cumulative time is about 8 hours. This research helps to improve the visual effects of remote sensing images.

A Hybrid Speech Enhancement Algorithm for Voice Assistance Application.

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.

Design of a MAPK signalling cascade balances energetic cost versus accuracy of information transmission

Cellular processes are inherently noisy, and the selection for accurate responses in presence of noise has likely shaped signalling networks. Here, we investigate the trade-off between accuracy of information transmission and its energetic cost for a mitogen-activated protein kinase (MAPK) signalling cascade. Our analysis of the pheromone response pathway of budding yeast suggests that dose-dependent induction of the negative transcriptional feedbacks in this network maximizes the information per unit energetic cost, rather than the information transmission capacity itself. We further demonstrate that futile cycling of MAPK phosphorylation and dephosphorylation has a measurable effect on growth fitness, with energy dissipation within the signalling cascade thus likely being subject to evolutionary selection. Considering optimization of accuracy versus the energetic cost of information processing, a concept well established in physics and engineering, may thus offer a general framework to understand the regulatory design of cellular signalling systems.

ARGUMENTATION IN BUSINESS PUBLICATIONS (BASED ON THE MATERIALS PUBLISHED IN THE “KOMMERSANT”)

The article highlights and analyzes special methods of argumentation used in Russian business publications on a significant theoretical and empirical basis. The study reveals the logic of such argumentation and its effectiveness for representing the issues covered by business media. The article establishes the interrelationship between the features presented and the professional standards used in business publications to ensure objectivity, accuracy of information transmission, completeness and depth of analysis, and balance of opinions presented in the materials. The analysis and the conclusions are illustrated by the materials published in the newspaper “Kommersant”, the flagship of Russian business journalism, over the period from October 2019 to March 2020, with a total of more than 100 publications. The materials are devoted to various issues of economic development in Russia and the Ural Federal district, and are written in various genres and employ different composition techniques. Content analysis has become the main method of research. It is a method of qualitative and quantitative analysis of the content of certain documents in order to identify or measure various facts and trends reflected in these documents. The following types of “special” argumentation peculiar to business journalism publications have been discovered: expert argumentation, anonymous source argumentation, statistical data argumentation, analogy argumentation, infographics argumentation, “argumentative” methods of authored syntax, background argumentation, and authoritative argumentation. Identification of the typical features of argumentation in the materials of business media allows the researcher to see the tendencies in the development of mass media related to the business segment, and to observe the further formation of professional standards of business journalism in this development. The results obtained from this study can be useful for both practicing business journalists and students of journalism and philological departments.

Research on Accurate Information Pushing Based on Human Network

Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship between them of which 43 used WeChat and 44 used QQ, and it also used the tool Netdraw to visualize the network sociogram. By mining the four aspects of density, accessibility, centrality, block model, the results demonstrated that QQ social network and WeChat social network existed the phenomenon of small world, leaders and subgroups, and the key nodes of QQ human network were more than WeChat network. Through using the key nodes, it can push the precise and efficient information and improve the accuracy of information transmission and impact among network members.

Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: a computational modeling study

Deep brain stimulation (DBS) and lesioning are two surgical techniques used in the treatment of advanced Parkinson's disease (PD) in patients whose symptoms are not well controlled by drugs, or who experience dyskinesias as a side effect of medications. Although these treatments have been widely practiced, the mechanisms behind DBS and lesioning are still not well understood. The subthalamic nucleus (STN) and globus pallidus pars interna (GPi) are two common targets for both DBS and lesioning. Previous studies have indicated that DBS not only affects local cells within the target, but also passing axons within neighboring regions. Using a computational model of the basal ganglia-thalamic network, we studied the relative contributions of activation and silencing of local cells (LCs) and fibers of passage (FOPs) to changes in the accuracy of information transmission through the thalamus (thalamic fidelity), which is correlated with the effectiveness of DBS. Activation of both LCs and FOPs during STN and GPi-DBS were beneficial to the outcome of stimulation. During STN and GPi lesioning, effects of silencing LCs and FOPs were different between the two types of lesioning. For STN lesioning, silencing GPi FOPs mainly contributed to its effectiveness, while silencing only STN LCs did not improve thalamic fidelity. In contrast, silencing both GPi LCs and GPe FOPs during GPi lesioning contributed to improvements in thalamic fidelity. Thus, two distinct mechanisms produced comparable improvements in thalamic function: driving the output of the basal ganglia to produce tonic inhibition and silencing the output of the basal ganglia to produce tonic disinhibition. These results show the importance of considering effects of activating or silencing fibers passing close to the nucleus when deciding upon a target location for DBS or lesioning.

Networked Optimal Decision Support System for Cutting Parameters

The real-time, the consistency and the accuracy of information transmission in cutting process are poor, and it is hard for manufacturing enterprises to enhance the machining efficiency and quality. Consequently, based on the real-time dynamic environment of cutting system and oriented to first-line production and operating workers, a networked optimal decision support system for cutting parameters was constructed; its system structure and function structure were described and its running mode was analyzed likewise. On the basis of research on the normative description of cutting process knowledge, an ontology-based description method of cutting process knowledge was proposed.