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DNA storage is a new digital data storage technology based on specific encoding and decoding methods between 0 and 1 binary codes of digital data and A-T-C-G quaternary codes of DNAs, which and is expected to develop into a major data storage form in the future due to its advantages (such as high data density, long storage time, low energy consumption, convenience for carrying, concealed transportation and multiple encryptions). In this review, we mainly summarize the recent research advances of four main encoding and decoding methods of DNA storage technology: direct mapping method between 0 and 1 binary and A-T-C-G quaternary codes in early-stage, fountain code for higher logical storage density, inner and outer codes for random access DNA storage data, and CRISPR mediated in vivo DNA storage method. The first three encoding/decoding methods belong to in vitro DNA storage, representing the mainstream research and application in DNA storage. Their advantages and disadvantages are also reviewed: direct mapping method is easy and efficient, but has high error rate and low logical density; fountain code can achieve higher storage density without random access; inner and outer code has error-correction design to realize random access at the expense of logic density. This review provides important references and improved understanding of DNA storage methods. Development of efficient and accurate DNA storage encoding and decoding methods will play a very important and even decisive role in the transition of DNA storage from the laboratory to practical application, which may fundamentally change the information industry in the future.

Polar code is currently implemented in high-speed high performance modern communication systems. The polar code enabled these systems to transmit near Shannon’s limit with minimum bit error rate (BER). However, the performance of polar encoding is enhanced using a systematic encoding technique that convolves the data, providing higher noise immunity. This paper provides the hardware implementation of systematic and nonsystematic encoding using an FPGA to provide fully parallel operation for maximum processing speed. Both techniques' performance is evaluated under the additive white Gaussian noise (AWGN) channel and a practical indoor multipath channel. Both techniques showed a significant BER improvement compared to BPSK. The tests showed that the systematic performance is superior to the nonsystematic technique.

Nonlinear dynamic systems and chaotic systems have been quite exhaustively researched in the domain of cryptography. However, the possibility of using fractional chaotic systems in the cryptosystem design has been much less explored while it bears advantages such as enlarged keyspace and better resistance to attack compared to classical nonlinear systems. This paper, therefore, proposes a novel structure for the pseudo-random number generator based on 3 different fractional chaotic systems, namely fractional Chen system, Lu system, and fractional generalized double-humped logistic map. Then, the outputs of this fractional chaotic pseudo-random number generator are used as a keystream for an image encryption scheme. The confusion layer of the scheme is conducted by a dynamic DNA encoding and decoding method combined with a 2D cat map for the permutation in the DNA bases level. The diffusion layer is performed through the adoption of a 32 bits discrete logistic map. The performance and security analyses have been conducted for the above-designed cryptosystem, proving that the proposed cryptosystem is practical and efficient, and can be successfully implemented in image encryption.

At the end of his life, Stuart Hall called for the reengagement of Cultural Studies and Marxism. This paper contributes to this task. It analyses Stuart Hall’s works on communication and the media.The goal of the paper is to read Stuart Hall in a manner that can inform the renewal of Marxist Humanism and the development of a Marxist-Humanist theory of communication. This involves reconstructing elements of Hall’s approach, criticising certain aspects of his work, and through this engagement developing new theory elements.The article’s analysis of Stuart Hall’s theory of communication and the media is conducted in four steps. First, the paper reengages and re-evaluates what Hall called the two paradigms of Cultural Studies: Structuralism and “Culturalism”/Humanism. It discusses the role of human agency in society. Second, the paper engages with Hall’s and Althusser’s notions of articulation and sets the notion of articulation in relation to the concept of communication. Third, it discusses the relationship between communication and work in the context of Hall’s works. Fourth, the article revisits and engages with Hall’s encoding/decoding-model in the context of digitalisation.This paper grounds a dialectical concept of communication that is based on the dialectic of articulating and articulatedness, the dialectic of work and communication, as well as the dialectic of communication in the public sphere and society’s power forcefields. It shows how a critical, dialectical theory of communication benefits from engagement with Stuart Hall’s works. The present work argues with, for, against, and beyond Stuart Hall in order or productively draw on ideas that emerge from this engagement.

The Internet of Vehicles (IoV), as the core of intelligent transportation system, enables comprehensive interconnection between vehicles and their surroundings through multiple communication modes, which is significant for autonomous driving and intelligent traffic management. However, with the emergence of new applications, traditional communication technologies face the problems of scarce spectrum resources and high latency. Semantic communication, which focuses on extracting, transmitting, and recovering some useful semantic information from messages, can reduce redundant data transmission, improve spectrum utilization, and provide innovative solutions to communication challenges in the IoV. This paper systematically reviews state-of-the-art semantic communications in the IoV, elaborates the technical background of the IoV and semantic communications, and deeply discusses key technologies of semantic communications in the IoV, including semantic information extraction, semantic communication architecture, resource allocation and management, and so on. Through specific case studies, it demonstrates that semantic communications can be effectively employed in the scenarios of traffic environment perception and understanding, intelligent driving decision support, IoV service optimization, and intelligent traffic management. Additionally, it analyzes the current challenges and future research directions. This survey reveals that semantic communications have broad application prospects in the IoV, but it is necessary to solve the real existing problems by combining advanced technologies to promote their wide application in the IoV and contributing to the development of intelligent transportation systems.

There are many studies on 3D reconstruction based on monocular vision, but for complex surface parts, contour occlusion problems will occur, which requires binocular or multi-eye vision for 3D reconstruction. This paper mainly uses binocular structured light vision to study 3D reconstruction. The specific methods are: use structured light coding to calibrate the binocular camera, the projector and the left and right cameras to obtain the calibration parameters, and then obtain the 3D coordinates based on the triangulation principle that there is only one intersection between the camera straight line equation and the projector plane equation in space. Because of the binocular structured light used in this article, the point clouds of the left and right cameras need to be merged to complete the stitching process of the two points clouds. Through the verification of this methods, the stitching error is good, and the point cloud can be streamlined later to improve the point cloud registration rate.

This paper proposes a novel phase-based short-term traffic flow prediction method based on parallel multi-task learning for isolated intersections. Different from traditional short-term traffic flow prediction methods, we take the traffic flow of each phase as the minimum prediction unit, instead of directly utilising the traffic flow of a single lane with large random fluctuations. Meanwhile, we design a novel encoding and decoding structure whereby external influencing factors have been incorporated both into encoding and decoding operations. Furthermore, a fusion strategy is proposed to predict the traffic flow of each phase by integrating the traffic flows of lanes whose right of way are provided by the phase. In the fusion strategy, we develop a parallel multi-task prediction framework whereby a new loss function is defined to improve the prediction accuracy. Finally, the proposed method is tested with the traffic flow data collected from an intersection of South Changsheng Road located in the city of Jiaxing. The findings illustrate that the proposed method can achieve better prediction results at different sampling time scales, compared to the existing short-term traffic flow prediction methods.

With the rapid advancement of large-language models in natural language processing (NLP), many studies have explored their role in brain encoding and decoding. In this study, we developed BrainLM, a pre-trained multimodal model that incorporates paired brain activity data from text stimuli. We demonstrated its accuracy in brain encoding and decoding across multiple NLP tasks. Our research produced several notable findings: we successfully developed a model for brain encoding and decoding, validated its reliability through bidirectional experiments, and outperformed 20 state-of-the-art models in brain encoding tasks. Additionally, we designed an autoencoder module to extract brain features. We extended the capabilities of BrainLM to new datasets and explored multilingual tasks using transfer learning, which enhanced the generalization ability of the model. Notably, BrainLM achieved 51.75% accuracy in binary classification tasks and increased the correlation coefficient by 3%–15% in brain prediction tasks. This study expands the applications of BrainLM and uncovers the complex interactions between brain regions and language models across different linguistic environments.

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions.

Defects in signaling pathways are the root cause of many disorders. These malformations come in a wide variety of types, and their causes are also very diverse. Some of these flaws can be brought on by pathogenic organisms and viruses, many of which can obstruct signaling processes. Other illnesses are linked to malfunctions in the way that cell signaling pathways work. When thinking about how errors in signaling pathways might cause disease, the idea of signalosome remodeling is helpful. The signalosome may be conveniently divided into two types of defects: phenotypic remodeling and genotypic remodeling. The majority of significant illnesses that affect people, including high blood pressure, heart disease, diabetes, and many types of mental illness, appear to be caused by minute phenotypic changes in signaling pathways. Such phenotypic remodeling modifies cell behavior and subverts normal cellular processes, resulting in illness. There has not been much progress in creating efficient therapies since it has been challenging to definitively confirm this connection between signalosome remodeling and illness. The considerable redundancy included into cell signaling systems presents several potential for developing novel treatments for various disease conditions. One of the most important pathways, NF-κB, controls several aspects of innate and adaptive immune responses, is a key modulator of inflammatory reactions, and has been widely studied both from experimental and theoretical perspectives. NF-κB contributes to the control of inflammasomes and stimulates the expression of a number of pro-inflammatory genes, including those that produce cytokines and chemokines. Additionally, NF-κB is essential for controlling innate immune cells and inflammatory T cells’ survival, activation, and differentiation. As a result, aberrant NF-κB activation plays a role in the pathogenesis of several inflammatory illnesses. The activation and function of NF-κB in relation to inflammatory illnesses was covered here, and the advancement of treatment approaches based on NF-κB inhibition will be highlighted. This review presents the temporal behavior of NF-κB and its potential relevance in different human diseases which will be helpful not only for theoretical but also for experimental perspectives.