The global production of information continuously increases in quantity and variety. However, the tools and technologies developed to handle such large volumes of data have not adequately met the security and privacy requirements. Existing cloud security systems, often managed by a trusted third party, are susceptible to various security risks. To address these challenges and ensure the protection of personal information, blockchain technology emerges as a crucial solution with substantial potential. This research uses the blockchain-powered attribute-aware encryption method to establish a real-time secure communication approach over the cloud. By employing attribute-based encryption technology, data owners can implement fine-grained search permissions for data users. The proposed solution incorporates accessible encryption technology to enable secure access to encrypted data and facilitate keyword searches on the blockchain. This study provides a functional comparison of recently developed attribute-based encryption algorithms. The access control strategy comprises two access tree types and a linear secret-sharing system, serving as the main components. The elliptic curve’s base field was set to 512b, and the bilinear pairing parameter type used was Type-A. This approach involves storing keywords on a remote server and encrypting them using attribute-based encryption. Furthermore, the encrypted data blockchain and the corresponding ciphertext are stored in the blockchain. Numerical experiments were conducted to evaluate the system’s key generation, trapdoor building, and keyword retrieval capabilities.

RSA is one among the most popular public key cryptographic algorithm for security systems. It is explored in the results that RSA is prone to factorization problem, since it is sharing common modulus and public key exponent. In this paper the concept of fake modulus and generalized Pell’s equation is used for enhancing the security of RSA. Using generalized Pell’s equation it is explored that public key exponent depends on several parameters, hence obtaining private key parameter itself is a big challenge. Fake modulus concept eliminates the distribution of common modulus, by replacing it with a prime integer, which will reduce the problem of factorization. It also emphasizes the algebraic cryptanalysis methods by exploring Fermat’s factorization, Wiener’s attack, and Trial and division attacks.

The advancements in communication speeds have enabled the centralized financial market to be faster and more complex than ever. The speed of the order execution has become exponentially faster when compared to the early days of electronic markets. Though the transaction speed has increased, the underlying architecture or models behind the markets have remained the same. These models come with their own disadvantages. The disadvantages are usually faced by non-institutional or small traders. The bigger players, such as financial institutions, have an advantage over smaller players because of factors such as information asymmetry and access to better infrastructure, which give them an advantage in terms of the speed of execution. This makes the centralized stock market an uneven playing field. This paper discusses the limitations of centralized financial markets, particularly the disadvantage faced by non-institutional or small traders due to information asymmetry and better infrastructure access by financial institutions. The authors propose the usage of blockchain technology and the data highway protocol to create a decentralized stock exchange that can potentially eliminate these disadvantages. The data highway protocol is used to generate new blocks with a flexible finality condition that allows for the consensus mechanism to configure security thresholds more freely. The proposed framework is compared with existing frameworks to confirm its effectiveness and identify areas that require improvement. The evaluation of the proposed approach showed that the improved highway protocol boosted the transaction rate compared to the other two mechanisms (PoS and PoW). Specifically, the transaction rate of the proposed model was found to be 2.2 times higher than that of PoS and 12 times higher than that of the PoW consensus model.

With the rapid expansion of digital communication and data sharing, ensuring robust security for sensitive information has become increasingly critical, particularly when data are transmitted over public networks. Traditional encryption techniques are increasingly vulnerable to evolving cyber threats, making single-layer security mechanisms less effective. This study proposes a multi-layered security approach that integrates cryptographic and steganographic techniques to enhance data protection. The framework leverages advanced methods such as encrypted data embedding in images, DNA sequence coding, QR codes, and least significant bit (LSB) steganography. To evaluate its effectiveness, experiments were conducted using text messages, text files, and images, with security assessments based on PSNR, MSE, SNR, and encryption–decryption times for text data. Image security was analyzed through visual inspection, correlation, entropy, standard deviation, key space analysis, randomness, and differential analysis. The proposed method demonstrated strong resilience against differential cryptanalysis, achieving high NPCR values (99.5784%, 99.4292%, and 99.5784%) and UACI values (33.5873%, 33.5149%, and 33.3745%), indicating robust diffusion and confusion properties. These results highlight the reliability and effectiveness of the proposed framework in safeguarding data integrity and confidentiality, providing a promising direction for future cryptographic research.

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones’ features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an $R^2$ of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal.

The purpose of this study is to explore how modern virtual reality technology is applied in India's landscape design. This study provides a brief overview of virtual reality technology and a scientific classification of its main categories. At the same time, the advantages of the currently emerging VR technology are summarised and scientifically analysed and discussed before attempting to apply the technology to landscape design. Research has found that virtual reality can create an immersive environment for users if it is used well in landscape design, bring users a sense of physical and mental enjoyment, and effectively play a more vivid, intuitive, interesting and simple auxiliary role in complex landscape architecture design.