The implementation of dynamic and realistic weather systems in video games represents a cutting-edge ambition in environmental simulation and player immersion. This review article investigates the purpose behind dynamic weather, its evolution throughout the history of video games, the core techniques and design philosophies behind weather-based mechanics and how the game engine lend assistance to simulate realistic weather. A wide range of sources was analyzed from academic papers to developer interviews and the case studies performed on notable titles from past decade that set a benchmark in weather simulation such as The Witcher 3: Wild Hunt and Ghost of Tsushima (GoT) to list a few, highlight the interplay between narrative context and technical execution. This study also explored challenges including performance optimization, synchronizing audio-visual transitions and maintaining gameplay clarity during weather events. This work contributes to a deeper understanding of how weather can function not only as visual ambiance but as a dynamic gameplay element. Ultimately, the findings aim to assist developers and researchers in crafting weather systems that balance technical innovation with emotional impact.

This study presents first micro-analysis of player sentiment toward AI companions in commercially successful video games. A custom dataset of 15,412 Steam reviews was constructed and used, which contained discourse regarding AI companions. It spanned 17 titles across RPG, JRPG, open-world, shooter, and cinematic action-adventure genres. The research examines how players evaluate companion behavior, personality, and performance. Lexical exploratory data analysis (using unigram, bigram, and keyword co-occurrence analysis) and VADER sentiment analysis were performed to identify sentiment patterns and recurring evaluative themes. Results indicate that functional competence in combat, coordination, and survival is the baseline expectation for positive perception, while mechanical inabilities drive negative sentiment. Emotional integration and narrative embedding were found to amplify positive sentiment and to moderate technical shortcomings. The findings further demonstrate that companion discourse density and assessment are genre sensitive. Systemic companions highlight performance-oriented evaluation, whereas narrative companions are evaluated on the basis of relational depth and character identity. Importantly, companion-specific sentiment emerges independent of overall sentiment for the game. Through empirical evidence gathered, the study extends current literature while validating companion design concepts.

Copyright violation issues have a growing impact on applications of the digital era, especially images. It is not easy to guarantee the copyright protection of essential information. This paper presents a robust copyright protection technique with high embedding capacity for color images. The technique first fuses the multi-focus images using non-subsampled contourlet transform (NSCT) to ensure that the fused images have more rich information than a single image. Further, hash value of the cover media is computed for authentication purposes. The fused image and hash value of the cover is then embedded into the cover media with the help of transformed-domain schemes. To achieve higher security, we apply an encryption scheme on a fused media watermark before embedding it into the cover. Lastly, a hybrid optimization algorithm is employed to compute an optimal factor, which makes a good imperceptibility and robustness at the same time. We demonstrate that the proposed technique is effective and resistant to common attacks on image datasets. Compared to existing works, this work increases the 9.5% robustness and 8.8% quality with high embedding capacity.

Summary Due to the remarkable development of Internet technologies, a great deal of valuable digital data is now transmitted over public networks. To guarantee the security of this data during the transfer process, the authentication of its integrity is extremely important. This paper introduces a robust and secure dual‐watermarking‐based fusion of watermarking, optimization, and a compression method utilizing non‐sub‐sampled contourlet transform (NSCT), redundant discrete wavelet transform (RDWT), and singular value decomposition (SVD). In our method, we first apply the NSCT to a higher entropy sub‐band of the host image. Then, our method uses RDWT‐SVD on higher frequency coefficients of the NSCT image. A similar procedure is followed for both mark images. Finally, an appropriate scaling factor, as obtained by fuzzy inference system, is used to invisibly embed the singular values of both mark data into the host image. Here, any more important mark data are scrambled before the embedding process. The simulation tests reveal that the proposed technique is not only imperceptible and secure but also robust against common attacks. The suggested method has a superior ability to extract hidden information than previous conventional techniques.

Nowadays, the demand for digital images from different intelligent devices and sensors has dramatically increased in smart healthcare. Due to advanced low-cost and easily available tools and software, manipulation of these images is an easy task. Thus, the security of digital images is a serious challenge for the content owners, healthcare communities, and researchers against illegal access and fraudulent usage. In this article, a secure medical image encryption algorithm, EiMOL , based on optimization and the Lorenz system, is proposed for smart healthcare applications. In the first stage, an optimized random sequence (ORS) is generated through directed weighted complex network particle swarm optimization using the genetic algorithm (GDWCN-PSO). This random number matrix and the Lorenz system are adopted to encrypt plain medical images, obtaining the cipher messages with a relationship to the plain images. According to our obtained results, the proposed EiMOL encryption algorithm is effective and resistant to the many attacks on benchmark Kaggle and Open-i datasets. Further, extensive experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art approaches.