Because of their smaller size, microstrip patch antennas are more often utilized in current communication equipment than traditional antennas. In order to design an effective, low profile, small, compatible, and affordable microstrip antenna, authors have used a survey of commonly used techniques and designs found in microstrip antenna papers. These techniques are primarily used to design reconfigurable, multiband, and wideband antennas. Following this, a starter patch design with dimensions is provided, along with the technique that will be used to analyses various antenna parameters.

A solar air heater (SAH) is an energy-efficient device designed to harness solar energy to heat air, which can then be used for a wide range of applications such as space heating, industrial processes, ventilation, and drying. Comparison of the experimental and predicted Values of Nusselt Number. It is found that the smooth plate data agree reasonably well with the values predicted. the value of convective heat transfer coefficient increase with the increasing Reynolds Number and increasing the value of roughness. Nusselt number increases with a rise in Reynolds number. The maximum Nusselt number is observed for the 60° double-inclined roughened plate with a 3.5 mm gap. The Nusselt number represents the ratio of conductive resistance to convective resistance in heat flow. As the Reynolds number increases, the boundary layer thickness decreases, leading to a reduction in convective resistance, which subsequently results in a higher Nusselt number. At low Reynolds numbers, the improvement in the Nusselt number compared to a smooth plate is relatively small. This can be attributed to the thicker laminar sublayer, where the flow is slowed down by the roughness elements. Furthermore, the figure demonstrates that the enhancement in heat transfer for roughened plates relative to smooth plates also increases with rising Reynolds numbers.

The widespread dissemination of fake news on social media presents a significant challenge in today’s digital era, calling for intelligent and scalable detection models. Traditional machine learning and deep learning techniques often fail to capture the complex relational structures inherent in misinformation spread. This paper explores two advanced graph-based approaches—Attention-enhanced Graph Neural Networks (GNNs) and Hypergraph Neural Networks (HGNNs)—using the UPFD (User Profile Fake News Detection) dataset. The first model leverages attention mechanisms in GNNs to dynamically weigh contextual node relationships, improving accuracy, precision, recall, and F1-score. It also incorporates preprocessing strategies like node preparation and retweet handling to mitigate overfitting, especially in long training cycles. While effective on UPFD, models like GraphSAGE show promise on larger datasets such as Gossipcop, highlighting the need for scalable solutions. The second approach introduces an HGNN framework that models higher-order interactions among users, posts, and news articles using hyperedges and incidence matrices. This structure allows for richer feature extraction and, when combined with attention, further enhances performance over traditional GNNs and other baseline models. These findings underline the value of capturing both local and global relationships in fake news detection and point toward future improvements in feature mapping, scalability, and multilingual adaptability.

In recent years, the prevalence of hate speech, offensive language, sexism, racism, cyberbullying, and other forms of online abuse has escalated significantly on social media platforms such as Facebook, Twitter, and Instagram. Individuals often exploit the openness and anonymity of these platforms to propagate harmful content, tarnishing the reputation of others without fear of consequences. Despite ongoing efforts by social media platforms to curb such abusive activities, their existing mechanisms have proven inadequate in effectively detecting and moderating hate speech and offensive language. Numerous companies and research institutions are investing significant resources in developing solutions to mitigate this problem. However, the task remains challenging due to the subtle and evolving nature of online abuse and the need for extensive manual intervention to identify and remove harmful content. One of the primary challenges in automated hate speech detection is the ability to accurately distinguish between hate speech, offensive language, and other forms of abuse, such as cyberbullying. This distinction is critical for developing reliable, scalable, and interpretable models that can address the growing threat of online abuse while safeguarding user expression and maintaining platform integrity.

Cloud computing has emerged as a dominant paradigm in modern computing by offering scalable, on-demand resources over the internet. However, with the rapid increase in user demands and dynamic workload variations, efficient load balancing has become a critical concern to ensure optimal resource utilization, minimal response time, and high availability. This research provides an extensive review and comparative analysis of various load balancing techniques employed in cloud computing. It explores traditional, heuristic, and intelligent approaches, categorizing them into a detailed taxonomy based on parameters such as decision-making strategies, scalability, adaptability, and energy efficiency. The study also identifies gaps and challenges in current methods and proposes potential future research directions focused on improving real-time adaptability, energy-awareness, and integration with edge computing and AI technologies. The findings contribute to a deeper understanding of load balancing mechanisms and pave the way for designing more resilient and intelligent cloud infrastructure.