This study investigates traversable wormhole solutions within the framework of Ricci inverse gravity incorporating the matter trace term 𝒯 . By employing the Karmarkar embedding condition in a static, spherically symmetric spacetime, exact wormhole geometries are constructed and analyzed through embedding diagrams. The role of the anti-curvature scalar and matter trace contributions is examined in reducing the requirement of exotic matter. The obtained solutions indicate that the energy conditions are partially satisfied or only mildly violated, which is consistent with realistic wormhole configurations. Furthermore, stability analysis confirms that the solutions remain in equilibrium under the adopted model parameters. These findings suggest that Ricci inverse gravity with a trace term provides a viable framework for modeling traversable wormholes with reduced dependence on exotic matter.
Forecasting the evolution of research topics is essential for identifying emerging trends in rapidly evolving scientific domains. In this study, scholarly themes are modeled as temporal keyword co-occurrence networks to capture their structural and temporal dynamics. A multi-domain evaluation is conducted using three graphs derived from the DBLP Citation Network V14: the Cyber Security Research Graph (CSRG), the Artificial Intelligence Research Graph (AIRG), and the Social Network Research Graph (SNRG), each represented as yearly snapshots (2001–2022). The datasets exhibit high temporal novelty, where most keyword associations emerge dynamically over time, motivating the use of inductive models. A GraphSAGE-based framework is employed for dynamic link prediction, evaluating multiple aggregation functions and neighborhood sampling strategies under a temporal inductive setting. Experimental results (2018–2022) demonstrate strong predictive performance across all datasets. Importantly, the findings reveal that no single configuration is universally optimal; instead, model performance is strongly influenced by graph structural properties such as size, density, and local connectivity. These results highlight the importance of structure-aware model design and demonstrate the effectiveness of inductive graph neural networks for forecasting evolving keyword relationships in dynamic scholarly environments.
Agentic Artificial Intelligence (AI) marks a shift from traditional AI systems that simply generate responses to autonomous systems that can independently plan to achieve goals with minimal human intervention. These models can do much more than just respond to prompts as they can observe, adapt, coordinate with other agents and even refine their own outputs over time. The present study draws insights from fifty-one recent studies to understand how agentic AI is being built and used now a days. Agentic AI systems appear in the domains of healthcare, digital twin architectures, educational platforms, e-commerce applications, cybersecurity systems and large-scale network management systems. They often improve efficiency, reduce manual workload, and help in making more informed decisions. However, this increased autonomy also raises several concerns. This is because autonomous systems that can act without human intervention must be reliable, explainable, secure and aligned with human expectations. Many implementations of such systems are still in early stages, lacking standard evaluation methods and are facing challenges such as data access, ethical responsibility, and coordination among multiple agents. For clearer understanding, this study outlines a taxonomy of agentic AI and describes its current application domains, discusses common architectures and techniques, and highlights its limitations and future directions. The results of this study suggest that progress in governance, multi-modal reasoning and scalable coordination will be central to advancing safe and useful agentic AI systems.
Time series analysis involves examining data collected at successive time intervals to uncover patterns, trends, and dependencies for predictive purposes across various domains. Challenges include managing non-stationarity, seasonal variations, and noisy data, with critical tasks like addressing missing timestamps or values that affect model accuracy. Specific applications, such as power consumption monitoring and stock market analysis, require adept handling of irregular patterns and volatility prediction amidst external influences. Selecting optimal techniques for handling missing data and choosing appropriate forecasting methods, each with unique hyperparameters, poses significant challenges. This paper introduces Preplysis, a framework designed to automate time series analysis, employ various cleaning techniques, tune forecasting model hyperparameters. It is a recommendation system for technique selection. This comprehensive approach aims to enhance efficiency, accuracy, and accessibility in time series analysis, for informed decision-making and predictive modeling. Preplysis achieved exceptional performance with an RMSE of 0.03 in univariate stock prediction, surpassing benchmarks set by hybrid LSTM and RNN models and in multivariate power consumption prediction, Preplysis recommended GRU with exponential smoothing, achieving an improved RMSE of 0.639 compared to an average of 0.83 across 12 models tested, highlighting its efficacy in optimizing model selection for superior predictive accuracy.
The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), and zero-knowledge proofs (ZKPs). By eliminating reliance on centralized authorities, our system enhances data privacy, reducing personally identifiable information (PII) disclosure by 80% while ensuring compliance with AML and GDPR regulations. The integration of zk-SNARKs enables trustless identity verification with an average proof generation time of 12.5 seconds, significantly reducing the 3-5 day verification period required by traditional systems. Smart contract-based KYC automation eliminates intermediaries, cutting compliance costs by 40% and reducing fraud risk by 60%. Through comparative analysis, we highlight that decentralized KYC improves security, cost-effectiveness, and scalability compared to traditional models. Performance evaluation confirms that transaction throughput remains within acceptable blockchain limits, with gas costs stabilized at 35,000-55,000 Gwei per verification request. Despite challenges in regulatory adaptation and zk-SNARK scalability, the proposed model demonstrates the feasibility of Web3-driven identity management for trustless, privacy-preserving, and compliant financial ecosystems.
