Modern artificial intelligence and high-performance computing systems face critical bottlenecks in inter-chip communication as computational capabilities continue to advance beyond the limits of traditional copper-based interconnects and board-edge optical modules. Co-packaged optics emerges as a transformative solution by integrating photonic components directly within processor packages, dramatically reducing electrical path lengths and enabling unprecedented bandwidth densities while lowering energy consumption per transmitted bit. This article presents a comprehensive hybrid integration architecture that combines two-and-a-half-dimensional and three-dimensional packaging techniques to co-locate photonic chiplets with compute dies on silicon interposers. The article leverages micro-ring resonator-based wavelength division multiplexing on silicon photonic platforms to achieve high aggregate throughput while maintaining compatibility with advanced logic manufacturing processes. Through detailed co-design of packaging structures, electrical-optical interfaces, thermal management systems, and control algorithms, the architecture addresses key technical challenges that have historically impeded photonic integration efforts. Validation through multi-level simulations and physical prototypes demonstrates feasibility for rack-scale optical connectivity meeting the demanding requirements of distributed machine learning workloads. The article examines critical parameters that affect the timing of commercial adoption, such as manufacturing yield, serviceability, the need for standardization, and economic trade-offs. Results indicate that hybrid co-packaged optics architectures provide viable pathways for sustaining bandwidth scaling in next-generation data center fabrics serving artificial intelligence applications.

Zenodo DOI:- https://doi.org/10.5281/zenodo.18875724

In the current work, the researchers examine 20 years (2004-2024) of literature on Industry 4.0 and IoT, visualizing intellectual organisations, knowledge sharing, and changes in themes. Based on data collected from 6,000+ articles scraped from Scopus and processed with VOSviewer, Biblioshiny, and Python, it answers three research questions: the creation of Industry 4.0 and IoT scholarship, market forces and market uptake, and convergences in technologies. Results indicate soaring growth in publications starting in 2016, with significant contributions from China, the US, Germany, India, and the UK. The study identifies a gap in the scholarly literature and in the uptake of IoT, with high implementation evident in the manufacturing and logistics sectors, whereas in the healthcare, agriculture, and building sectors, implementation challenges persist. It includes suggestions for what researchers should consider in the future, including socio-technical integration, cybersecurity, and sustainable digital ecosystems.

The current financial regulatory frameworks, specifically the ‘Fundamental Review of the Trading Book’ (FRTB), require almost real-time disclosure of Expected Shortfall and Value at Risk measures of heterogeneous portfolios of equities, fixed-income securities, and complex over-the-counter derivatives. Switching from batch-based reporting to continuous intraday risk reporting poses significant computational challenges, especially when dealing with portfolios of over 100,000 positions with nonlinear sensitivities. This article introduces the Decentralized Risk Computation Grid (DRCG), a cloud-native design that uses stateful orchestration and sensitivity-aware sharding to decentralize portfolio decomposition to elastic worker clusters. In contrast to the classical stateless parallel models, the DRCG uses a warm-cache worker pattern that avoids unnecessary market data loading cycles, and tail latency is significantly lower, and yet deterministic recovery is achieved in the event of node failures. The framework is horizontally scalable, with sensitivity-based portfolio decomposition formalized mathematically and Byzantine fault-tolerance principles, without affecting audit compliance or data consistency. Empirical confirmation in distributed settings shows that state-conscious orchestration offers the resilience required in systemic risk surveillance in times of high market volatility and stress.

Zenodo DOI:- https://doi.org/10.5281/zenodo.18875781

Digital twin technology has emerged as one of the most transformative paradigms in modern cyber-physical systems, enabling the creation of dynamic virtual representations of physical assets, environments, and processes. When integrated with next-generation communication infrastructures and edge intelligence, digital twins enable real-time monitoring, predictive analytics, and autonomous decision-making across domains such as manufacturing, aerospace, smart cities, and unmanned aerial systems. The convergence of digital twin architectures with edge computing and artificial intelligence is particularly relevant in environments requiring ultra-low latency and high-fidelity simulation, including autonomous vehicles, industrial automation, and distributed drone systems. However, the deployment of real-time digital twin platforms introduces significant challenges related to interoperability, security, privacy, and cross-domain standardization. Emerging communication networks, including 5G and anticipated 6G systems, promise to address these challenges by enabling scalable, high-bandwidth connectivity and distributed intelligence.

This study investigates the evolving role of secure edge intelligence in enabling scalable digital twin deployments within next-generation communication ecosystems. Drawing from a comprehensive analysis of prior research across smart manufacturing, autonomous aerial systems, industrial automation, and edge computing architectures, the research synthesizes theoretical frameworks that explain how distributed intelligence can support real-time synchronization between physical systems and their digital counterparts. Particular attention is devoted to digital twin implementations in autonomous vehicles, unmanned aerial vehicles, smart grids, and industrial production environments. The analysis explores the integration of machine learning models, distributed edge computing platforms, and next-generation wireless technologies to support digital twin operations at scale.

The study further examines security and privacy implications associated with edge-enabled digital twin systems, highlighting vulnerabilities arising from distributed data flows, device heterogeneity, and cyber-physical integration. Through an extensive theoretical analysis of contemporary research, the article proposes a conceptual framework that integrates edge intelligence, cross-domain standardization, and secure communication protocols for digital twin environments. The findings suggest that the future of digital twin ecosystems will rely heavily on intelligent edge architectures capable of supporting autonomous decision-making while ensuring trustworthiness, resilience, and interoperability across complex technological infrastructures.