Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed workload execution. Such modeling is essential for pre-deployment system-level optimizations (e.g., parallelization strategies) and hardware design-space explorations. While recent efforts have proposed collecting execution traces from real systems, access to large-scale infrastructure remains limited to major cloud providers. Moreover, traces capturing execution on a specific platform cannot be easily adapted to study alternate software and/or hardware configurations, especially at scale. We introduce STAGE, a framework that synthesizes high-fidelity execution graphs to accurately model distributed AI workloads (including LLMs and MoEs). STAGE supports a comprehensive set of parallelization strategies, allowing users to systematically explore a wide spectrum of model architectures and system configurations. STAGE demonstrates its scalability by synthesizing high-fidelity LLM traces spanning over 128K GPUs, while preserving tensorlevel accuracy in compute, memory, and communication. STAGE is publicy available at https://github.com/astra-sim/stage
Smart electronic devices are emerging as a foundational component of next-generation technological systems, enabling intelligent sensing, communication, computation, automation, and real-time decision-making across diverse application environments. The rapid convergence of advanced semiconductor technologies, embedded systems, artificial intelligence, Internet of Things (IoT), flexible electronics, wireless communication, and energy-efficient circuit architectures has transformed conventional electronic devices into highly connected and adaptive platforms. This research paper examines the evolving landscape of smart electronic devices and their potential to support future applications in healthcare, smart homes, intelligent transportation, industrial automation, agriculture, environmental monitoring, wearable technology, and infrastructure management. Particular attention is given to devices equipped with integrated sensors, low-power processors, wireless connectivity, and intelligent data-processing capabilities that allow them to respond dynamically to changing operating conditions. The study explores how miniaturization, improved materials, edge computing, energy harvesting, flexible substrates, and advanced fabrication techniques contribute to enhanced device functionality, portability, reliability, and sustainability. Smart electronic devices can collect physical and environmental information, process data locally or through connected platforms, and generate actionable responses with reduced dependence on conventional computing infrastructure. Their integration with artificial intelligence and machine learning further enables predictive analysis, personalized services, anomaly detection, and autonomous operation. At the same time, the widespread deployment of such devices introduces challenges associated with power consumption, data security, privacy, interoperability, hardware reliability, scalability, electronic waste, and ethical use of intelligent technologies. The paper therefore considers both the technological opportunities and practical limitations associated with developing and deploying next-generation smart electronic systems. Emphasis is placed on the need for multidisciplinary design approaches that combine electronics engineering, computing, communication technologies, materials science, and application-specific requirements. The research highlights that future smart electronic devices will increasingly depend on seamless integration between sensing, processing, communication, and intelligent control while maintaining compact form factors and efficient resource utilization. By examining these developments collectively, the study provides a broad perspective on how smart electronics can contribute to more responsive, connected, sustainable, and human-centered technological ecosystems and identifies important directions for continued research and innovation in next-generation electronic applications.
This paper introduces a novel l-shaped slots self-nonuplexing antenna in a nested substrate integrated waveguide cavity for LTE 5537.5/WiFi/WiMAX (5.21-5.50 GHz), WLAN/ISM (5.71-5.89 GHz), satellite communication (6-6.27 GHz), super ext. C-band (6.5-6.74 GHz), space communication (6.91-7.23 GHz), LTE/LTE advanced (7.45-7.69 GHz), meteorological satellite (7.90-8.12 GHz), ITU (8.21-8.79 GHz), and weather RADAR (8.75-9.33 GHz) applications. The antenna comprises eight different l-shaped slots in four subcavities and one square ring slot in the nested cavity. The inset feed of the 50 line has been used to excite the l-shaped slots, and the ring slot is excited by a coaxial feed line. Each slot radiates at nine different frequencies, such as 5.35, 5.8, 6.15, 6.6, 7.1, 7.65, 8.05, 8.65, and 9.2 GHz, respectively. The corresponding gains are 5.56, 5.84, 5.82, 6.32, 6.61, 7.50, 6.67, 6.77, and 7.08 dBi at the respective resonant frequencies. This antenna offers better than 20 dB measured isolation between any two ports and independent tenability features across any band. This antenna is appropriate for high-integration portable devices because of its planar design and its ability to eliminate intermodal electromagnetic interference. This antenna ends the requirement of RF switches in multiband transceivers and Internet of Things-based applications.
Dynamic pricing is commonly used to regulate congestion in shared service systems. This paper is motivated by the fact that when heterogeneaous user groups (in terms of price responsiveness) are present, conventional monotonic pricing can lead to unfair outcomes by disproportionately excluding price-elastic users, particularly under high or uncertain demand. The paper's contributions are twofold. First, we show that when fairness is imposed as a hard state constraint, the optimal (revenue maximizing) pricing policy is generally non-monotonic in demand. This structural result departs fundamentally from standard surge pricing rules and reveals that price reduction under heavy load may be necessary to maintain equitable access. Second, we address the problem that price elasticity among heterogeneous users is unobservable. To solve it, we develop a robust dynamic pricing and admission control framework that enforces resource capacity and fairness constraints for all user type distributions consistent with aggregate measurements. By integrating integral High Order Control Barrier Functions (iHOCBFs) with a worst case robust optimization framework, we obtain a controller that guarantees forward invariance of safety and fairness constraints while optimizing revenue. Numerical experiments demonstrate improved fairness and revenue performance relative to monotonic surge pricing policies.
As Low Power and Lossy Networks (LLNs) become more prevalent, new forms of interaction are required. It was suggested that IPv6 Routing Protocols for Low-Power and Lossy Networks (RPL) be used to simplify connectivity among such low-powered gadgets. The main aspect of the proposed work is to achieve better routing performance and enhance the routing quality with the utilization of various constraints in the objective function. While building the RPL network, some of the parameters are optimized by Artificial Gorilla Troops with the African Vultures Optimization Algorithm (HAGT-AVOA) to enhance the routing performance. Thus, the novel network is built with objective measures such as energy, Expected Transmission Count (ETX), throughput, Link Quality Indicator (LQI), Received Signal Strength (RSSI), and hop count. Thus, the designed RPL network aims to satisfy the following factors, such as enhancing the more packet delivery, link quality, energy consumption, and managing the power. Hence, the performance is validated, and its results are compared with existing techniques. Based on the validation phase, the Packet Delivery Ratio (PDR) of the designed model is 96.87, which is better than traditional models. Thus, the findings demonstrate that it provides better routing performance in the RPL network.
