For many emerging digital economies in the world, the decentralization of digital services offers a way to incentivize the development and operations of the Web3 infrastructures for these services by the private sector while avoiding the concentration of power in the hands of a few. Indonesia, as one of the fastest-growing economies in the global south, can benefit from utilizing Web3 infrastructures as a form of the Digital Public Infrastructure (DPI) concept. Key to implementing the DPI is the standardization of open protocols and APIs in the architecture, enabling various market players to develop basic services with a high degree of interoperability. The tokenization of real-world assets built on decentralized Web3 infrastructures could be a promising new sub-sector of Indonesia's economy, enabling local communities to actively participate in the coding, development, and running of the nodes that constitute these new Web3 infrastructures.

The integration of High Voltage Direct Current (HVDC) systems and Artificial Intelligence (AI) has emerged as a pivotal advancement in modern power systems. As HVDC technology facilitates efficient long-distance power transmission and renewable energy integration, ensuring robust protection mechanisms is critical to maintaining grid reliability and stability. This paper explores the role of AI in enhancing the protection of HVDC systems, focusing on fault detection, diagnosis, and response mechanisms. By analyzing AI-driven methods such as machine learning, deep learning, and optimization algorithms, we underscore their potential to address challenges like fault discrimination, response time, and system adaptability. The study also highlights the significance of integrating AI to handle the complexities introduced by renewable energy sources and increasing grid interconnections. Challenges such as data dependency, computational requirements, and safety considerations are addressed alongside future directions for AI-enabled HVDC protection systems.

Integration of Control Systems with Artificial Intelligence Control systems, the backbone of automation across industries, are increasingly being integrated with artificial intelligence (AI) to enhance efficiency, adaptability, and decision-making. This amalgamation addresses the growing demand for intelligent automation, enabling systems to manage complexity, improve accuracy, and adapt to dynamic environments. AI-powered control systems are redefining traditional methodologies by embedding machine learning, neural networks, and data-driven optimization into their architectures. This essay explores the principles, advancements, challenges, and applications of integrating control systems with AI[1-32].

The integration of Artificial Intelligence (AI) into various sectors has revolutionized the way we live, work, and interact with technology. While AI offers unprecedented opportunities for innovation and efficiency, it also presents significant challenges related to control and protection. This paper explores the importance of implementing robust control mechanisms and protection strategies in the integration of AI. It delves into the potential risks of uncontrolled AI deployment, including ethical dilemmas, security vulnerabilities, and societal impacts. The discussion underscores the necessity for a balanced approach that harnesses AI's benefits while mitigating its risks through effective governance, ethical frameworks, and technological safeguards.

With the rise of Industry 4.0 and intelligent manufacturing, real-time anomaly detection and predictive maintenance have become critical components to ensure operational efficiency and product quality. This paper explores convolutional neural networks (CNNs) for predictive maintenance within the context of a milling process. We developed and trained a CNN model to predict tool wear under varying operational conditions using the Milling Data Set from UC Berkeley's BEST Lab, which captures sensor data such as vibration and acoustic emission during milling operations. The performance of the CNN model was evaluated in terms of Mean Squared Error (MSE) and R 2 , demonstrating its capability to detect wear patterns and predict potential tool failures with an MSE of 0.056. The results were benchmarked against traditional machine learning models, such as Random Forest, Linear Regression, and XGBoost. Insights into feature importance were gained by analyzing the contributions of vibration and acoustic emission data to tool wear prediction. These findings suggest CNNs, when optimized for sensor data, can be effectively integrated into smart manufacturing systems, offering real-time predictions to reduce machine downtime and extend tool life.

Domino logic circuits inherent speed and area efficiency benefits make them essential for high-speed and low-power digital applications. However, particularly in contemporary, massively scaled technologies, traditional domino logic architectures typically encounter issues such as excessive power waste, higher delay, and lower noise immunity. To overcome these constraints, we suggest two to three improved circuit designs that offer new designs on traditional domino logic. To meet the demands of diverse high-performance applications, each design concentrates on certain performance improvements, such as lowering power consumption, enhancing noise margins, or optimizing delay characteristics. The newly designed domino circuits presented in this work are carefully examined and contrasted with traditional domino logic in several performance parameters. Important features including noise margin, area efficiency, power-delay product (PDP), and process variability tolerance are all included in a thorough comparison table. According to simulation data in ideal conditions, the suggested circuits perform better than conventional domino designs in controlled environments, showing notable gains in speed, delay, and energy while preserving a high level of operational reliability. The results of our study demonstrate how domino logic circuits can improve the performance of high-speed digital systems. The proposed designs give practical solutions to some of the fundamental difficulties we face in existing domino logic such as power and delay.

Efficient processing of large data volumes at low power is a critical challenge in modern computing systems. Spiking Neural Networks (SNNs), inspired by biological spikebased communication, offer a promising solution through energyefficient neuromorphic architectures. Unlike traditional neural networks, SNNs utilize spike-timing-dependent plasticity (STDP) learning, reducing computational complexity and power consumption. Prior implementations often rely on external computational platforms or memristive devices, which introduce fabrication and integration challenges. This work presents the design and implementation of an on-chip SNN architecture using SCL 180nm CMOS technology. The architecture features Leaky Integrate-and-Fire (LIF) neurons and a memristive synapse circuit that incorporates STDP learning for efficient pattern recognition. Optimizations, such as removing current mirrors and reset logic in the neuron circuit, minimize area and power requirements. A Verilog-A designed summing amplifier processes synaptic outputs, while a Winner-Takes-All mechanism ensures accurate classification of input patterns, including characters and directional signs. The single-layer network demonstrates lowlatency performance and high efficiency in recognizing simple patterns. Simulation results validate the robustness of the architecture, paving the way for future enhancements to include hidden layers for handling complex patterns and integrating advanced technologies to expand the scope of neuromorphic computing applications.

Nonlinear constrained optimization is really at the heart of computational mathematics, ranging from resource allocation to material stress analysis. The contribution of this paper is a sophisticated optimization framework that harnesses trust-region methods, dynamic regularization, and thorough sensitivity analysis to solve high-dimensional nonlinear problems. The proposed method minimizes an objective function comprising quadratic terms, linear penalties, and sparsityinducing regularization, subject to stress and budget constraints. The framework achieved precise convergence in 46 iterations and 756 function evaluations, satisfying the gtol termination condition with optimality of 4.43 × 10−9 and zero constraint violations, all within 0.36 seconds. To provide insight into the optimization process, the following four visualizations are generated: a Convergence Plot showing exponential convergence of the objective function that converges and stabilizes after approximately 100 iterations; a Gradient Magnitude Plot showing exponential decay in the gradient norm, confirming smooth and efficient convergence; a Hessian Matrix Heatmap showing the structure of the dependencies and curvature around the optimal solution to assist in sensitivity interpretation; and a Variable Contribution Bar Plot, which quantifies the relative contribution from each decision variable to the objective function to identify the dominant contributors. This work showcases the robustness, efficiency, and scalability of the proposed framework. The visualizations not only validate the results of the optimization but further give insight into the solution space. These results open up perspectives toward the extension of such a framework to more complex dynamic constraints and challenging real-world datasets, bringing important contributions to computational mathematics and applied optimization.

This paper presents a detailed examination of Group Key Establishment methods, pivotal in securing group communication over public networks. We classify Group Key Establishment into three key categories: classical, quantum-safe, and quantum-based, each tailored to address distinct challenges in key management, particularly under the constraints of member dynamics and resource efficiency. Highlighting the critical influence of emerging quantum technologies, our study emphasizes the growing importance of quantum-resistant approaches to safeguard group communications against quantum threats. This paper traverses the evolution of Group Key Establishment protocols, shedding light on the shifting cryptographic paradigms and future prospects in the era of quantum advancement.

In this paper, we present analytic solutions for explicit Model Predictive Control (EMPC) applied to the current control of Surface-mounted Permanent Magnet Synchronous Motor (SPMSM) drives. A key challenge in applying MPC to motor current control is solving the constrained optimization problem in real time. To address this, we develop an analytic procedure based on planar geometric projections to solve the problem efficiently. The correctness of our method is validated through comparisons with standard numerical solvers. Experimental results show that our algorithm can be implemented on low-cost hardware with rapid execution, supporting long prediction and control horizons while delivering fast dynamic response. These advantages make it suitable for both high-speed servo systems and budget-sensitive applications.

Rapidly identifying anomalous events on freeways can significantly improve emergency response time and reduce congestion. However, the unpredictable nature of these incidents, along with the limitations of current monitoring systems, makes accurate and timely detection challenging. In this study, we propose a Cycle-Consistent Bidirectional Graph Generative Adversarial Network (CCB-GraphGAN) model to enhance lanelevel traffic anomaly detection on freeways. By treating freeway traffic information as graph-structured data, our model captures the joint distribution of regular traffic patterns and their underlying latent representations through an adversarial process. It also incorporates cycle consistency constraints to effectively reconstruct both normal traffic data and its latent features during training. During the inference phase, we employ an autoencoder-based anomaly detection approach to identify lanelevel traffic anomalies. We evaluate the performance of our method through comparative and case study analyses using the recently released Freeway Traffic Anomalous Event Detection (FT-AED) dataset. The results indicate that our method outperforms all baseline models in detecting freeway anomalous events early. Notably, it achieves a lower percentage of missed crashes compared to baseline models. With a false positive rate (FPR) of only 1%, our method reduces detection delay by identifying crashes an average of 5 minutes earlier than their official report time. Additionally, an analysis of real-world crash events further confirms our method's effectiveness in accurately identifying freeway anomalies at an early stage.

Neural Token Compression (NTC) introduces a dynamic tokenization strategy that adjusts token representations based on semantic and structural dependencies, aiming to enhance the efficiency and scalability of Large Language Models (LLMs). Traditional tokenization methods often result in fixed token sets, which may not optimally capture the complexities of diverse linguistic patterns. NTC addresses this limitation by enabling adaptive token merging, thereby reducing the number of tokens processed during training and inference. This reduction leads to decreased computational overhead and memory usage, facilitating the deployment of LLMs in resource-constrained environments. The implementation of NTC involves a mathematical framework that leverages semantic similarity measures and structural dependencies to inform token merging decisions. Experimental evaluations demonstrate that NTC achieves significant improvements in perplexity and compression ratios compared to baseline tokenization approaches. Qualitative analyses further reveal that NTC maintains the consistency and coherence of generated text, indicating its potential for various downstream tasks. However, certain limitations, such as variability in token sequences and the complexity of capturing linguistic dependencies, warrant further research. Future directions include refining the NTC framework and exploring its applicability across diverse languages and domains. The integration of NTC into LLM architectures signifies a meaningful advancement in natural language processing, offering a promising avenue for optimizing tokenization processes and improving the performance of largescale language models.

One of the main goals of wireless sensor networks is to permit the involved nodes to communicate with low energy budgets, as they are typically battery-powered. When such networks are employed in industrial scenarios, constraints about latency may have a significant role, too. The TSCH mechanism, and more in general TDMA schemes, rely on traffic scheduling, and consequently they can feature low power consumption and more predictable latency. Some recent proposals like PRIL-M enable further consistent energy savings, but unfortunately they cause at the same time a dramatic increase in latency. This work presents an extension of PRIL-M, we named PRIL-ML, that achieves a significantly shorter latency in exchange for a slight increase in power consumption. Its operating principles are first illustrated, then some approximate equations are provided for assessing analytically the improvements it achieves, starting from simulation results obtained for both standard TSCH and the original PRIL-M technique.

Abnormal traffic patterns caused by extreme events have the potential to disrupt traffic flow on large regions of urban road networks. Timely and reliable detection of such disruptions is crucial for effective traffic management. However, existing methods for detecting extreme traffic events are unable to simultaneously identify disruptions at both network and local levels. Moreover, most existing methods cannot handle incomplete traffic data, which can be an essential challenge during extreme events. In this study, we address these limitations by creating a novel method for traffic anomaly detection using a multitask sequence-to-sequence learning process. Specifically, we propose a Self-Imputing Deep Multitask Sequence (SI-DMSeq) model that can simultaneously reconstruct sequential traffic data and provide one-step-ahead predictions. This model also integrates an unsupervised data imputation strategy to enable training with incomplete data. During inference, the proposed model adopts an autoencoder-based anomaly detection approach to identify large-scale disruptions at both network and local levels. Additionally, it utilizes one-step-ahead predictions to reliably impute missing data under typical traffic conditions. To evaluate the performance of our proposed method, we applied it to the Manhattan road network in New York City using historical traffic data. The results indicated that the proposed method accurately detects disruptions caused by Hurricane Sandy in 2012 at both network and local levels and efficiently imputes missing traffic data under typical conditions.

Recently, Photo-Voltaic energy has been a subject of intensive research for green and sustainable energies. The boost converter is the main backbone of this conversion. Conventionally, coupled inductors have been employed to increase and extend the output voltage of this converter. This work introduces a novel converter using a conventional boost combined with a coupled inductor. The proposed topology responds to a wider range of overcasts and functions as an efficient harvester with an improved output current in Continuous Current Mode (CCM). Furthermore, it extends the functionality of the harvester by improving the efficiency and output voltage concurrently. A prototype was built and successfully tested to verify the proposed topology. An efficiency of 91% is retained during a partial overcast under matched condition, with a single stage converter.

This study introduces a novel multi-sensor system and a fusion strategy for continuous ice thickness detection. To address the limitations of traditional labor-intensive methods, which have lower resolution and are affected by environmental dependencies, we developed an enhanced air-coupled Ground Penetrating Radar (GPR) system integrated with temperature sensors and a transformer-based algorithm for anomaly compensation. It offers a reliable method for mapping ice structures by leveraging the dielectric contrast between ice and water. However, real-time monitoring during rapid temperature fluctuations remains challenging. The proposed method leverages Dynamic Time Warping (DTW) to identify significant signal changes, while the transformer network offers a physical information-supported prediction for ice thickness using environmental parameters. Results demonstrate that the proposed method improves detection accuracy, providing early warning capabilities and enhancing ice thickness tracking in dynamic conditions. The system real-time performance is validated through extensive field data from the Yellow River, showcasing the effectiveness of this integrative framework in challenging monitoring scenarios.

The field of wireless communications has traditionally been defined by what seems like unending exponential traffic growth. History suggests this trend is unlikely to continue in perpetuity, at least with the current set of applications, with recent evidence pointing to moderating traffic growth. In this article we evaluate the implications of the peak smartphone era for those designing wireless networks, within the context of the next-generation of wireless broadband technologies. Firstly, three potential future demand scenarios are identified ranging from a return to exponential traffic growth (optimistic), through to continued moderation in growth (realistic), and even a scenario of declining traffic (pessimistic). Secondly, we compare the emerging properties of the 6th generation of cellular technology ("6G") envisioned by IMT2030, and two new Wi-Fi standards, including IEEE 802.11be ("Wi-Fi 7") and IEEE 802.11bn ("Wi-Fi 8"). Finally, an alternative vision for the future of wireless broadband is proposed focusing on enhanced coverage, reduced deployment costs, and improved energy efficiency. Four key recommendations include (i) neutral hosts for superior indoor coverage, (ii) ensuring spectrum sharing and intelligent handover/roaming integration between cellular, Wi-Fi and Non-Terrestrial Networks (NTNs), (iii) strong support for infrastructure sharing and national roaming in rural and remote areas, and (iv) efficient (re)organization of existing spectrum allocations.

The rapid development of Electric Vertical Take Off and Landing (eVTOL) aircraft is transforming urban aviation, introducing new challenges for airworthiness certification an essential process to ensure operational safety and regulatory compliance. This paper presents a Type Certification framework for eVTOL aircraft, leveraging the Arcadia Model-Based Systems Engineering (MBSE) approach through the Capella tool. This framework replaces traditional document-based methods with a model-driven approach that integrates regulatory standards and Means of Compliance (MoCs) into a unified system model. Additionally, automation is introduced through Large Language Models (LLMs) to dynamically predict and validate MoCs from textual requirements, streamlining checklist generation and enabling rapid adaptation to evolving regulations. While the framework improves efficiency, it complements rather than replaces expert supervision, emphasizing the importance of human oversight in complex regulatory contexts. The proposed framework is scalable and adaptable, applicable not only to various eVTOL platforms but also to other emerging aircraft types. Findings from case studies and expert evaluations demonstrate a significant reduction in workload, enabling faster certification while maintaining accuracy and regulatory alignment. This study offers a replicable, efficient model for certification that fosters the adoption of emerging technologies while ensuring high safety standards and supporting sustainable growth in urban air mobility.