Search Results (132)

Deployment of Unmanned Aerial Vehicles (UAVs) continues to expand rapidly across a wide range of applications, including environmental monitoring, precision agriculture, and disaster response. Despite their increasing ubiquity, UAVs remain inherently vulnerable to security threats due to resource-constrained hardware, energy limitations, and reliance on open wireless communication channels. These factors render traditional cryptographic solutions impractical, thereby necessitating the development of lightweight, UAV-specific security mechanisms. This review article presents a comprehensive analysis of lightweight encryption techniques and key management strategies designed for energy-efficient and secure UAV communication. Special emphasis is placed on recent cryptographic advancements, including the adoption of the ASCON family of ciphers and the emergence of post-quantum algorithms that can secure UAV networks against future quantum threats. Key management techniques such as blockchain-based decentralized key exchange, Physical Unclonable Function (PUF)-based authentication, and hierarchical clustering schemes are evaluated for their performance and scalability. To ensure comprehensive protection, this review introduces a multilayer security framework addressing vulnerabilities from the physical to the application layer. Comparative analysis of lightweight cryptographic algorithms and multiple key distribution approaches is conducted based on energy consumption, latency, memory usage, and deployment feasibility in dynamic aerial environments. Unlike design- or implementation-focused studies, this work synthesizes existing literature across six interconnected security dimensions to provide an integrative foundation. Our review also identifies key research challenges, including secure and efficient rekeying during flight, resilience to cross-layer attacks, and the need for standardized frameworks supporting post-quantum cryptography in UAV swarms. By highlighting current advancements and research gaps, this study aims to guide future efforts in developing secure communication architectures tailored to the unique operational constraints of UAV networks. Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

In the evolving digital landscape, the pervasive influence of artificial intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human imperfections. Such biases prompt rigorous contemplation on matters of fairness, equity, and societal ramifications, and penetrate the foundational essence of AI. Within this intricate context, the present work ventures into novel domains by examining the potential of quantum computing as a viable remedy for bias in artificial intelligence. The conceptual framework of the quantum sentinel is presented—an innovative approach that employs quantum principles for the detection and scrutiny of biases in AI algorithms. Furthermore, the study poses and investigates the question of whether the integration of advanced quantum computing to address AI bias is seen as an excessive measure or a requisite advancement commensurate with the intricacy of the issue. By intertwining quantum mechanics, AI bias, and the philosophical considerations they induce, this research fosters a discourse on the journey toward ethical AI, thus establishing a foundation for an ethically conscious and balanced digital environment. We also show that the quantum Zeno effect can protect SVM hyperplanes from bias through targeted simulations. Full article

Traditional brain emulation approaches often rely on classical computational models that inadequately capture the stochastic, nonlinear, and potentially coherent features of biological neural systems. In this position paper, we introduce NeuroQ a quantum-inspired framework grounded in stochastic mechanics, particularly Nelson’s formulation. By reformulating the FitzHugh–Nagumo neuron model with structured noise, we derive a Schrödinger-like equation that encodes membrane dynamics in a quantum-like formalism. This formulation enables the use of quantum simulation strategies—including Hamiltonian encoding, variational eigensolvers, and continuous-variable models—for neural emulation. We outline a conceptual roadmap for implementing NeuroQ on near-term quantum platforms and discuss its broader implications for neuromorphic quantum hardware, artificial consciousness, and time-symmetric cognitive architectures. Rather than demonstrating a working prototype, this work aims to establish a coherent theoretical foundation for future research in quantum brain emulation. Full article

Rovelli’s relational interpretation of quantum mechanics tells us that the description of a system in the formalism of quantum mechanics is not an absolute but is relative to the observer itself. The interpretation goes further and proposes a set of axioms. In standard non-relational language, one of them states that an observer can only retrieve a finite amount information from a system by means of measurement. Our contribution starts with the observation that quantum mechanics, i.e., quantum field theory (QFT) in dimension 1, radically differs from QFT in higher dimensions. In higher dimensions, boundary data (or initial data) cannot be characterized by finitely many measurements. This calls for a notion of measuring scale, which we provide. At a given measuring scale, the observer has partial information about the system. Our notion of measuring scale generalizes the one implicitly used in Wilsonian QFT. At each measuring scale, there are effective theories, which may be corrected, and if the theory turns out to be renormalizable, the mentioned corrections converge to determine a completely corrected (or renormalized) theory at the given measuring scale. The notion of a measuring scale is the cornerstone of Wilsonian QFT; this notion tells us that we are not describing a system from an absolute perspective. An effective theory at that scale describes the system with respect to the observer, which may retrieve information from the system by means of measurement in a specific way determined by our notion of measuring scale. We claim that a relational interpretation of quantum physics for spacetimes of dimensions greater than 1 is Wilsonian. Full article

The general framework of Entropic Dynamics (ED) is used to construct non-relativistic models of relational Quantum Mechanics from well-known inference principles—probability, entropy and information geometry. Although only partially relational—the absolute structures of simultaneity and Euclidean geometry are still retained—these models provide a useful testing ground for ideas that will prove useful in the context of more realistic relativistic theories. The fact that in ED the positions of particles have definite values, just as in classical mechanics, has allowed us to adapt to the quantum case some intuitions from Barbour and Bertotti’s classical framework. Here, however, we propose a new measure of the mismatch between successive states that is adapted to the information metric and the symplectic structures of the quantum phase space. We make explicit that ED is temporally relational and we construct non-relativistic quantum models that are spatially relational with respect to rigid translations and rotations. The ED approach settles the longstanding question of what form the constraints of a classical theory should take after quantization: the quantum constraints that express relationality are to be imposed on expectation values. To highlight the potential impact of these developments, the non-relativistic quantum model is parametrized into a generally covariant form and we show that the ED approach evades the analogue of what in quantum gravity has been called the problem of time. Full article

Key exchange mechanisms are foundational to secure communication, yet traditional methods face challenges from quantum computing. The Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) is a post-quantum cryptographic key exchange protocol with unknown successful quantum vulnerabilities. This study evaluates the ML-KEM using experimental benchmarks. We implement the ML-KEM in Python for clarity and in C++ for performance, demonstrating the latter’s substantial performance improvements. The C++ implementation achieves microsecond-level execution times for key generation, encapsulation, and decapsulation. Python, while slower, provides a user-friendly introduction to the ML-KEM’s operation. Moreover, our Python benchmark confirmed that the ML-KEM consistently outperformed RSA in execution speed across all tested parameters. Beyond benchmarking, the ML-KEM is shown to handle the computational hardness of the Module Learning With Errors (MLWE) problem, ensuring resilience against quantum attacks, classical attacks, and Artificial Intelligence (AI)-based attacks, since the ML-KEM has no pattern that could be detected. To evaluate its practical feasibility on constrained devices, we also tested the C++ implementation on a Raspberry Pi 4B, representing IoT use cases. Additionally, we attempted to run integration and benchmark tests for the ML-KEM on microcontrollers such as the ESP32 DevKit, ESP32 Super Mini, ESP8266, and Raspberry Pi Pico, but these trials were unsuccessful due to memory constraints. The results showed that while the ML-KEM can operate effectively in such environments, only devices with sufficient resources and runtimes can support its computational demands. While resource-intensive, the ML-KEM offers scalable security across diverse domains such as IoT, cloud environments, and financial systems, making it a key solution for future cryptographic standards. Full article

Traditional cryptographic systems face critical vulnerabilities posed by the rapid advancement of quantum computing, particularly concerning key exchange mechanisms and the quality of entropy sources for random number generation. To address these challenges, this paper proposes a multi-layered, quantum-resistant hybrid cryptographic architecture. First, to ensure robust data confidentiality and secure key establishment, the architecture employs AES-256 (Advanced Encryption Standard-256) for data encryption and utilizes the Kyber Key Encapsulation Mechanism (KEM), which is based on the Learning With Errors (LWE) problem, for secure key exchange. Second, to further bolster overall security by establishing a high-quality cryptographic foundation, we design a TRNG (true random number generator) system based on a multi-level Ring Oscillator (RO) architecture (employing 5, 7, 9, and 11 inverter stages), which provides a reliable and high-quality entropy source. Third, to enable intelligent and adaptive security management, we introduce FA-Kyber (Flow-Adaptive Kyber), a dual-trigger key exchange framework facilitating dynamic key management strategies. Experimental evaluations demonstrate that our implementation exhibits robust performance, achieving an encrypted data transmission throughput of over 550 Mbps with an average end-to-end latency of only 3.14 ms and a key exchange success rate of 99.99% under various network conditions. The system exhibits excellent stability under network congestion, maintaining 86% of baseline throughput under moderate stress, while adaptively increasing the key rotation frequency to enhance security. This comprehensive approach strikes an optimal balance between performance and post-quantum resilience for sensitive communications. Full article

Carbon nanotubes (CNTs) functionalized with metal oxides exhibit synergistic properties that enhance their performance across various applications, particularly in electrochemistry. Recent advancements have highlighted the potential of CNT–metal oxide heterostructures, with a specific focus on their electrochemical properties, which are pivotal for applications in sensors, supercapacitors, batteries, and catalytic systems. Among these, nickel oxide (NiO)-modified CNTs have garnered significant attention due to their cost-effectiveness, facile synthesis, and promising gas-sensing capabilities. This study employs quantum-chemical calculations within the framework of density functional theory (DFT) to elucidate the interaction mechanisms between CNTs and NiO. The results demonstrate that the adsorption process leads to the formation of stable CNT-NiO complexes, with detailed analysis of adsorption energies, equilibrium distances, and electronic structure modifications. The single-electron spectra and density of states (DOS) of the optimized complexes reveal significant alterations in the electronic properties, particularly the modulation of the energy gap induced by surface and edge functionalization. Furthermore, the interaction of CNT-NiO composites with acetone (C₃H₆O) and carbon dioxide (CO₂) is modeled, revealing a physisorption-dominated mechanism. The adsorption of these gases induces notable changes in the electronic properties and charge distribution within the system, underscoring the potential of CNT-NiO composites for gas-sensing applications. This investigation provides a foundational understanding of the role of metal oxide modifications in tailoring the sensory activity of CNTs toward trace amounts of diverse substances, including metal atoms, inorganic molecules, and organic compounds. The findings suggest that CNT-NiO systems can serve as highly sensitive and selective sensing elements, with potential applications in medical diagnostics and environmental monitoring, thereby advancing the development of next-generation sensor technologies. Full article

Lithium-ion batteries (LIBs) have come to hold ever greater significance across diverse fields. However, thermal runaway and associated fire incidents have undeniably constrained the application and development of LIBs. Consequently, gaining a profound understanding of the reaction mechanisms of LIB electrolytes during thermal runaway is of critical importance for ensuring the fire protection of LIBs. In this study, quantum chemical calculations were employed to construct oxidation reaction models of electrolytes, and a comprehensive summary of the sources of fire gas generation during the thermal runaway of LIBs is presented. During the sequence of oxidation reactions, the -COH functional group emerged as the most critical intermediate product. Under conditions of low oxygen availability, it was prone to decompose into CO, whereas in the presence of sufficient oxygen, it could undergo further oxidation to form -COOH and subsequently decompose into CO₂. Moreover, the reaction chains associated with electrolyte oxidation were found to be highly intricate, characterized by multiple branches and a wide variety of intermediate products. Furthermore, an in-depth analysis was carried out on the generation mechanisms of several typical fire gases. The analysis revealed that CH₃OH and C₂H₅OH could be considered as the characteristic products of the oxidation reactions of DMC and DEC, respectively. It is anticipated that this research will provide a robust theoretical foundation for elucidating the complex reactions involved in LIB fires and offer reaction models for fire simulation purposes, thereby contributing to the enhancement of the safety and reliability of LIBs in various applications. Full article

Inorganic nanomaterials are pivotal foundational materials driving traditional industries’ transformation and emerging sectors’ evolution. However, their industrial application is hindered by the limitations of conventional synthesis methods, including poor batch stability, scaling challenges, and complex quality control requirements. This review systematically examines strategies for constructing automated synthesis systems to enhance the production efficiency of inorganic nanomaterials. Methodologies encompassing hardware architecture design, software algorithm optimization, and artificial intelligence (AI)-enabled intelligent process control are analyzed. Case studies on quantum dots and gold nanoparticles demonstrate the enhanced efficiency of closed-loop synthesis systems and their machine learning-enabled autonomous optimization of process parameters. The study highlights the critical role of automation, intelligent technologies, and human–machine collaboration in elucidating synthesis mechanisms. Current challenges in cross-scale mechanistic modeling, high-throughput experimental integration, and standardized database development are discussed. Finally, the prospects of AI-driven synthesis systems are envisioned, emphasizing their potential to accelerate novel material discovery and revolutionize nanomanufacturing paradigms within the framework of AI-plus initiatives. Full article

Brain aging is a complex process regulated by genetic, environmental, and metabolic factors, and increasing evidence suggests that environmental pollutants can significantly accelerate this process by interfering with oxidative stress, neuroinflammation, and mitochondrial function-related signaling pathways. Traditional studies have focused on the direct damage of pollutants on macromolecules (e.g., proteins, DNA), while the central role of senescence-associated small molecules (e.g., ROS, PGE2, lactate) in early regulatory mechanisms has been long neglected. In this study, we innovatively proposed a cascade framework of “small molecule metabolic imbalance-signaling pathway dysregulation-macromolecule collapse”, which reveals that pollutants exacerbate the dynamics of brain aging through activation of NLRP3 inflammatory vesicles and inhibition of HIF-1α. Meanwhile, to address the technical bottleneck of small molecule spatiotemporal dynamics monitoring, this paper systematically reviews the cutting-edge detection tools such as electrochemical sensors, genetically encoded fluorescent probes and antioxidant quantum dots (AQDs). Among them, AQDs show unique advantages in real-time monitoring of ROS fluctuations and intervention of oxidative damage by virtue of their ultra-high specific surface area, controllable surface modification, and free radical scavenging ability. By integrating multimodal detection techniques and mechanism studies, this work provides a new perspective for analyzing pollutant-induced brain aging and lays a methodological foundation for early intervention strategies based on small molecule metabolic networks. Full article

The POVM theorem is a central result in Bohmian mechanics, grounding the measurement formalism of standard quantum mechanics in a statistical analysis based on the quantum equilibrium hypothesis (the Born rule for Bohmian particle positions). It states that the outcome statistics of an experiment are described by a positive operator-valued measure (POVM) acting on the Hilbert space of the measured system. In light of recent debates about the scope and status of this result, we provide a systematic presentation of the POVM theorem and its underlying assumptions with a focus on their conceptual foundations and physical justifications. We conclude with a brief discussion of the scope of the POVM theorem—especially the sense in which it does (and does not) place limits on what is “measurable” in Bohmian mechanics. Full article

This manuscript introduces a comprehensive framework for augmenting classical statistical methodologies through the targeted integration of core quantum mechanical principles—specifically superposition, entanglement, measurement, wavefunctions, and density matrices. By concentrating on these foundational concepts instead of the whole expanse of quantum theory, we propose “quantum-inspired” models that address persistent shortcomings in conventional statistical approaches. In particular, five pivotal distributions (normal, binomial, Poisson, Student’s t, and chi-square) are reformulated to incorporate interference terms, phase factors, and operator-based transformations, thereby facilitating the representation of multimodal data, phase-sensitive dependencies, and correlated event patterns—characteristics that are frequently underrepresented in purely real-valued, classical frameworks. Furthermore, ten quantum-inspired statistical principles are delineated to guide practitioners in systematically adapting quantum mechanics for traditional inferential tasks. These principles are illustrated through domain-specific applications in finance, cryptography (distinct from direct quantum cryptography applications), healthcare, and climate modeling, demonstrating how amplitude-based confidence measures, density matrices, and measurement analogies can enrich standard statistical models by capturing more nuanced correlation structures and enhancing predictive performance. By unifying quantum constructs with established statistical theory, this work underscores the potential for interdisciplinary collaboration and paves the way for advanced data analysis tools capable of addressing high-dimensional, complex, and dynamically evolving datasets. Complete R code ensures reproducibility and further exploration. Full article

Traditional centralized energy grids struggle to meet urban areas’ increasingly complex energy demands, necessitating the development of more sustainable and resilient energy solutions. Smart microgrids offer a decentralized approach that enhances energy efficiency, facilitates the integration of renewable energy sources, and improves urban resilience. This study follows a systematic review approach, analyzing the literature published in peer-reviewed journals, conference proceedings, and industry reports between 2011 and 2025. The research draws from academic publications of energy institutions alongside regulatory reports, examining actual smart microgrid deployments in San Diego, Barcelona, and Seoul. Additionally, this article provides real-world case studies from New York and London, showcasing successful and unsuccessful smart microgrid deployments. The Brooklyn Microgrid in New York demonstrates peer-to-peer energy trading, while London faces regulations and funding challenges in its decentralized energy systems. The paper also explores economic and policy frameworks such as public–private partnerships (PPPs), localized energy markets, and standardized regulatory models to enable microgrid adoption at scale. While PPPs provide financial and infrastructural support for microgrid deployment, they also introduce stakeholder alignment and regulatory compliance complexities. Countries like Germany and India have successfully used PPPs for smart microgrid development, leveraging low-interest loans, government incentives, and regulatory mechanisms to encourage innovation and adoption of smart microgrid technologies. In addition, the review examines new trends like the utilization of AI and quantum computing to optimize energy, peer-to-peer energy trading, and climate resilient design before outlining a future research agenda focused on cybersecurity, decarbonization, and the inclusion of new technology. Contributions include the development of a modular and scalable microgrid framework, innovative hybrid storage systems, and a performance-based policy model suited to the urban environment. These contributions help to fill the gap between what is possible today and what is needed for future sustainable urban energy systems and create the foundation for resilient cities of the next century. Full article