Search Results (147)

This follow-up scientific case study builds on prior research to explore the computational challenges of applying quantum algorithms to financial asset management, focusing specifically on solving the graph-cut problem for investment recommendation. Unlike our prior study, which focused on idealized QAOA performance, this work introduces a graph compression pipeline that enables QAOA deployment under real quantum hardware constraints. This study investigates quantum-accelerated spectral graph compression for financial asset recommendations, addressing scalability and regulatory constraints in portfolio management. We propose a hybrid framework combining the Quantum Approximate Optimization Algorithm (QAOA) with spectral graph theory to solve the Max-Cut problem for investor clustering. Our methodology leverages quantum simulators (cuQuantum and Cirq-GPU) to evaluate performance against classical brute-force enumeration, with graph compression techniques enabling deployment on resource-constrained quantum hardware. The results underscore that efficient graph compression is crucial for successful implementation. The framework bridges theoretical quantum advantage with practical financial use cases, though hardware limitations (qubit counts, coherence times) necessitate hybrid quantum-classical implementations. These findings advance the deployment of quantum algorithms in mission-critical financial systems, particularly for high-dimensional investor profiling under regulatory constraints. Full article

This article reconsiders the double-slit experiment by establishing a new type of relationship between it and the concept of entanglement. While the role of entanglement in the double-slit experiment has been considered, this particular relationship appears to have been missed in preceding discussions of the experiment, even by Bohr, who extensively used it to support his argument concerning quantum physics. The main reason for this relationship is the different roles of the diaphragm with slits in two setups, S1 and S2, defining the double-slit experiment as a quantum experiment. In S1, in each individual run of the experiment one can in principle (even if not actually) know throughout which slit the quantum object considered has passed; in S2 this knowledge is in principle impossible, which impossibility is coextensive with the appearance of the interference pattern, once a sufficient number of individual runs of the experiment have taken place. The article offers the following argument based on two new concepts, an “experimentally quantum object” and an “ontologically quantum object.” In S1 the diaphragm can be treated as part of an observational arrangement and thus considered as a classical object, while the object passing through one or the other slit is considered as an “ontologically quantum object,” defined as an object necessary to establish a quantum phenomenon. By contrast, in S2, the diaphragm can, via the concept of Heisenberg-von-Neumann cut, be treated as an “experimentally quantum object,” defined as an object treatable by quantum theory, even while possibly being an ontologically classical object. This interaction is not an observation but a quantum entanglement between these two quantum objects, one ontologically and one experimentally quantum. This argument is grounded in a particular interpretation of quantum phenomena and quantum theory, which belongs to the class of interpretations designated here as “reality without realism” (RWR) interpretations. The article also argues that wave-particle complementarity, with which the concept of complementarity is often associated, plays little, if any, role in quantum physics, or in Bohr’s thinking, and may be misleading in considering the double-slit experiment, often explained by using this complementarity. Full article

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent. Full article

Against the background of the vigorous development of materials science and the deep cross-infiltration in many fields, a new medicine food homology, carbon dots (herein combined and abbreviated as MFH-CDs), has sprung up, showing great potential. This review used ChatGPT 4.0 to collect background information related to carbon dots, focusing on the common rich medicinal and food resources such as Lycium barbarum, Chinese yam, honeysuckle, and Ganoderma lucidum. These carbon dots are synthesized by hydrothermal synthesis, microwave radiation, and pyrolysis, which have the advantages of small particle size, high quantum yield, and low cytotoxicity. Recent studies have found that MFH-CDs have great application potential in biosensors, biological imaging, and drug delivery. In this paper, the characteristics of preparing carbon dots from different medicinal and edible resources and their applications in biology in recent years are reviewed, which provides in-depth guidance for the research and application of carbon dots from medicinal and edible biomass, helps it shine in multidisciplinary fields, and opens a brand-new journey from traditional medicinal and edible culture to cutting-edge technology application. Full article

We present a study of relic gravitational waves based on a foliated gauge field theory defined over a spacetime endowed with a noncommutative algebraic–geometric structure. As an ontological extension of general relativity—concerning manifolds, metrics, and fiber bundles—the conventional space and time coordinates, typically treated as classical numbers, are replaced by complementary quantum dual fields. Within this framework, consistent with the Bekenstein criterion and the Hawking–Hertog multiverse conception, singularities merge into a helix-like cosmic scale factor that encodes the topological transition between the contraction and expansion phases of the universe analytically continued into the complex plane. This scale factor captures the essence of an intricate topological quantum-leap transition between two phases of the branching universe: a contraction phase preceding the now-surpassed conventional concept of a primordial singularity and a subsequent expansion phase, whose transition region is characterized by a Riemannian topological foliated structure. The present linearized formulation, based on a slight gravitational field perturbation, also reveals a high sensitivity of relic gravitational wave amplitudes to the primordial matter and energy content during the universe’s phase transition. It further predicts stochastic homogeneous distributions of gravitational wave intensities arising from the interplay of short- and long-spacetime effects within the non-commutative algebraic framework. These results align with the anticipated future observations of relic gravitational waves, expected to pervade the universe as a stochastic, homogeneous background. Full article

Wastewater management has a direct impact on the supply of drinking water. New cutting-edge technologies are crucial to the ever-growing demand for tailored solutions for pollutant removal, but these pollutants first need to be detected. Traditional techniques are costly and are no longer competitive in the wastewater cleaning market. One sustainable and economically viable alternative is the fabrication of integrated nanosensors composed of conducting polymers. These include polyaniline doped with various types of nanomaterials such as nanocarbons (carbon nanotubes and graphene), metal oxide nanoparticles/nanostructures, and quantum dots. The synergistic properties of these components can endow sensing materials with enhanced surface reactivity, greater electrocatalytic activity, as well as tunable redox activity and electrical conductivity. This review covers key recent advances in the field of non-enzyme electrochemical conductive polymer nanosensors for pollutant detection in aqueous environments or simulated polluted samples. It provides an introduction to these sensors, their preparation, applications, the environmental and economic hurdles impeding the large-scale development of PANI-based nanomaterials in sensing applications, and future directions for research and real-world applications. Full article

Molecular alignment under strong laser pulses is an important tool for manipulating quantum states and investigating ultrafast phenomena. This review summarizes two decades of advancement in laser-driven alignment techniques, such as cross-polarized double pulses, optical centrifuges, and elliptically truncated fields. Given the prominent emphasis on transformational applications in current alignment research, we outline its importance in cutting-edge applications under strong laser pulses, such as chiral discrimination, high-harmonic generation (HHG), photoelectron angular distributions (PADs) and ionization yields in photoionization, and Terahertz (THz) manipulation. These interdisciplinary developments provide fundamental insights into ultrafast molecular dynamics. They also establish frameworks for advanced light–matter interaction control. Full article

Heavy metal ion (HMI) contamination poses significant threats to public health and environmental safety, necessitating advanced detection technologies that are rapid, sensitive, and field-deployable. While conventional methods like atomic absorption spectroscopy (AAS) and inductively coupled plasma mass spectrometry (ICP-MS) remain prevalent, their limitations—including high costs, complex workflows, and lack of portability—underscore the urgent need for innovative alternatives. This review consolidates advancements in the last five years in microfluidic technologies for HMI detection, emphasizing their transformative potential through miniaturization, integration, and automation. We critically evaluate the synergy of microfluidics with cutting-edge materials (e.g., graphene and quantum dots) and detection mechanisms (electrochemical, optical, and colorimetric), enabling ultra-trace detection at parts-per-billion (ppb) levels. We highlight novel device architectures, such as polydimethylsiloxane (PDMS)-based labs-on-chip (LOCs), paper-based microfluidics, 3D-printed systems, and digital microfluidics (DMF), which offer unparalleled portability, cost-effectiveness, and multiplexing capabilities. Additionally, we address persistent challenges (e.g., selectivity and scalability) and propose future directions, including AI integration and sustainable fabrication. By bridging gaps between laboratory research and practical deployment, this review provides a roadmap for next-generation microfluidic solutions, positioning them as indispensable tools for global HMI monitoring. Full article

Graphene quantum dots (GQDs) show significant promise as antibacterial agents, but their application is hindered by several limitations, including potential cytotoxicity at high concentrations, as well as concerns regarding aggregation and reusability. In this study, sodium titanate (NTO) ultralong nanotubes were utilized as both a photocatalyst and support for GQDs. The NTO/GQDs heterojunction was formed by embedding GQDs nanoplates onto the walls of NTO nanotubes. This integration significantly improved the visible light absorption and enhanced the separation and transfer of electron–hole pairs, leading to an efficient photocatalytic antibacterial process. The NTO/GQD-8 self-supporting membrane composed of these ultralong nanotubes demonstrated outstanding antibacterial efficiency (99.99%) against E. coli and exhibited remarkable cycling stability. Radical scavenging experiments revealed that ∙OH and e⁻ were the primary reactive species driving the photocatalytic antibacterial process. Notably, NTO and NTO/GQDs-8 exhibited distinct antibacterial outcomes. After photocatalytic treatment with NTO/GQDs-8, E. coli cells were completely fragmented, with no intact cell structures remaining due to the synergy effect of GQDs’ physical cutting during photocatalytic treatment. 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

This study explores the application of quantum computing in asset management, focusing on the use of the Quantum Approximate Optimization Algorithm (QAOA) to solve specific classes of financial asset recommendation problems. While quantum computing holds promise for combinatorial optimization tasks, its application to portfolio management faces significant challenges in scalability for practical implementations. In this work, we model the problem using a graph representation where nodes represent investors, and edges reflect significant similarities in asset choices. We test the proposed method using quantum simulators, including cuQuantum, Cirq-GPU, and Cirq with IonQ, and compare the performance of quantum optimization against classical brute-force methods. Our results suggest that quantum algorithms may offer computational advantages for certain use cases, though classical heuristics also provide competitive performance for smaller datasets. This study contributes to the ongoing investigation into the potential of quantum computing for real-time financial decision-making, providing insights into both its applicability and limitations in asset management for larger and more complex investor datasets. Full article

The confluence of the Internet of Things (IoT) and cloud computing heralds a paradigm shift in data-driven applications, promising unprecedented insights and automation across critical sectors like healthcare, smart cities, and industrial automation. However, this transformative synergy introduces a complex tapestry of security vulnerabilities stemming from the intrinsic resource limitations of IoT devices and the inherent complexities of cloud infrastructures. This survey delves into the escalating threats—from conventional data breaches and Application programming interface (API) exploits to emerging vectors such as adversarial artificial intelligence (AI), quantum-resistant attacks, and sophisticated insider threats—that imperil the integrity and resilience of IoT–cloud ecosystems. We critically evaluated existing security paradigms, including encryption, access control, and service-level agreements, juxtaposed with cutting-edge approaches like AI-driven anomaly detection, blockchain-secured frameworks, and lightweight cryptographic solutions. By systematically mapping the landscape of security challenges and mitigation strategies, this work identified the following critical research imperatives: the development of standardized, end-to-end security architectures, the integration of post-quantum cryptography for resource-constrained IoT devices, and the fortification of resource isolation in multi-tenant cloud environments. A comprehensive comparative analysis of prior research, coupled with an in-depth case study on IoT–cloud security within the healthcare domain, illuminates the practical challenges and innovative solutions crucial for real-world deployment. Ultimately, this survey advocates for the development of scalable, adaptive security frameworks that leverage the synergistic power of AI and blockchain, ensuring the secure and efficient evolution of IoT–cloud ecosystems in the face of evolving cyber threats. Full article

Many public key cryptosystems in use today run the risk of being broken as quantum computing develops, affecting the security of popular commercial applications. Although there are possible defenses against quantum assaults, their complexity and inefficiency make them unsuitable for daily usage. This article discusses asymmetric hash-based digital signature algorithms, focusing on the symmetric and balanced structure achieved through the use of $k$-ary Verkle trees and lattice-based vector commitments. The study investigates the new concepts using vector commitments, a Verkle tree, and $k$-ary Verkle tree, which enhance efficiency and scalability while maintaining a symmetric structure in cryptographic proofs. Using cutting-edge Verkle tree technology, the authors of this paper provide a novel approach to creating a digital signature system. This is accomplished by using vector commitments, a $k$-ary Verkle tree, Verkle tree, and lattice-based vector commitments for post-quantum characteristics. The study also presents the ideas for designing post-quantum signatures using a $k$-ary Verkle tree, emphasizing the symmetry in tree structures and the balance between security and efficiency in asymmetric cryptographic systems. Full article

Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt the transition to Ag5.0, this paper comprehensively reviews the role of AI, machine learning (ML) and other emerging technologies to overcome current and future crop management challenges. Crop management has progressed significantly from early agricultural methods to the advanced capabilities of Ag5.0, marking a notable leap in precision agriculture. Emerging technologies such as collaborative robots, 6G, digital twins, the Internet of Things (IoT), blockchain, cloud computing, and quantum technologies are central to this evolution. The paper also highlights how machine learning and modern agricultural tools are improving the way we perceive, analyze, and manage crop growth. Additionally, it explores real-world case studies showcasing the application of machine learning and deep learning in crop monitoring. Innovations in smart sensors, AI-based robotics, and advanced communication systems are driving the next phase of agricultural digitalization and decision-making. The paper addresses the opportunities and challenges that come with adopting Ag5.0, emphasizing the transformative potential of these technologies in improving agricultural productivity and tackling global food security issues. Finally, as Agriculture 5.0 is the future of agriculture, we highlight future trends and research needs such as multidisciplinary approaches, regional adaptation, and advancements in AI and robotics. Ag5.0 represents a paradigm shift towards precision crop management, fostering sustainable, data-driven farming systems that optimize productivity while minimizing environmental impact. Full article