Search Results (1,210)

With the ever-growing demand for high-speed optical communications, microwave photonics, and quantum key distribution systems, compact electro-optic (EO) modulators with high extinction ratios, large bandwidth, and high tuning efficiency are urgently pursued. However, most integrated lithium–niobate (LN) modulators cannot achieve these high performances simultaneously. In this paper, we propose an improved theoretical model of a chip-scale electro-optic (EO) microring modulator (EO-MRM) based on X-cut lithium–niobate-on-insulator (LNOI) with a hybrid architecture consisting of a 180-degree Euler bend in the coupling region, double-layer metal electrode structure, and ground–signal–signal–ground (G-S-S-G) electrode configuration, which can realize highly comprehensive performance and a compact footprint. After parameter optimization, the designed EO-MRM exhibited an extinction ratio of 38 dB. Compared to the structure without Euler bends, the increase was 35 dB. It also had a modulation bandwidth of 29 GHz and a tunability of 8.24 pm/V when the straight waveguide length was 100 μm. At the same time, the proposed device footprint was 1.92 × 10⁴ μm². The proposed MRM model provides an efficient solution to high-speed optical communication systems and microwave photonics, which is helpful for the fabrication of high-performance and multifunctional photonic integrated devices. Full article

Photothermal reactions, involving both photochemical and thermal reaction steps, are the most abundant sequences in photochemistry. The derivation of their rate laws is standardized, but the integration of these rate laws has not yet been achieved. Indeed, the field still lacks integrated rate laws for the description of these reactions’ behavior and/or identification of their reaction order. This made difficult a comprehensive account of the photokinetics of photothermal reactions, which created a gap in knowledge. This gap is addressed in the present paper by introducing an unprecedented general model equation capable of mapping out the kinetic traces of such reactions when exposed to light or in the dark. The integrated rate law model equation also applies when the reactive medium is exposed to either monochromatic or polychromatic light irradiation. The validity of the model equation was established against simulated data obtained by a fourth-order Runge–Kutta method. It was then used to describe and quantify several situations of photothermal reactions, such as the effects of initial concentration, spectator molecules, and incident radiation intensity, and the impact of the latter on the photonic yield. The model equation facilitated a general elucidation method to determine the intrinsic reaction parameters (quantum yields and absorptivities of the reactive species) for any photothermal mechanism whose number of species is known. This paper contributes to rationalizing photokinetics along the same general guidelines adopted in chemical kinetics. Full article

We study an interesting superization problem of integrable nonlinear dynamical systems on functional manifolds. As an example, we considered a quantum many-particle Schrödinger–Davydov model on the axis, whose quasi-classical reduction proved to be a completely integrable Hamiltonian system on a smooth functional manifold. We checked that the so-called “naive” approach, based on the superization of the related phase space variables via extending the corresponding Poisson brackets upon the related functional supermanifold, fails to retain the dynamical system super-integrability. Moreover, we demonstrated that there exists a wide class of classical Lax-type integrable nonlinear dynamical systems on axes in relation to which a superization scheme consists in a reasonable superization of the related Lax-type representation by means of passing from the basic algebra of pseudo-differential operators on the axis to the corresponding superalgebra of super-pseudodifferential operators on the superaxis. Full article

Monte Carlo (MC) simulations have become important in advancing nanoparticle (NP)-based applications for cancer imaging and therapy. This review explores the critical role of MC simulations in modeling complex biological interactions, optimizing NP designs, and enhancing the precision of therapeutic and diagnostic strategies. Key findings highlight the ability of MC simulations to predict NP bio-distribution, radiation dosimetry, and treatment efficacy, providing a robust framework for addressing the stochastic nature of biological systems. Despite their contributions, MC simulations face challenges such as modeling biological complexity, computational demands, and the scarcity of reliable nanoscale data. However, emerging technologies, including hybrid modeling approaches, high-performance computing, and quantum simulation, are poised to overcome these limitations. Furthermore, novel advancements such as FLASH radiotherapy, multifunctional NPs, and patient-specific data integration are expanding the capabilities and clinical relevance of MC simulations. This topical review underscores the transformative potential of MC simulations in bridging fundamental research and clinical translation. By facilitating personalized nanomedicine and streamlining regulatory and clinical trial processes, MC simulations offer a pathway toward more effective, tailored, and accessible cancer treatments. The continued evolution of simulation techniques, driven by interdisciplinary collaboration and technological innovation, ensures that MC simulations will remain at the forefront of nanomedicine’s progress. Full article

In the context of data security, this work aims to present a novel solution that, rather than addressing the topic of endpoint security—which has already garnered significant attention within the international scientific community—offers a different perspective on the subject. In other words, the focus is not on device security but rather on the protection and security of the information contained within those devices. As we will see, the result is a next-generation decentralized infrastructure that simultaneously integrates two cognitive areas: data storage and its protection and security. In this context, an innovative Multiscale Relativistic Quantum (MuReQua) chain is considered to realize a novel decentralized and security solution for storing data. This engine is based on the principles of Quantum Mechanics, stochastic processes, and a new approach of decentralization for data storage focused on information security. The solution is broken down into four main components, considered four levels of security against attackers: (i) defocusing, (ii) fogging, (iii) puzzling, and (iv) crypto agility. The defocusing is realized thanks to a fragmentation of the contents and their distributions on different allocations, while the fogging is a component consisting of a solution of hybrid cyphering. Then, the puzzling is a unit of Information Fusion and Inverse Information Fusion, while the crypto agility component is a frontier component based on Quantum Computing, which gives a stochastic dynamic to the information and, in particular, to its data fragments. The data analytics show a very effective and robust solution, with executions time comparable with cloud technologies, but with a level of security that is a post quantum one. In the end, thanks to a specific application example, going beyond purely technical and technological aspects, this work introduces a new cognitive perspective regarding (i) the distinction between data and information, and (ii) the differentiation between the owner and the custodian of data. Full article

Affective computing aims to develop systems capable of effectively interacting with people through emotion recognition. Neuroscience and psychology have established models that classify universal human emotions, providing a foundational framework for developing emotion recognition systems. Brain activity related to emotional states can be captured through electroencephalography (EEG), enabling the creation of models that classify emotions even in uncontrolled environments. In this study, we propose an emotion recognition model based on EEG signals using deep learning techniques on a proprietary database. To improve the separability of emotions, we explored various data transformation techniques, including Fourier Neural Networks and quantum rotations. The convolutional neural network model, combined with quantum rotations, achieved a 95% accuracy in emotion classification, particularly in distinguishing sad emotions. The integration of these transformations can further enhance overall emotion recognition performance. Full article

This paper explores fundamental issues in the correct contraction and expansion of operators, with a primary focus on the concept of symmetry within operator theory. Special attention is given to how symmetry influences the behavior of operators, particularly regarding their approximation and convergence properties. In the domains of quantum mechanics and condensed matter physics, such operators are essential for modeling phenomena like superconductivity, excitons, and surface states. The symmetric properties of operators have a profound impact on the physical interpretations and predictions these models generate. A rigorous analysis is provided regarding the existence of correct contractions and expansions for a specific class of nonlinear operators, demonstrating how symmetry affects the structural integrity of operators under natural conditions. The study presents a comprehensive description of the set of all correct contractions, expansions, and regular expansions, with an application to a third-order nonlinear differential expression. Additionally, a condition for the unique solvability of a Bitsadze–Samarskii-type problem is derived, showcasing how symmetry plays a crucial role in guiding the solution of complex physical models. Furthermore, the paper emphasizes the importance of preserving symmetry in the construction of operators, ensuring the consistency and accuracy of mathematical models. This has significant implications for both theoretical research and practical applications in various fields, including nuclear physics and quantum theory. Full article

We introduce the concept of ergodicity and explore its deviation caused by quantum scars in an isolated quantum system, employing a pedagogical approach based on a toy model. Quantum scars, originally identified as traces of classically unstable orbits in certain wavefunctions of chaotic systems, have recently regained interest for their role in non-ergodic dynamics, as they retain memory of their initial states. We elucidate these features of quantum scars within the same framework of this toy model. The integrable part of the model consists of two large spins, with a classical counterpart, which we combine with a random matrix to induce ergodic behavior. Scarred states can be selectively generated from the integrable spin Hamiltonian by protecting them from the ergodic states using a projector method. Deformed projectors mimic the ‘quantum leakage’ of scarred states, enabling tunable mixing with ergodic states and thereby controlling the degree of scarring. In this simple model, we investigate various properties of quantum scarring and shed light on different aspects of many-body quantum scars observed in more complex quantum systems. Notably, the underlying classicality can be revealed through the entanglement spectrum and the dynamics of ‘out-of-time-ordered correlators’. Full article

This review article provides the basics and discusses some important applications of thermal field theory, namely, the combination of statistical mechanics and relativistic quantum field theory. In the first part, the fundamentals are covered: the density matrix, the corresponding averages, and the treatment of fields of various spin in a medium. The second part is dedicated to the computation of thermal Green’s function for scalars, vectors, and fermions with path-integral methods. These functions play a crucial role in thermal field theory as explained here. A more applicative part of the review is dedicated to the production of particles in a medium and to phase transitions in field theory, including the process of vacuum decay in a general theory featuring a first-order phase transition. To understand this review, the reader should have good knowledge of non-statistical quantum field theory. Full article

In this work, we elaborate further on a (3+1)-dimensional cosmological Running-Vacuum-type-Model (RVM) of inflation based on string-inspired Chern-Simons(CS) gravity, involving axions coupled to gravitational-CS(gCS) anomalous terms. Inflation in such models is caused by primordial-gravitational-waves(GW)-induced condensation of the gCS terms, which leads to a linear-axion potential. We demonstrate that this inflationary phase may be metastable, due to the existence of imaginary parts of the gCS condensate. These are quantum effects, proportional to commutators of GW perturbations, hence vanishing in the classical theory. Their existence is quantum-ordering-scheme dependent. We argue in favor of a physical importance of such imaginary parts, which we compute to second order in the GW (tensor) perturbations in the framework of a gauge-fixed effective Lagrangian, within a (mean field) weak-quantum-gravity-path-integral approach. We thus provide estimates of the inflation lifetime. On matching our results with the inflationary phenomenology, we fix the quantum-ordering ambiguities, and obtain an order-of-magnitude constraint on the String-Mass-Scale-to-Planck-Mass ratio, consistent with previous estimates by the authors in the framework of a dynamical-system approach to linear-axion RVM inflation. Finally, we examine the role of periodic modulations in the axion potential induced by non-perturbative effects on the slow-roll inflationary parameters, and find compatibility with the cosmological data. Full article

Integrating functional materials with photonic and optoelectronic technologies has revolutionized medical diagnostics, enhancing imaging and sensing capabilities. This review provides a comprehensive overview of recent innovations in functional materials, such as quantum dots, perovskites, plasmonic nanomaterials, and organic semiconductors, which have been instrumental in the development of diagnostic devices characterized by high sensitivity, specificity, and resolution. Their unique optical properties enable real-time monitoring of biological processes, advancing early disease detection and personalized treatment. However, challenges such as material stability, reproducibility, scalability, and environmental sustainability remain critical barriers to their clinical translation. Breakthroughs such as green synthesis, continuous flow production, and advanced surface engineering are addressing these limitations, paving the way for next-generation diagnostic tools. This article highlights the transformative potential of interdisciplinary research in overcoming these challenges and emphasizes the importance of sustainable and scalable strategies for harnessing functional materials in medical diagnostics. The ultimate goal is to inspire further innovation in the field, enabling the creation of practical, cost-effective, and environmentally friendly diagnostic solutions. Full article

Low-power gas sensors that can be used in IoT (Internet of Things) systems, consumer devices, and point-of-care devices will enable new applications in environmental monitoring and health protection. We fabricated a monolithic chemiresistive gas sensor by integrating a micro-lightplate with a 2D sensing material composed of single-layer graphene and monolayer-thick TiO₂. Applying ultraviolet (380 nm) light with quantum energy above the TiO₂ bandgap effectively enhanced the sensor responses. Low (<1 μW optical) power operation of the device was demonstrated by measuring NO₂ gas at low concentrations, which is typical in air quality monitoring, with an estimated limit of detection < 0.1 ppb. The gas response amplitudes remained nearly constant over the studied light intensity range (1–150 mW/cm²) owing to the balance between the photoinduced adsorption and desorption processes of the gas molecules. The rates of both processes followed an approximately square-root dependence on light intensity, plausibly because the electron–hole recombination of photoinduced charge carriers is the primary rate-limiting factor. These results pave the way for integrating 2D materials with micro-LED arrays as a feasible path to advanced electronic noses. Full article

We propose a temperature-dependent optimization procedure for the second-nearest neighbor (2NN) sp³s* tight-binding (TB) theory parameters to calculate the effects of strain, structure dimensions, and alloy composition on the band structure of heterostructure spherical core/shell quantum dots (QDs). We integrate the thermoelastic theory of solids with the 2NN sp³s* TB theory to calculate the strain, core and shell dimensions, and composition effects on the band structure of binary/ternary CdSe/Cd(Zn)S and ZnSe/Zn(Cd)S QDs at any temperature. We show that the 2NN sp³s* TB theory with optimized parameters greatly improves the prediction of the energy dispersion curve at and in the vicinity of L and X symmetry points. We further used the optimized 2NN sp³s* TB parameters to calculate the strain, core and shell dimensions, and composition effects on the nanocrystal bandgaps of binary/ternary CdSe/Cd(Zn)S and ZnSe/Zn(Cd)S core/shell QDs. We conclude that the 2NN sp³s* TB theory provides remarkable agreement with the measured nanocrystal bandgaps of CdSe/Cd(Zn)S and ZnSe/Zn(Cd)S QDs and accurately reproduces the energy dispersion curves of the electronic band structure at any temperature. We believe that the proposed optimization procedure makes the 2NN sp³s* TB theory reliable and accurate in the modeling of core/shell QDs for nanoscale devices. Full article

Single-photon sources with photon antibunching characteristics are essential for quantum information technologies. This paper investigates the potential of quaternary-alloy CdTeSeS colloidal core quantum dots (cQDs) as compact, room-temperature, and fiber-integrated single-photon sources. Single-photon emission from CdTeSeS cQDs was verified by measuring the second-order correlation function, $g^{\left(2\right)}\left(\tau \right)$, using a Hanbury-Brown and Twiss setup. A novel method to determine zero-time delay through afterpulsing analysis is presented. The results demonstrate strong photon antibunching with $g^{\left(2\right)}\left(0\right)=0.13$, confirming that the photoemission from the CdTeSeS cQDs function as a single-photon source. This work highlights the potential of CdTeSeS cQDs as reliable and efficient single-photon sources for practical use in fiber-based quantum information technologies. Full article

The widespread use of the internet, coupled with the increasing production of digital content, has caused significant challenges in information security and manipulation. Deepfake detection has become a critical research topic in both academic and practical domains, as it involves identifying forged elements in artificially generated videos using various deep learning and artificial intelligence techniques. In this dissertation, an innovative model was developed for detecting deepfake videos by combining the Quantum Transfer Learning (QTL) and Class-Attention Vision Transformer (CaiT) architectures. The Deepfake Detection Challenge (DFDC) dataset was used for training, and a system capable of detecting spatiotemporal inconsistencies was constructed by integrating QTL and CaiT technologies. In addition to existing preprocessing methods in the literature, a novel preprocessing function tailored to the requirements of deep learning models was developed for the dataset. The advantages of quantum computing offered by QTL were merged with the global feature extraction capabilities of the CaiT. The results demonstrated that the proposed method achieved a remarkable performance in detecting deepfake videos, with an accuracy of 90% and ROC AUC score of 0.94 achieved. The model’s performance was compared with other methods evaluated on the DFDC dataset, highlighting its efficiency in resource utilization and overall effectiveness. The findings reveal that the proposed QTL-CaiT-based system provides a strong foundation for deepfake detection and contributes significantly to the academic literature. Future research should focus on testing the model on real quantum devices and applying it to larger datasets to further enhance its applicability. Full article