Search Results (321)

Quantum technology, a critical 21st-century strategic frontier science, has been a key technological competition between China and the U.S. This study employs natural language processing (NLP) techniques and a technology analytical framework to analyze the quantum technology policies of both countries. While the U.S. emphasized free-market innovation and global technological leadership on quantum technology from 2018 to 2024, China prioritized government-led development and socioeconomic stability. Moreover, the Chinese government adopts a systematic top-down approach characterized by government planning and direct intervention. However, the U.S. fosters innovation through market mechanisms and industry-academia collaboration. U.S. policies have gradually shifted from pure technological innovation to national security considerations. On the other hand, China has moved from breakthrough research to industrial deployment and application. These policy differences reflect distinct political systems and governance models, which may also resonate with their respective cultural traditions and philosophical foundations. Our findings fill a critical gap in comparative quantum technology policy research, offering significant insights for policymakers, researchers, and international stakeholders. Full article

Recurrence resonance, a phenomenon that enhances system computational capability by exploiting noise to amplify hidden attractors, holds significant potential for applications such as edge computing and neuromorphic computing. Although previous studies have extensively explored its characteristics, the underlying mechanism regarding its generation remains unclear. Here, we employed a Stochastic Recurrent Neural Network to simulate neural networks under various coupling conditions. By introducing appropriate inhibitory connections and examining the state transition matrices, we analyzed the characteristics and correlations of attractor landscapes in both global and local systems to elucidate the generative mechanism behind the “Edge of Chaos” dynamics observed under the quantum logic connectivity structure during recurrence resonance. The results show that the strategic introduction of inhibitory connections enriches the system’s attractor landscape without compromising the intensity of recurrence resonance. Furthermore, we find that when neurons are coupled via quantum logic and noise intensity meets specific conditions, the strong attractors of the global system decompose into those of distinct local subsystems, accompanied by the sharing of structurally similar weak attractors. These findings suggest that under quantum logic connectivity, the interaction between the strong attractors of different subsystems is mediated by a background of shared weak attractors, thereby enhancing both the system’s robustness against noise and the diversity of its state evolution. Full article

Phosphorus-31 nuclear magnetic resonance (³¹P NMR) spectroscopy is a powerful analytical physical chemistry experimental technique that is widely used to study the structure and dynamics of phosphorus-containing compounds today. Accurate calculation of ³¹P NMR chemical shifts lies in the basis of the proper assignment of NMR signals, as they can be closely spaced to each other in the NMR spectra of systems that bear nuclei with subtly different electron environments, like complex organophosphorus compounds, nucleic acids, and phosphates, etc. The most advanced quantum chemistry (QC) methods allow us to reach the agreement between theoretical values of ³¹P NMR chemical shifts and experiments within a few ppm, which makes them a useful tool for studying chemical structure, reaction mechanisms, and catalyst design with the aid of the NMR method. This review surveys the application of both density functional and wave function methods of electron structure to the calculation of ³¹P NMR chemical shifts and proposes a thorough discussion of the latest findings related to the factors affecting the final accuracy of the ³¹P NMR chemical shifts prediction, including basis sets, the geometry factor effect, solvent, vibrational, and relativistic corrections. Full article

IoT-based blockchain technology has improved the healthcare system to ensure the privacy and security of healthcare data. A Blockchain Bridge (BB) is a tool that enables multiple blockchain networks to communicate with each other. The existing approach combining the classical and quantum blockchain models failed to secure the data transmission during cross-chain communication. Thus, this study proposes a new BB verification for secure healthcare data transfer. Additionally, a brain tumor analysis framework is developed based on segmentation and neural networks. After the patient’s registration on the blockchain network, Brain Magnetic Resonance Imaging (MRI) data is encrypted using Hash-Keyed Quantum Cryptography and verified using a Peer-to-Peer Exchange model. The Brain MRI is preprocessed for brain tumor detection using the Fuzzy HaMan C-Means (FHMCM) segmentation technique. The features are extracted from the segmented image and classified using the LeCun Kaiming-based Convolutional ModSwish Neural Network (LCK-CMSNN) classifier. Subsequently, the brain tumor diagnosis report is securely transferred to the patient via a smart contract. The proposed model verified BB with a Verification Time (VT) of 12,541 ms, secured the input with a Security level (SL) of 98.23%, and classified the brain tumor with 99.15% accuracy, thus showing better performance than the existing models. Full article

Microtubules are cylindrical protein polymers that organize the cytoskeleton and play essential roles in intracellular transport, cell division, and possibly cognition. Their highly ordered, quasi-crystalline lattice of tubulin dimers, notably tryptophan residues, endows them with a rich topological and arithmetic structure, making them natural candidates for supporting coherent excitations at optical and terahertz frequencies. The Penrose–Hameroff Orch OR theory proposes that such coherences could couple to gravitationally induced state reduction, forming the quantum substrate of conscious events. Although controversial, recent analyses of dipolar coupling, stochastic resonance, and structured noise in biological media suggest that microtubular assemblies may indeed host transient quantum correlations that persist over biologically relevant timescales. In this work, we build upon two complementary approaches: the parametric resonance model of Nishiyama et al. and our arithmetic–geometric framework, both recently developed in Quantum Reports. We unify these perspectives by describing microtubules as rectangular lattices governed by the imaginary quadratic field $\mathbb{Q}\left(i\right)$, within which nonlinear dipolar oscillations undergo stochastic parametric amplification. Quantization of the resonant modes follows Gaussian norms $N=p^2+q^2$, linking the optical and geometric properties of microtubules to the arithmetic structure of $\mathbb{Q}\left(i\right)$. We further connect these discrete resonances to the derivative of the elliptic L-function, $L\prime (E,1)$, which acts as an arithmetic free energy and defines the scaling between modular invariants and measurable biological ratios. In the appended adelic extension, this framework is shown to merge naturally with the Bost–Connes and Connes–Marcolli systems, where the norm character on the ideles couples to the Hecke character of an elliptic curve to form a unified adelic partition function. The resulting arithmetic–elliptic resonance model provides a coherent bridge between number theory, topological quantum phases, and biological structure, suggesting that consciousness, as envisioned in the Orch OR theory, may emerge from resonant processes organized by deep arithmetic symmetries of space, time, and matter. Full article

In this paper, we theoretically investigate nonlinearly tunable Fano resonance by employing a light tunneling heterostructure with one-dimensional defective photonic crystals and a lossy metallic film. We find that the phenomenon of Fano resonance can be created by coupling the Fabry–Pérot cavity mode with the topological optical Tamm state. We emphasize that the local field confinement induced by Fano resonance can ensure that the large nonlinear permittivity of metal can be utilized sufficiently. We show that the Fano-type transmission spectrum can be actively modulated by altering the input power intensity of light. We also illustrate that the hysteresis effects and nonreciprocal transmission behaviors can be obtained directly by using the Fano resonant heterostructure, allowing for the realization of high-performance all-optical switches and diodes. Our findings may open up new prospects for the nonlinear topological photonic systems with classical analogue–quantum phenomena. Full article

High-resolution optical sensing typically relies on complex, high-finesse interferometers, limiting the scalability and cost-effectiveness of extreme-precision metrology. We propose a simple, compact alternative: a metallic-boundary waveguide containing a single-point dielectric impurity, operated near its cutoff frequency. This device achieves ultra-high spectral resolution by exploiting Fano resonance, arising from the quantum–optical interference between the waveguide’s continuous modes and a quasi-bound state induced by the local impurity. For analytical modeling, we employ the Impurity D Function (IDF), an approach previously confined to quantum mechanical scattering, demonstrating its first application in an integrated optical system. Our analysis shows that the spectral resolution ($\Re$) scales powerfully with the geometry, specifically $\Re ~{\left(\mathrm{\epsilon }/w\right)}^{-12}$, where $\mathrm{\epsilon }/w$ is the impurity-to-waveguide ratio. This translates directly into an extremely sensitive strain gauge, with transmission linearity $T_{11}=1/2+\Re \mathrm{\eta }$ near the 50% working point ($\mathrm{\eta }$ is the mechanical strain). We calculate that for a practical ratio of $\mathrm{\epsilon }/w\cong 1\%$, the device yields a resolution of $\Re ~{10}^{20}$, confirming its potential to measure mechanical strains smaller than $\mathrm{\eta }~{10}^{-21}$ using a fundamentally simple, integrated platform. Full article

Ceftobiprole is a novel and promising antibiotic; however, the direct pharmacological use of its native form is limited by its low water solubility. The first part of this study provides a deeper insight into the physicochemical properties of this drug. One- and two-dimensional nuclear magnetic resonance (NMR) spectra in D₂O were recorded, and a complete assignment of ¹H and ¹³C signals was achieved with the support of quantum mechanical calculations. The combined results from capillary electrophoresis and NMR confirmed the cationic nature of ceftobiprole at pH values well below 3 and the protonation of the secondary amino group, thus supporting the theoretically predicted dominant protonation states. Molecular dynamics simulations revealed that zwitterionic ceftobiprole molecules associate through hydrogen bonding, whereas in the cationic form, the attractive forces involve weaker π-π and stacking interactions. The use of ceftobiprole in its native form in pharmaceutical formulations was made possible through the development of a novel freeze-dried cyclodextrin-based delivery system. Consequently, the second part of this article focuses on evaluating the biological properties of this system (ceftobiprole/maleic acid/sulfobutylether-β-cyclodextrin in a molar ratio of 1:25:4), including its antibacterial activity against the most common pneumonia-causing pathogens and its cytotoxicity towards normal and cancer cells. Full article

Thanks to both endogenous and exogenous antioxidants (AOs), the antioxidant defense system ensures redox homeostasis, which is crucial for protecting the body from oxidative stress and maintaining overall health. The food industry also exploits the antioxidant properties to prevent or delay the oxidation of other molecules during processing and storage. There are many classical methods for assessing antioxidant capacity/activity, which are based on mechanisms such as hydrogen atom transfer (HAT), single electron transfer (SET), electron transfer with proton conjugation (HAT/SET mixed mode assays) or the chelation of selected transition metal ions (e.g., Fe²⁺ or Cu¹⁺). The antioxidant capacity (AOxC) index value can be expressed in terms of standard AOs (e.g., Trolox or ascorbic acid) equivalents, enabling different products to be compared. However, there is currently no standardized method for measuring AOxC. Nanoparticle sensors offer a new approach to assessing antioxidant status and can be used to analyze environmental samples, plant extracts, foodstuffs, dietary supplements and clinical samples. This review summarizes the available information on nanoparticle sensors as tools for assessing antioxidant status. Particular attention has been paid to nanoparticles (with a size of less than 100 nm), including silver (AgNPs), gold (AuNPs), cerium oxide (CeONPs) and other metal oxide nanoparticles, as well as nanozymes. Nanozymes belong to an advanced class of nanomaterials that mimic natural enzymes due to their catalytic properties and constitute a novel signal transduction strategy in colorimetric and absorption sensors based on the localized surface plasmon resonance (LSPR) band. Other potential AOxC sensors include quantum dots (QDs, <10 nm), which are particularly useful for the sensitive detection of specific antioxidants (e.g., GSH, AA and baicalein) and can achieve very good limits of detection (LOD). QDs and metallic nanoparticles (MNPs) operate on different principles to evaluate AOxC. MNPs rely on optical changes resulting from LSPR, which are monitored as changes in color or absorbance during synthesis, growth or aggregation. QDs, on the other hand, primarily utilize changes in fluorescence. This review aims to demonstrate that, thanks to its simplicity, speed, small sample volumes and relatively inexpensive instrumentation, nanoparticle-based AOxC assessment is a useful alternative to classical approaches and can be tailored to the desired aim and analytes. Full article

Breast cancer is one of the most prevalent malignant tumors worldwide, with the highest incidence and mortality among women. Early precise diagnosis and the development of efficient treatment regimens remain major clinical challenges. Harnessing the programmable size, surface chemistry, and tumor microenvironment (TME) responsiveness of nanomaterials, there is tremendous potential for their applications in breast cancer diagnosis and therapy. In the diagnostic arena, nanomaterials serve as core components of novel contrast agents (e.g., gold nanorods, quantum dots, superparamagnetic iron oxide nanoparticles) and biosensing platforms, substantially enhancing the sensitivity and specificity of molecular imaging modalities—such as magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging (FLI)—and enabling high-sensitivity detection of circulating tumor cells and tumor-derived exosomes, among various liquid biopsy biomarkers. In therapy, nanoscale carriers (e.g., liposomes, polymeric micelles) improve tumor targeting and accumulation efficiency through passive and active targeting strategies, thereby augmenting anticancer efficacy while effectively reducing systemic toxicity. Furthermore, nanotechnology has spurred the rapid advancement of emerging modalities, including photothermal therapy (PTT), photodynamic therapy (PDT), and immunotherapy. Notably, the construction of theranostic platforms that integrate diagnostic and therapeutic units within a single nanosystem enables in vivo, real-time visualization of drug delivery, treatment monitoring, and therapeutic response feedback, providing a powerful toolkit for advancing breast cancer toward personalized, precision medicine. Despite challenges that remain before clinical translation—such as biocompatibility, scalable manufacturing, and standardized evaluation—nanomaterials are undoubtedly reshaping the paradigm of breast cancer diagnosis and treatment. Full article

This article presents a comprehensive overview of the current and emerging roles of cryogenics and superconductivity in medical diagnostics, imaging, and therapy. Beginning with the historical foundations of both fields and their technological maturation, this review emphasizes how cryogenic engineering and superconducting materials have become indispensable to modern medical systems. Cryogenic technologies are highlighted in applications such as cryosurgery, cryotherapy, cryostimulation, and cryopreservation, all of which rely on controlled exposure to extremely low temperatures for therapeutic or biological preservation purposes. This article outlines the operating principles of cryomedical devices, the refrigerants and cooling methods used, and the technological barriers. This paper reviews the latest applications of superconductivity phenomena in medicine and identifies those that could be used in the future. These include cryogenic therapy, radiotherapy (cyclotrons, particle accelerators, synchrotron radiation generation, isotope production, and proton and ion beam delivery), magnetic resonance imaging (MRI), nuclear magnetic resonance spectroscopy (NMR), positron emission tomography (PET), and ultra-sensitive magnetic signal transducers based on SQUIDs for detecting ultra-low bio-signals emitted by human body organs. CT, MRI/NMR, and PET features are compared using the operation principle, specific applications, safety, contraindications for patients, examination time, and additional valued peculiarities. This article outlines the prospects for the development of superconducting and cryogenic materials and technologies in medical applications. Advances in diagnostic imaging are reviewed, with particular attention on the progression from conventional MRI scanners to ultra-high-field (UHF) systems exceeding 7–10.5 T, culminating in the 11.7 T Iseult whole-body MRI magnet. Another important application area described in this article includes biofunctionalized magnetic nanoparticles and superconducting quantum interference devices (SQUIDs), which enable the ultrasensitive detection of biomagnetic fields and targeted cancer diagnostics. Finally, this article identifies future directions of development in superconducting and cryogenic technologies for medicine. Full article

Fluorescent proteins find application as biocompatible, genetically encoded labels for visualization of living organisms tissues. Green fluorescent proteins (GFPs) are the most diverse, but proteins with red fluorescence have advantages, such as lower phototoxicity and better penetration into biological tissues. A promising approach is to obtain red fluorescent proteins (RFPs) from GFPs by introducing mutations that stabilize the oxidized chromophore state with an extended conjugated π-system. However, to date this remains a non-trivial task and experimental developments are carried out mainly by random mutagenesis. Development of descriptors obtained in molecular modeling can rationalize this field. Herein, we rely on experimental data on the AzamiGreen fluorescent protein and its variants that are oxidized to the red form. We perform classical molecular dynamics (MD) and combined quantum mechanics/molecular mechanics (QM/MM) simulations to determine structural and dynamic features that govern oxidation. We demonstrate that the red state is predominantly stabilized by interactions of polar lysine residues with chromophore oxygen atoms. Dynamic network analysis demonstrates that in red fluorescent proteins the chromophore motions are correlated with the movement of surrounding protein side chains to a higher extent than in green variants. The presence of different resonance forms of the chromophore determines the fluorescence band maximum value: a decrease in the phenolate form population leads to the red shift. Full article

Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves $0.021 \mathrm{ng}/\mathrm{mL}$ detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach $0.17 \mathrm{nM}$ sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains $0.2 \mathrm{pM}$ sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at $0.0011 \mathrm{mg}/\mathrm{L}$ detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Full article

A nested semi-tube hollow-core anti-resonant fiber (HC-ARF) that can support the high-purity transmission of a few polarization-maintaining modes is designed in this paper. By employing bi-thickness hybrid silica/silicon anti-resonant tubes, the birefringence of the orthogonal polarized modes is significantly improved, and the weak coupling condition of the five lowest-order polarization maintaining modes, including the LP_{01_x}, LP_{01_y}, LP_{11a_x}, LP_{11b_x}, and LP_{11a_y}, can be met. The effective refractive index difference between each pair of the supported adjacent modes is larger than 1.0 × 10⁻⁴. With hybrid multi-layer nested semi-tubes, the confinement losses of the supported modes are all less than 1.50 × 10⁻¹ dB/m within a transmission band from 1.530 to 1.620 μm. The minimum confinement losses of the LP_{01_y}, LP_{01_x}, LP_{11a_y}, LP_{11a_x}, and LP_{11b_x} modes are 3.71 × 10⁻⁴ dB/m, 1.61 × 10⁻³ dB/m, 2.00 × 10⁻² dB/m, 1.30 × 10⁻¹ dB/m, and 4.20 × 10⁻² dB/m, respectively. Meanwhile, the unwanted higher-order modes are filtered out well to guarantee the modal purity. The minimum higher-order-mode extinction ratio of the lowest-loss LP₂₁ mode to the highest-loss LP₁₁ mode remains larger than 139 from 1.545 to 1.615 μm. The numerical results highlight the potential of the proposed polarization-maintaining few-mode hollow-core anti-resonant fibers in many application fields, such as short-range and high-capacity data transmission networks, fiber sensing systems, quantum communication systems, and so on. Full article

Single-molecule detection (SMD) has reformed analytical science by enabling the direct observation of individual molecular events, thus overcoming the limitations of ensemble-averaged measurements. This review presents a comprehensive analysis of the principles, devices, and emerging materials that have shaped the current landscape of SMD. We explore a wide range of sensing mechanisms, including surface plasmon resonance, mechanochemical transduction, transistor-based sensing, optical microfiber platforms, fluorescence-based techniques, Raman scattering, and recognition tunneling, which offer distinct advantages in terms of label-free operation, ultrasensitivity, and real-time responsiveness. Each technique is critically examined through representative case studies, revealing how innovations in device architecture and signal amplification strategies have collectively pushed the detection limits into the femtomolar to attomolar range. Beyond the sensing principles, this review highlights the transformative role of advanced nanomaterials such as graphene, carbon nanotubes, quantum dots, MnO₂ nanosheets, upconversion nanocrystals, and magnetic nanoparticles. These materials enable new transduction pathways and augment the signal strength, specificity, and integration into compact and wearable biosensing platforms. We also detail the multifaceted applications of SMD across biomedical diagnostics, environmental monitoring, food safety, neuroscience, materials science, and quantum technologies, underscoring its relevance to global health, safety, and sustainability. Despite significant progress, the field faces several critical challenges, including signal reproducibility, biocompatibility, fabrication scalability, and data interpretation complexity. To address these barriers, we propose future research directions involving multimodal transduction, AI-assisted signal analytics, surface passivation techniques, and modular system design for field-deployable diagnostics. By providing a cross-disciplinary synthesis of device physics, materials science, and real-world applications, this review offers a comprehensive roadmap for the next generation of SMD technologies, poised to impact both fundamental research and translational healthcare. Full article