Search Results (375)

Background and Clinical Significanc: Neuropathic corneal pain is a debilitating condition characterized by ocular pain disproportionate to clinical signs, often resulting from peripheral and central sensitization of the corneal somatosensory pathway. Emerging evidence suggests that chronic involuntary muscle contraction in blepharospasm may lead to irritation of trigeminal afferents and corneal neurogenic inflammation, potentially predisposing patients to neuropathic corneal pain. Given its debilitating nature, early recognition can prevent the progression of neuropathic sequelae. This study examines the potential role of blepharospasm as a predisposing factor contributing to neuropathic corneal pain. Case Presentation: This retrospective case series describes three cases (median age: 50 years) of neuropathic corneal pain in association with blepharospasm and their clinical course following multimodal treatment over a median follow-up period of one year. Ocular surface was evaluated using slit-lamp biomicroscopy, while corneal nerve structure and morphology were assessed with in vivo confocal microscopy. All the three subjects presented with minimal ocular surface staining but disproportionate ocular pain characterized by burning sensation and photophobia. Proparacaine challenge testing was performed to determine the subtype of neuropathic corneal pain. Pain symptoms and quality of life were evaluated using the Ocular Pain Assessment Survey and Ocular Surface Disease Index questionnaires. In vivo confocal microscopy demonstrated characteristic corneal nerve abnormalities including reduced corneal nerve density, increased nerve tortuosity, and the presence of microneuromas. Treatment included oral Pregabalin or Gabapentin, topical lubricants, Cyclosporine 0.05% (1 case), and 20% autologous serum eye drops (1 case). Two of the three cases received four to five injections of botulinum toxin for blepharospasm, whereas one had undergone a single injection prior to review. All patients also received weekly periorbital quantum molecular resonance electrotherapy for two months. Improvements were observed across multiple domains of the Ocular Pain Assessment Survey and Ocular Surface Disease Index evaluation, including ocular pain, photophobia, non-ocular pain, and quality-of-life measures following multimodal treatment. The co-existence of blepharospasm and neuropathic corneal pain observed in our cases supports a possible association between chronic periocular muscle hyperactivity and corneal nociceptor sensitization. Proposed mechanisms include chronic trigeminal nerve irritation, neurogenic inflammation, and sensitization mediated by pro-inflammatory neuropeptides. Multimodal treatment targeting both motor hyperactivity and neuropathic pain pathways appeared to provide symptomatic relief, including the use of quantum molecular resonance electrotherapy, which might modulate pain pathways, block nociceptor neurotransmission, and accelerate corneal nerve regeneration. Given the complexity of the neural pathways responsible for ocular discomfort, further studies are required to elucidate the relationship between neuropathic corneal pain and blepharospasm in larger cohorts, as well as refine existing therapeutic approaches, including evaluating the therapeutic role of electrotherapy. Conclusions: Blepharospasm may represent a potential predisposing factor of neuropathic corneal pain. Early recognition and concurrent treatment of blepharospasm and neuropathic corneal pain can effectively relieve symptoms and improve quality of life. Adopting a multimodal treatment approach is therefore recommended. Full article

Metal oxide nanoparticles serve as crucial drivers in modern biomedical, catalytic, environmental, and energy technologies due to their high surface-to-volume ratios and quantum confinement properties. Traditional chemical and physical synthesis methods remain limited by significant energy footprints, high costs, and the use of hazardous reagents. To address these challenges, bioinspired (“green”) synthesis has emerged as a sustainable paradigm that employs biological systems as nature nanofactories. This critical review provides a provides a comprehensive and systematic analysis of the green synthesis of major metal oxide systems (ZnO, TiO₂, Fe₃O₄/Fe₂O₃, CuO, Co₃O₄, CeO_2, and MnO₂) using diverse biological templates, including plant extracts, bacteria, fungi, algae, and biopolymers. Moving beyond simple descriptive summaries, we critically evaluate the foundational electron-transfer and nucleation mechanism, systematically correlate processing parameters with physical outcomes, and offer a rigorous comparative analysis across different biological kingdoms. Finally, we directly address the underlying challenges facing the field: reproducibility bottlenecks, scalability limits, environmental safety variations, and regulatory hurdles necessary for industrial translation. Full article

We propose an exploratory framework for a Bohmian model of quantum matter propagating on a classical curved spacetime background. The gravitational sector is governed by classical Einstein field equations throughout; no quantisation of spacetime is attempted. The wave function emerges as the scalar contraction $\mathrm{\Psi }={\psi }_{\nu }{\psi }^{\nu }\in \mathbb{C}$ of a complex-valued tensorial field ${\psi }^{\mu }$, encoding quantum dynamics in a geometric object. The wave tensor interacts with spacetime via the stress–energy tensor $T_{\mu \nu }$, mediated by a real scalar field a of dimension volume, so that $a{T}_{\mu \nu }{\psi }^{\mu }{\psi }^{\nu }$ yields the correct potential energy. We derive a covariant Adapted Schrödinger Equation as the unique minimal covariant lift of the standard equation, justify it from four guiding principles, and verify three internal consistency checks. Under seven explicit approximations the framework reproduces the Schrödinger equation with Coulomb potential for the hydrogen atom. We also derive a dynamical equation for ${\psi }^{\mu }$ that entails the Adapted Schrödinger Equation by contraction. Two open problems are then resolved. First, a complete Lagrangian formulation is provided: a real-valued action for $\mathrm{\Psi }$ yields the Adapted Schrödinger Equation via the Euler–Lagrange equations; a separate action for ${\psi }^{\mu }$, extended by a non-polynomial term, yields the full dynamical equation variationally. Second, two experimental predictions are derived. Expanding to first post-Newtonian order, the perturbation Hamiltonian has coefficients $(3, 1)$ on the kinetic and potential operators; via the virial theorem these produce a coordinate-time blueshift, which after photon propagation yields the universal Einstein gravitational redshift $\delta \nu /\nu =\mathrm{\Phi }/c^2$, confirming consistency with the equivalence principle. The same kinetic coefficient independently predicts that free quantum wave packets spread more slowly by the fractional amount $3\left|\mathrm{\Phi }\right|/c^2$, a correction absent in standard non-relativistic quantum mechanics. Full article

Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era. Full article

Quantitative ¹H NMR (qNMR) is a versatile analytical tool that provides simultaneous structural and quantitative information without the need for analyte-specific standards. This review summarizes its key methodological fundamentals and broad applications in both pharmaceutical and biomedical analysis. In drug analysis, qNMR enables content determination and purity assessment of small molecules, polysaccharides and glycoconjugates, synthetic polymers, and complex herbal medicines. In biomedical analysis, it serves as a powerful platform for metabolomics profiling, real-time monitoring of cellular processes, and absolute quantification of metabolites in biofluids and tissues. Recent and emerging technological advancements, including hyperpolarization, quantum mechanical spectral analysis, artificial intelligence, and deep learning, hold great promise for further enhancing sensitivity, resolving power, and automation. With ongoing integration into pharmacopoeial standards and regulatory frameworks, qNMR is poised to expand its role in both routine quality control and cutting-edge biomedical research. Full article

The field of infrared (IR) photodetection is undergoing rapid development through the emergence of solution-processable nanoparticle (NP)-based materials and fabrication strategies. This review critically examines recent advances in fabrication approaches for NP-based IR detectors, emphasizing the relationship between synthesis, surface engineering, deposition processes, and device architecture in determining detector performance. Representative material platforms are discussed, including colloidal quantum dots (CQDs) such as PbS and HgTe, which enable tunable operation from the near-infrared (NIR) and short-wave infrared (SWIR) to selected mid-wave (MWIR), long-wave (LWIR), and emerging very-long-wave infrared (VLWIR) regimes depending on material composition and operating conditions. Further platforms including plasmonic metal NPs, black phosphorus, and topological nanomaterials are evaluated for their unique mechanisms of optical enhancement and broadband response. Fabrication approaches including continuous-flow synthesis, ligand exchange, blade coating, inkjet printing, electrophoretic deposition, and other scalable solution-processing methods are analyzed with respect to their influence on film quality, charge transport, interface engineering, and integration compatibility. The review further compares major device architectures, including photoconductors, photodiodes, plasmonic absorbers, and phototransistors, using key performance metrics such as specific detectivity (D*), responsivity (R), response speed, and operating temperature, while emphasizing the importance of measurement conditions in cross-platform comparisons. Critical challenges including dark-current generation, 1/f noise, transport limitations associated with ligand chemistry, environmental instability of narrow-bandgap materials, manufacturability constraints, and toxicity considerations are also discussed. Emerging directions such as neuromorphic sensing, CMOS-compatible integration, and sustainable lead-free nanomaterials are highlighted. By linking nanoscale material design and fabrication processes to device-level performance, this review provides a framework for advancing NP-based IR technologies toward scalable and application-relevant sensing systems. Full article

Irradiation can impact the properties of semiconductor materials and the function of microelectronic devices. In the present review, we consider how irradiation interacts with semiconductor materials important, primarily silicon (Si), focusing on the defect processes. These, in turn, will have an impact on the physical properties of the material and can impact important properties for devices such as the electrical conductivity and mechanical integrity. We consider the ways that irradiation impacts the operation of microelectronic devices. We thereafter review the defect engineering strategies and other ways to mitigate against the impact of irradiation in devices. Finally, we consider the potentially important role of irradiation defects as qubits in the emerging quantum technologies. Full article

The Internet of Vehicles (IoV) is rapidly emerging as a core component of intelligent transportation systems, enabling real-time communication among vehicles, infrastructure, and cloud platforms. However, the increasing interconnectivity of vehicular systems and the advancement of quantum computing introduce significant security challenges to existing cryptographic mechanisms. Conventional schemes such as RSA and Elliptic Curve Cryptography (ECC) are vulnerable to quantum attacks and are computationally inefficient for resource-constrained vehicular environments. To address these limitations, this paper proposes a Double-Ring Hybrid Sparse NTRU (DRH-SNTRU) framework, a lightweight and quantum-resistant cryptographic scheme for secure IoV communication. The proposed framework introduces three key enhancements: (i) controlled-support sparse polynomial structures to reduce polynomial multiplication complexity while improving entropy distribution; (ii) a double-ring algebraic architecture that separates key operations from message processing to enhance structural security and minimize coefficient leakage; and (iii) hybrid ephemeral keys derived from contextual entropy to strengthen forward secrecy and adaptive security. An optional ciphertext evaluation mechanism is further incorporated to detect malformed and replayed ciphertexts prior to decryption. Security analysis demonstrates that the proposed framework achieves IND-CPA security under the hardness assumption of the NTRU lattice problem and can be extended to resist chosen-ciphertext attacks through the integrated validation mechanism. Experimental benchmarking across polynomial dimensions N = 512 to 8192 demonstrates that DRH-SNTRU achieves low setup overhead below 3 μs, efficient decryption latency of approximately 305.64 μs at N = 8192, and compact sparse private key representation of only 117 bytes at higher dimensions. Compared with Standard NTRUEncrypt, NTRU-HRSS, and Ring-LWE Encryption, the proposed framework demonstrates improved decryption efficiency, lightweight storage overhead, and enhanced ciphertext integrity protection while maintaining practical scalability for resource-constrained post-quantum IoV environments. Full article

Carbon dots (CDs), including carbon quantum dots (CQDs), are ultra-small carbon-based nanomaterials, typically below 10 nm, with tunable photoluminescence, high aqueous dispersibility, favorable biocompatibility, low toxicity, and abundant surface functional groups. These properties make CDs promising multifunctional platforms for nanomedicine, particularly in bioimaging, biosensing, targeted drug/gene delivery, photodynamic therapy (PDT), photothermal therapy (PTT), antimicrobial treatment, and theranostic applications. This review critically examines recent advances in CD fabrication, including top-down, bottom-up, green biomass-derived, microwave-assisted, hydrothermal, and emerging hybrid strategies, with emphasis on how precursor selection, heteroatom doping, surface passivation, and polymer/ligand functionalization regulate optical performance, biological interaction, and therapeutic efficiency. The review discusses structural classification, including CQDs, graphene quantum dots (GQDs), carbon nanodots, and carbonized polymer dots (CPDs), together with major characterization approaches such as ultraviolet–visible (UV–Vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and high-resolution transmission electron microscopy (HRTEM). Particular attention is given to red/near-infrared (NIR) emission, renal clearance, drug-loading behavior, reactive oxygen species (ROS) generation, toxicity mechanisms, biodistribution, and long-term biosafety. This review also highlights key translational barriers, including batch-to-batch variability, limited standardization, scalable manufacturing, regulatory uncertainty, and incomplete pharmacokinetic evaluation. It considers artificial intelligence (AI) and machine learning (ML) as emerging tools for reproducible CD design. CDs represent versatile and clinically promising nanoplatforms, but their translation requires standardized synthesis, rigorous safety assessment, and application-specific regulatory validation. Full article

The rapid growth of digital communication and large-scale data exchange has increased the demand for advanced cryptographic techniques capable of resisting emerging computational threats. Conventional encryption methods primarily rely on mathematical complexity, which may become vulnerable with the advancement of high-performance computing and future quantum technologies. Biological molecules such as deoxyribonucleic acid (DNA) and RiboNucleic Acid (RNA) provide unique properties, including extremely high storage density, massive parallelism, and complex nucleotide structures that can inspire novel cryptographic mechanisms. This study proposes a bio-inspired cryptographic framework that integrates DNA encoding and RNA-based transformations to enhance data security. In the proposed framework, digital information is first converted into binary format and mapped to nucleotide sequences using a predefined encoding scheme. The encryption process incorporates multiple molecular transformations, including complementary base pairing, sequence permutation, and transcription-inspired DNA-to-RNA conversion to generate a highly randomized ciphertext. Decryption reverses these transformations to reconstruct the original plaintext. Security evaluation demonstrates that the proposed framework produces high entropy outputs, a substantially large key space, and enhanced resistance to statistical and brute-force attacks. The results indicate that DNA and RNA-inspired cryptographic systems can substantially enhance encryption complexity while maintaining reliable data recovery. This research highlights the potential of molecular cryptography as a promising interdisciplinary approach for future secure communication and biological data storage systems. Full article

Edge computing has emerged as a distributed computing technology to mitigate the cloud computing overload caused by the rapid increase in connected devices. However, because communications between devices and edge servers are conducted over public channels, authentication and secure session key establishment are imperative to protect against various security attacks. In this paper, we show that Kenioua et al.’s authentication scheme for edge computing is vulnerable to several attacks such as impersonation, offline password guessing, and stolen verifier attacks, and also lacking quantum resistance against the emerging threat posed by quantum computing. To overcome these limitations, we propose a quantum-resistant authentication scheme by adopting module lattice-based key encapsulation mechanism (ML-KEM). We demonstrate the robustness of the proposed scheme through “the Burrows–Abadi–Needham (BAN) logic”, “Quantum Random Oracle Model (QROM)”, “Automated Validation of Internet Security Protocols and Application (AVISPA) tool”, and “Scyther tool”, and show that the proposed scheme achieves security with efficient communication and computation costs by comparing it with related studies. Full article

Negative capacitance (NC) has been reported across a wide range of physical systems, yet its interpretation has remained fragmented due to the lack of a unified conceptual framework. Existing explanations—spanning ferroelectric free-energy curvature, tunneling transport, plasmonic resonances, and electronic compressibility—have often been treated as unrelated or even contradictory. This review resolves these inconsistencies by showing that all manifestations of NC arise from non-synchronization between external excitation and internal response. We classify NC into three fundamental categories: temporal mismatch, originating from delays or inertia in charge or polarization dynamics; spatial mismatch, caused by nonuniform field or mode distributions; and quantitative mismatch, resulting from intrinsic parameter reversal such as negative curvature or negative compressibility. Despite their diverse physical origins, these mechanisms share the same mathematical signature (${\mathrm{C}}_{eff}=\partial \mathrm{Q}/\partial \mathrm{V}<0$). Organizing NC within this unified framework clarifies long-standing ambiguities, connects previously isolated research fields, and establishes a systematic foundation for engineering NC in electronic, photonic, and quantum devices. The framework further highlights tunnel-current-induced NC as a representative single-particle mechanism within the temporal mismatch category, expanding the scope of NC beyond ferroelectricity and collective modes. Overall, this work positions NC not as a singular anomaly but as a universal response class emerging from the interplay between excitation and internal dynamics. Full article

Quantum computing is increasingly seen as a disruptive technology capable of expanding the computational limits of advanced manufacturing systems within the emerging Industry 5.0 framework. By utilizing quantum mechanical principles such as superposition, entanglement, and quantum parallelism, quantum computation enables new approaches to solving complex optimization, simulation, and data-intensive problems that are challenging or impractical for classical computers. This paper offers a comprehensive and critical review of the potential impacts of quantum computing on advanced manufacturing, focusing on intelligent production planning, supply chain optimization, materials discovery, predictive maintenance, and human–machine collaboration, key aspects of Industry 5.0. The originality of this review lies in its integrated analysis of quantum computing alongside artificial intelligence, digital twins, and cyber–physical systems, highlighting how these technologies, when combined, improve decision-making speed, process efficiency, and sustainability. Despite these opportunities, the integration of quantum computing into Industry 5.0 systems faces critical challenges, including hardware limitations, algorithm scalability, data security concerns, workforce readiness, and the complexity of integrating quantum solutions with existing industrial infrastructures. The role of hybrid quantum-classical architectures is examined as a feasible and transitional approach for near-term manufacturing applications. By critically assessing both technological strengths and practical constraints, this review positions quantum computing as a promising enabler of resilient, human-centered, and sustainable manufacturing ecosystems. The insights aim to assist researchers, industry players, and policymakers in strategically managing the integration of quantum technologies as manufacturing systems advance toward Industry 5.0. Full article

Modelling plays a critical role in many engineering applications. Partial differential equations (PDEs) are ubiquitous, describing various physical phenomena such as fluid flow, electromagnetism, and quantum mechanics. Although some of these equations have analytical solutions, many require high-fidelity simulations of parametric PDEs. In general, high-fidelity simulations are computationally expensive and often infeasible for real-time or multi-query applications. This challenge has led to the development of reduced-order models (ROMs). Over the past few decades, ROMs have emerged as a practical solution for simulating, controlling, and optimizing large-scale and complex dynamical systems. This paper introduces a novel Calibrated Intrusive Reduced-Order Modelling (CIROM) approach for the efficient and accurate simulation of the one-dimensional Burgers’ equation, employed as a canonical benchmark because it is a simplified fundamental partial differential equation that captures the behaviour of many real-world phenomena. The proposed method, combining the strengths of proper orthogonal decomposition (POD) and long short-term memory (LSTM) networks, effectively reduces computational complexity while addressing inherent instabilities in classical reduced-order models. Unlike traditional POD-ROMs, which often suffer from error accumulation and instability at high Reynolds numbers, the CIROM employs an iterative LSTM-based error correction mechanism to learn and compensate for truncation and projection errors. This study is benchmark-oriented and does not aim to provide a general PDE solver. The performance of the proposed method is rigorously evaluated across a broad range of Reynolds numbers, including interpolation and extrapolation scenarios, demonstrating robust extrapolation within moderate ranges. Comprehensive numerical experiments confirm that the CIROM outperforms both pure intrusive ROMs and purely data-driven LSTM models in terms of prediction accuracy, stability, and computational cost. Full article

Chagas disease remains a major neglected parasitic illness in Latin America and other endemic regions, and benznidazole (BZN) is still the primary trypanosomacidal drug despite its incompletely understood mechanism of action. This work provides a detailed biophysical characterization of the conformational behavior and vibrational properties of benznidazole (BZN), a first-line trypanocidal drug still widely used for the treatment of Chagas disease. Using density functional theory combined with relaxed potential energy surface scans in vacuum and implicit water, two low-energy conformers (BZN1 and BZN2) were identified, separated by moderate rotational barriers and a small energy difference, indicating that both are intrinsically accessible at room temperature. For each conformer, infrared and Raman spectra were calculated and assigned via vibrational mode analysis, then compared with FT-IR and FT-Raman spectra recorded for pharmaceutical-grade polycrystalline BZN. The theoretical and experimental spectra show excellent agreement, with a Raman band in the 1350–1400 $\mathrm{c}{\mathrm{m}}^{-1}$ region emerging as a sensitive conformational marker: the experimental maximum at $1359\mathrm{c}{\mathrm{m}}^{-1}$ matches the most intense BZN1 mode, whereas the corresponding BZN2 band appears about $13\mathrm{c}{\mathrm{m}}^{-1}$ higher in frequency. This clear spectroscopic fingerprint demonstrates that the solid drug is overwhelmingly composed of the BZN1 conformer, despite the theoretical accessibility of BZN2. Overall, the study links the conformational landscape of benznidazole to its vibrational signatures and highlights Raman spectroscopy, supported by quantum chemical calculations, as a powerful tool for conformational and potential polymorphic control of this clinically important nitroimidazole. Full article