Search Results (96)

Large organizations face a critical systems integration challenge when executing multiple concurrent security modernization programs. This paper examines the U.S. Department of Defense’s simultaneous implementation of three transformational initiatives—post-quantum cryptography (PQC) migration, Zero Trust Architecture (ZTA) deployment, and AI security assurance—each operating under separate governance structures, timelines, and compliance frameworks. Through systematic evidence synthesis of 59 sources (47 policy/standards documents and 12 performance benchmarks), we identify cross-program dependencies that create integration failures when programs operate in isolation. We propose a shared modernization substrate—a four-layer infrastructure architecture (Cryptographic Services, Identity Management, Analytics Pipeline, Policy Orchestration) that enables coordinated execution while preserving program independence. The framework addresses the fundamental systems challenge of achieving interoperability across programs with misaligned schedules and competing resource demands. We introduce a five-level Triad Convergence Maturity Model (TCMM) with operationalized indicators enabling repeatable organizational assessment. Illustrative application to three DoD modernization contexts demonstrates the framework’s ability to differentiate maturity levels. Performance analysis synthesizes published benchmark data: enterprise PQC latency overhead is modest (measured), while tactical environment estimates of 158–383% overhead are derived from benchmark extrapolation under packet-loss assumptions (modeled). Scenario modeling suggests that coordinated incident response through the substrate architecture could substantially reduce risk exposure windows compared to siloed approaches (modeled). The framework transforms fragmented program execution into synchronized systems modernization, offering practical guidance for chief information officers, program managers, and enterprise architects managing concurrent technology transitions. Full article

We systematically investigate semiclassical dynamics of the optical field produced by quantum nanoemitters (NEs) embedded in a periodic lattice of conducting nanorings (NRs), in which plasmon polaritons (PPs) are excited. The coupling between PPs and NEs through the radiated optical field leads to establishment of a significant cross-correlation between NEs, so that their internal dynamics (photocurrent affected by the laser irradiation) depends on the NR’s plasma frequency ${\omega }_p$. The transition to this regime, combined with the nonlinearity of the system, leads to a quite increase in the photocurrent in the NEs, as well as to non-smooth (chimera-like or chaotic) behavior in the critical (transition) region, where considerably small variations in ${\omega }_p$ lead to significant changes in the level of the NE pairwise cross-correlations. The chimera-like state is realized as coexistence of locally synchronized and desynchronized NE dynamical states. A fit of the dependence of the critical current on ${\omega }_p$ is found, being in agreement with results of numerical simulations. The critical effect may help to design new optical devices, using dispersive nanolattices which are made available by modern nanoelectronics. Full article

We present a conceptual and computational investigation of vacuum memory within a discrete toy-model framework. In this phenomenological approach, we introduce an effective memory field that records virtual events and nonlocal couplings on a lattice, without claiming to derive a fundamental new field of nature. Using a discrete toy model, we simulate memory formation via virtual events, nonlocal links, and black-hole-like information sinks. The resulting dynamics exhibit long-range spatial correlations, curvature-induced accumulation, high-entropy retention zones, and distinct spectral features, indicating that the modeled memory field can store and organize information in a vacuum-like medium. Building on this foundation, we incorporate curvature-modulated vacuum memory fields into Bohmian particle dynamics. By varying the memory coupling strength λ, we demonstrate that memory gradients systematically bend particle trajectories toward curvature centers, illustrating an active role for structured memory in guiding quantum-like motion. We further show that when vacuum memory encodes the full quantum phase S(x, t) and particles are guided by the Bohmian relation $\dot{x}=m^{-1}{\partial }_{x}S$, the trajectories collapse onto a single path with machine-level precision, providing a numerical consistency check that our implementation reproduces exact pilot-wave guidance and minimal-action dynamics. Through a minimal two-site entangled-memory model, we demonstrate that coupled memory fields—without explicit particle dynamics—can spontaneously synchronize via weak informational coupling, generating robust nonlocal correlations reminiscent of entanglement. Finally, we simulate two-slit interference under vacuum memory perturbations. While random, unstructured memory preserves quantum coherence and fringe visibility, structured, phase-sensitive memory induces dephasing and suppresses interference, functioning as a phenomenological decoherence mechanism. Together, these results situate our toy model within emerging information-based views of quantum dynamics and spacetime, offering a computational platform and conceptual lens for exploring the informational dynamics of a vacuum-like medium. Full article

The solar-driven direct conversion of methanol to ethylene glycol, formaldehyde and simultaneous H₂ generation is an appealing strategy for converting sunlight to chemical energy. However, the low efficiency and stability of the photocatalyst remain critical bottlenecks hindering the practical implementation of this reaction. Herein, we synthesized the Cu₃P quantum dots/Cu-doped ZnIn₂S₄ p-n junction for efficient methanol oxidation and synchronous H₂ generation. The highly dispersed Cu₃P quantum dots promote electron–hole separation and furnish abundant catalytic sites. Moreover, the constructed p-n junction with a tight interface boosts the electron transfer, avoiding the serious photocorrosion of ZnIn₂S₄. Benefiting from these synergistic effects, the 2Cu₃P/Cu_0.5ZIS composite exhibits the highest photocatalytic conversion efficiency of methanol, yielding H₂, formaldehyde, and ethylene glycol with 10.34 mmol·g⁻¹·h⁻¹, 10.35 mmol·g⁻¹·h⁻¹ and 8.84 mmol·g⁻¹·h⁻¹ yields, which are 3.01, 3.05 and 3.10 times those of pure ZnIn₂S₄, respectively. A series of characterizations including X-ray diffraction, X-ray photoelectron spectroscopy, transmission electron microscopy and UV-Vis diffuse reflectance spectroscopy are employed to analyze the structure, composition, and photoelectrochemical properties of the materials. This work demonstrates a novel catalyst design paradigm for the high-efficiency solar light-driven photocatalytic activation of methanol enabling the co-production of value-added C1/C2 oxygenates and clean H₂ fuel simultaneously. Full article

The emergence of quantum computing threatens the security of classical cryptographic algorithms such as RSA and ECC. Post-quantum cryptography (PQC) offers mathematically secure alternatives, but migration is a complex, multi-year undertaking. Unlike past transitions (AES, SHA-2, TLS 1.3), PQC migration requires larger parameter sizes, hybrid cryptographic schemes, and unprecedented ecosystem coordination. This paper presents a structured expert synthesis of migration timelines, based on analysis of migration dependencies, historical precedents, and industry engagement. We analyze migration timelines for small, medium, and large enterprises, considering infrastructure upgrades, personnel availability, budget constraints, planning quality, and inter-enterprise synchronization. We argue that realistic timelines extend well beyond initial optimistic estimates: 5–7 years for small enterprises, 8–12 years for medium enterprises, and 12–15+ years for large enterprises under baseline assumptions. PQC migration is not a siloed technical upgrade but a global synchronization exercise, deeply intertwined with Zero Trust Architecture and long-term crypto-agility. These timelines are contextualized against expected arrival windows for fault-tolerant quantum computers (FTQC), projected between 2028 and 2033. We further analyze the “Store Now, Decrypt Later” threat model, crypto-agility frameworks, and provide comprehensive risk mitigation strategies for enterprises navigating this unprecedented cryptographic transition. Full article

In this work, the regio- and stereochemistry as well as the molecular mechanism of the cycloaddition reaction of nitrones with (E)-3-(methylsulfonyl)-propenoic acid derivatives were analyzed based on ωb97xD/6-311G(d,p) quantum chemical calculations. In light of these data, it is possible to propose selectivity of the analyzed processes, which was not clearly determined in light of previous experimental studies. Furthermore, the mechanism of the process was diagnosed. CDFT descriptors indicate that the reaction is triggered by a nucleophilic attack of the nitrone oxygen atom on the electrophilic carbon atom of (E)-3-(methylsulfonyl)-propenoic acid derivatives. In turn, PES analysis shows that, despite the nucleophilic-electrophilic character of the reactants, the corresponding transition states are only weakly polar and highly synchronous. IRC calculations rule out zwitterionic or biradical intermediates, confirming a single-step mechanism. The in silico ADME and PASS predictions indicate that the resulting isoxazolidines possess promising biological profiles, showing potential modulation of the serotonin system through 5-HT2A and 5-HT2C antagonism and stimulation of serotonin release, with structural features compatible with P450-mediated metabolism. Considering this attractive application potential, a detailed mechanistic investigation of their formation becomes essential for understanding and ultimately controlling the reaction pathways leading to these heterocycles. Full article

The Loop-Back Quantum Key Distribution (LB-QKD) protocol establishes a bidirectional architecture in which a single photon travels forth and back through the same optical channel. Unlike conventional one-way schemes such as BB84, Alice performs both state preparation and measurement, while Bob acts as a passive polarization modulator and reflector. This design eliminates detectors at Bob’s side, minimizes synchronization requirements, and enables compact, low-power implementations suitable for quantum-mobile and IoT platforms. An extended three-basis configuration $\{X,Y,Z\}$ is introduced, preserving the simplicity of the two-basis scheme while improving noise tolerance through enhanced orthogonality-based filtering. Analytical modeling shows that the effective protocol error decreases from $E_{\mathrm{protocol}}^{\left(2\right)}=e/2$ to $E_{\mathrm{protocol}}^{\left(3\right)}=e/3$, achieving a 33% improvement in noise resilience. Despite its slightly lower sifting efficiency ($\eta =1/6$), the total information gain reaches $G=0.26$ bits per pulse, maintaining post-sifting throughput comparable to BB84. The protocol doubles the tolerable QBER of conventional QKD, sustaining secure operation up to $22\%$ for two bases and approximately $47.58\%$ for three bases. Its passive, self-verifying architecture enhances resistance to man-in-the-middle, photon-number-splitting, and side-channel attacks, providing a scalable and energy-efficient framework for secure key distribution and authentication in next-generation mobile and distributed quantum networks. Full article

Key agreement protocols based on neural synchronization with Tree Parity Machines (TPMs) offer promising security advantages: they do not rely on trapdoor functions, making them resistant to quantum attacks, and they avoid the need for specialized hardware required by quantum-based schemes. Nevertheless, these protocols face a significant vulnerability: the large number of public message exchanges required for synchronization increases the risk that an attacker, acting as a Man-in-the-Middle, can successfully synchronize their own TPMs with those of the legitimate parties and ultimately recover the shared key. Motivated by the need to reduce this risk, we propose a novel probabilistic protocol that enables two parties to securely estimate the size of the shared key during intermediate steps, without revealing any key material. This estimation allows the protocol to terminate as soon as sufficient key material has been established, thereby reducing the number of synchronization rounds and limiting the opportunity for an attacker to synchronize. We integrate our estimation mechanism into a neural key agreement protocol and evaluate its performance and security, demonstrating improved efficiency and enhanced resistance to attacks compared to existing approaches. Full article

Spinning disk confocal microscopy enables fast optical sectioning with low phototoxicity but is often inaccessible due to high hardware costs. We present a lower-cost solution using a 25-megapixel machine vision CMOS camera and a custom-built spinning disk. This camera uses a back-illuminated sensor with high quantum efficiency and low read noise. High-resolution images of Thy1-GFP mouse brain slices, Drosophila embryos and larvae, and H&E-stained rat testis verified performance across 3D tissue volumes. The measured resolution was 215.8 nm in X, Y and 521.9 nm in Z with a 60×/1.42 NA objective. The custom disk, made with 18 µm pinholes (180 µm pitch) on a chrome photomask and mounted to an optical chopper motor, enables stable, near-telecentric imaging at lower magnifications. Micromanager software integration allows synchronized control of all hardware, which demonstrates that affordable CMOS sensors can potentially replace sCMOS in spinning disk microscopy, offering an open-access, scalable solution for advanced imaging. Full article

Homeostasis, which supports and maintains brain function, results from the continuous regulation of thermodynamics within tissue: the balance of heat production, redox oscillations, and vascular convection regulates coherent energy flow within the organ. Neuroinflammation disturbs this balance, creating measurable entropy gradients that precede structural damage to its tissue components. This paper proposes that a thermodynamic unity can be devised that incorporates nanoscale physics, energetic neurophysiology, and systems neuroscience, and can be used to understand and treat neuroinflammatory processes. Using multifactorial modalities such as quantum thermometry, nanoscale calorimetry, and redox oscillometry we define how local entropy production (s_t), relaxation time (τ^R), and coherence lengths (λc) allow quantification of the progressive loss of energetic symmetry within neural tissues. It is these variables that provide the basis for the etiology of thermodynamic biomarkers which on a molecular-redox-to-network scale characterize the transitions governing the onset of the neuroinflammatory process as well as the recovery potential of the organism. The entropic probing of systems (PEP) further allows the translation of these parameters into dynamic patient-specific trajectories that model the behavior of individuals by predicting recurrent bouts of instability through the application of machine learning algorithms to the vectors of entropy flux. The parallel development of the nanothermodynamic intervention, which includes thermoplasmonic heat rebalancing, catalytic redox nanoreacting systems, and adaptive field-oscillation synchronicity, shows by example how the corrections that can be applied to the entropy balance of the cell and system as a whole offer a feasible form of restoration of energy coherence. Such closed loop therapy would not function by the suppression of inflammatory signaling, but rather by the re-establishment of reversible energy relations between mitochondrial, glial, and vascular territories. The combination of these factors allows for correction of neuroinflammation, which can now be viewed from a fresh perspective as a dynamic phase disorder that is diagnosable, predictable, and curable through the physics of coherence rather than the molecular suppression of inflammatory signaling. The significance of this set of ideas is considerable as it introduces a feasible and verifiable structure to what must ultimately become the basis of a new branch of science: predictive energetic medicine. It is anticipated that entropy, as a measurable and modifiable variable in therapeutic “inscription”, will be found to be one of the most significant parameters determining the neurorestoration potential in future medical science. Full article

Low-temperature superconductivity has been known since 1957 to be described by BCS theory for effective single-band metals controlled by the density of states at the Fermi level, very far from band edges, the electron–phonon coupling constant l, and the energy of the boson in the pairing interaction w₀, but BCS has failed to predict high-temperature superconductivity in different materials above about 23 K. High-temperature superconductivity above 35 K, since 1986, has been a matter of materials science, where manipulating the lattice complexity of high-temperature superconducting ceramic oxides (HTSCs) has driven materials scientists to grow new HTSC quantum materials up to 138 K in HgBa₂Ca₂Cu₃O₈ (Hg1223) at ambient pressure and near room temperature in pressurized hydrides. This perspective covers the major results of materials scientists over the last 39 years in terms of investigating the role of lattice inhomogeneity detected in these new quantum complex materials. We highlight the nanoscale heterogeneity in these complex materials and elucidate their special role played in the physics of HTSCs. Especially, it is highlighted that the geometry of lattice and charge complex heterogeneity at the nanoscale is essential and intrinsic in the mechanism of rising quantum coherence at high temperatures. Full article

The increasing integration of artificial intelligence agents (AIAs) based on large language models (LLMs) is transforming many spheres of society. These agents act as human assistants, forming Distributed Intelligent Systems (DISs) and engaging in opinion formation, consensus-building, and collective decision-making. However, complex DIS network topologies introduce significant uncertainty into these processes. We propose a quantum-inspired graph signal processing framework to model collective behavior in a DIS interacting with an external environment represented by an influence matrix (IM). System topology is captured using scale-free and Watts–Strogatz graphs. Two contrasting interaction regimes are considered. In the first case, the internal structure fully aligns with the external influence, as expressed by the commutativity between the adjacency matrix and the IM. Here, a renormalization-group-based scaling approach reveals minimal reservoir influence, characterized by full phase synchronization and coherent dynamics. In the second case, the IM includes heterogeneous negative (antagonistic) couplings that do not commute with the network, producing partial or complete spectral disorder. This disrupts phase coherence and may fragment opinions, except for the dominant collective (Perron) mode, which remains robust. Spectral entropy quantifies disorder and external influence. The proposed framework offers insights into designing LLM-participated DISs that can maintain coherence under environmental perturbations. Full article

Recent publications have introduced the concept of quantum-like (QL) bits, along with their associated QL states and QL gate operations, which emerge from the dynamics of complex, synchronized networks. The present work extends these ideas to multi-level QL resources, referred to as QL dits, as higher-dimensional analogs of QL bits. We employ systems of k-regular graphs to construct QL-dits for arbitrary dimensions, where the emergent eigenspectrum of their adjacency matrices defines the QL-state space. The tensor product structure of multi-QL dit systems is realized through the Cartesian product of graphs. Furthermore, we examine the potential computational advantages of employing d-nary QL systems over two-level QL bit systems, particularly in terms of classical resource efficiency. Overall, this study generalizes the paradigm of using synchronized network dynamics for QL information processing to include higher-dimensional QL resources. Full article

Magnetocardiography (MCG) provides a non-invasive, contactless technique for evaluating the magnetic fields generated by cardiac electrical activity, offering unique spatial insights into cardiac electrophysiology. However, conventional MCG systems depend on superconducting quantum interference devices that require cryogenic cooling and magnetic shielded environments, posing considerable impediments to widespread clinical adoption. In this study, we present a novel MCG system utilizing a high-sensitivity, wide-dynamic-range magnetoresistive sensor array operating at room temperature. To mitigate environmental interference, identical sensors were deployed as reference channels, enabling adaptive noise cancellation (ANC) without the need for traditional magnetic shielding. MCG recordings were obtained from 40 healthy participants, with signals processed using ANC, R-peak-synchronized averaging, and Bayesian spatial signal separation. This approach enabled the reliable detection of key cardiac components, including P, QRS, and T waves, from the unshielded MCG recordings. Our findings underscore the feasibility of a cost-effective, portable MCG system suitable for clinical settings, presenting new opportunities for noninvasive cardiac diagnostics and monitoring. Full article

This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms—dynamic_rows and dynamic_matrix—are proposed and tested. These algorithms select the update rule quasi-randomly based on the input vector, reducing the effectiveness of synchronization-based attacks. A series of simulations were conducted to evaluate the security implications under various configurations, including different values of K, N, and L parameters of neural networks. The results demonstrate that the proposed dynamic algorithms can significantly reduce the attacker’s synchronization success rate without requiring additional communication overhead. Both proposed solutions outperformed hebbian, an update rule-based synchronization method utilizing the percentage of attackers synchronization. It has also been shown that when the attacker chooses their update rule randomly, the dynamic approaches work better compared to random walk rule-based synchronization, and that in most cases it is more profitable to use dynamic update rules when an attacker is using random walk. This study contributes to improving QKD’s robustness by introducing adaptive neural-based error correction mechanisms. Full article