Search Results (3,638)

Quantum machine learning (QML) has emerged as a promising approach for enhancing image classification by exploiting quantum computational principles such as superposition and entanglement. However, practical applications on complex datasets like CIFAR-100 remain limited due to the low expressivity of shallow circuits and challenges in circuit optimization. In this study, we propose HQCNN–REGA—a novel hybrid quantum–classical convolutional neural network architecture that integrates data re-uploading and genetic algorithm optimization for improved performance. The data re-uploading mechanism allows classical inputs to be encoded multiple times into quantum states, enhancing the model’s capacity to learn complex visual features. In parallel, a genetic algorithm is employed to evolve the quantum circuit architecture by optimizing gate sequences, entanglement patterns, and layer configurations. This combination enables automatic discovery of efficient parameterized quantum circuits without manual tuning. Experiments on the MNIST and CIFAR-100 datasets demonstrate state-of-the-art performance for quantum models, with HQCNN–REGA outperforming existing quantum neural networks and approaching the accuracy of advanced classical architectures. In particular, we compare our model with classical convolutional baselines such as ResNet-18 to validate its effectiveness in real-world image classification tasks. Our results demonstrate the feasibility of scalable, high-performing quantum–classical systems and offer a viable path toward practical deployment of QML in computer vision applications, especially on noisy intermediate-scale quantum (NISQ) hardware. Full article

Soil salinization severely limits plant growth and productivity. Mulberry (Morus alba L.), an economically and ecologically important tree, is widely cultivated, yet its salt-tolerance mechanisms at the seedling stage remain insufficiently understood. This study investigated the physiological and biochemical responses of two-year-old mulberry (‘Tailai Sang’) seedlings subjected to six NaCl treatments (0, 50, 100, 150, 200, and 300 mmol L⁻¹) for 28 days. Results showed that growth parameters and photosynthetic gas exchange exhibited dose-dependent declines. The reduction in net photosynthetic rate (Pn) was attributed to both stomatal limitations (decreased stomatal conductance) and non-stomatal limitations, as evidenced by a significant decrease in the maximum quantum efficiency of photosystem II (Fv/Fm) under high salinity. To cope with osmotic stress, seedlings accumulated compatible solutes, including soluble sugars, proteins, and proline. Critically, mulberry seedlings demonstrated effective ion homeostasis by sequestering Na⁺ in the roots to maintain a high K⁺/Na⁺ ratio in leaves, a mechanism that was compromised above 150 mmol L⁻¹. Concurrently, indicators of oxidative stress—malondialdehyde (MDA) and H₂O₂—rose significantly with salinity, inducing the activities of antioxidant enzymes (SOD, CAT, APX, and GR), which peaked at 150 mmol L⁻¹ before declining under extreme stress. A biomass-based LC₅₀ of 179 mmol L⁻¹ NaCl was determined. These findings elucidate that mulberry salt tolerance is a coordinated process involving three key mechanisms: osmotic adjustment, selective ion distribution, and a robust antioxidant defense system. This study establishes an indicative tolerance threshold under controlled conditions and provides a physiological basis for further field-based evaluations of ‘Tailai Sang’ mulberry for cultivation on saline soils. Full article

Financial market forecasting remains challenging due to complex nonlinear dynamics and regime-dependent behaviors that traditional models struggle to capture effectively. This research introduces the Adaptive Financial Reservoir Network with Hypernetwork Flow (AFRN–HyperFlow) framework, a novel ensemble architecture integrating Echo State Networks, temporal convolutional networks, mixture density networks, adaptive Hypernetworks, and deep state-space models for enhanced financial time-series prediction. Through comprehensive feature engineering incorporating technical indicators, spectral decomposition, reservoir-based representations, and flow dynamics characteristics, the framework achieves superior forecasting performance across diverse market conditions. Experimental validation on 26,817 balanced samples demonstrates exceptional results with an F1-score of 0.8947, representing a 12.3% improvement over State-of-the-Art baseline methods, while maintaining robust performance across asset classes from equities to cryptocurrencies. The adaptive Hypernetwork mechanism enables real-time regime-change detection with 2.3 days average lag and 95% accuracy, while systematic SHAP analysis provides comprehensive interpretability essential for regulatory compliance. Ablation studies reveal Echo State Networks contribute 9.47% performance improvement, validating the architectural design. The AFRN–HyperFlow framework addresses critical limitations in uncertainty quantification, regime adaptability, and interpretability, offering promising directions for next-generation financial forecasting systems incorporating quantum computing and federated learning approaches. Full article

The elastic and photoelastic coefficients of Ba₃TaGa₃Si₂O₁₄ (BTGS) crystals were determined by the quantum–mechanical calculation technique. Based on these data, extreme piezo-optic surfaces π′°_km were constructed, which describe the change in the path difference in light beams in the crystal under the influence of mechanical stress. The results for BTGS crystals are compared with the ones for other crystals of the langasite group (La₃Ga₅SiO₁₄, Ca₃Ga₂Ge₄O₁₄, Ca₃TaGa₃Si₂O₁₄ and Ca₃NbGa₃Si₂O₁₄). The global maxima of the π′°_km surfaces for BTGS crystals significantly exceed the ones for the other crystals mentioned above and, accordingly, BTGS crystals can be suitable for use in polarization-optic light modulators and devices based on them. The acousto-optic efficiency of BTGS crystals was evaluated. The correlations between the magnitude of the piezo- and elasto-optic coefficients and the parameters of the unit cell of the studied crystals were determined. Full article

The focal “hot spot” neuropathologies in COVID-19 infection are revealing footprints of a hidden underlying collapse of a novel ultrafast ultradian Piezo2 signaling system within the nervous system. Paradoxically, the same initiating pathophysiology may underpin the systemic findings in COVID-19 infection, namely the multiorgan SARS-CoV-2 infection-induced vascular pathologies and brain–body-wide systemic pro-inflammatory signaling, depending on the concentration and exposure to infecting SARS-CoV-2 viruses. This common initiating microdamage is suggested to be the primary damage or the acquired channelopathy of the Piezo2 ion channel, leading to a principal gateway to pathophysiology. This Piezo2 channelopathy-induced neural switch could not only explain the initiation of disrupted cell–cell interactions, metabolic failure, microglial dysfunction, mitochondrial injury, glutamatergic synapse loss, inflammation and neurological states with the central involvement of the hippocampus and the medulla, but also the initiating pathophysiology without SARS-CoV-2 viral intracellular entry into neurons as well. Therefore, the impairment of the proposed Piezo2-induced quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling seems to be the principal and critical underlying COVID-19 infection-induced primary damage along the brain axes, depending on the loci of SARS-CoV-2 viral infection and intracellular entry. Moreover, this initiating Piezo2 channelopathy may also explain resultant autonomic dysregulation involving the medulla, hippocampus and heart rate regulation, not to mention sleep disturbance with altered rapid eye movement sleep and cognitive deficit in the short term, and even as a consequence of long COVID. The current opinion piece aims to promote future angles of science and research in order to further elucidate the not entirely known initiating pathophysiology of SARS-CoV-2 infection. Full article

The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling. Full article

Multivalent lectin–glycan interactions (MLGIs) are vital for viral infection, cell-cell communication and regulation of immune responses. Their structural and biophysical data are thus important, not only for providing insights into their underlying mechanisms but also for designing potent glycoconjugate therapeutics against target MLGIs. However, such information remains to be limited for some important MLGIs, significantly restricting the research progress. We have recently demonstrated that functional nanoparticles, including ∼4 nm quantum dots and varying sized gold nanoparticles (GNPs), densely glycosylated with various natural mono- and oligo- saccharides, are powerful biophysical probes for MLGIs. Using two important viral receptors, DC-SIGN and DC-SIGNR (together denoted as DC-SIGN/R hereafter), as model multimeric lectins, we have shown that α-mannose and α-manno-α-1,2-biose (abbreviated as Man and DiMan, respectively) coated GNPs not only can provide sensitive measurement of MLGI affinities but also reveal critical structural information (e.g., binding site orientation and mode) which are important for MLGI targeting. In this study, we produced mannuronic acid (ManA) coated GNPs (GNP-ManA) of two different sizes to probe the effect of glycan modification on their MLGI affinity and antiviral property. Using our recently developed GNP fluorescence quenching assay, we find that GNP-ManA binds effectively to both DC-SIGN/R and increasing the size of GNP significantly enhances their MLGI affinity. Consistent with this, increasing the GNP size also significantly enhances their ability to block DC-SIGN/R-augmented virus entry into host cells. Particularly, ManA coated 13 nm GNP potently block Ebola virus glycoprotein-driven entry into DC-SIGN/R-expressing cells with sub-nM levels of EC₅₀. Our findings suggest that GNP-ManA probes can act as a useful tool to quantify the characteristics of MLGIs, where increasing the GNP scaffold size substantially enhances their MLGI affinity and antiviral potency. Full article

In this paper, we investigate the memory effects introduced by the time-fractional Schrödinger equation proposed by Naber on quantum entanglement and quantum dense coding under amplitude damping noise. Two formulations are analyzed: one with fractional operations applied to the imaginary unit and one without. Numerical results show that the formulation without fractional operations on the imaginary unit may be more suitable for describing non-Markovian (power-law) behavior in dissipative environments. This finding provides a more physically meaningful interpretation of the memory effects in time-fractional quantum dynamics and indirectly addresses fundamental concerns regarding the violation of unitarity and probability conservation in such frameworks. Our work offers a new perspective for the application of fractional quantum mechanics to realistic open quantum systems and shows promise in supporting the theoretical modeling of decoherence and information degradation. Full article

This paper serves as a comprehensive and practical resource to guide researchers in selecting suitable metals for use as photocathodes in radio-frequency (RF) and superconducting radio-frequency (SRF) electron guns. It offers an in-depth review of bulk and thin-film metals commonly employed in many applications. The investigation includes the photoemission, optical, chemical, mechanical, and physical properties of metallic materials used in photocathodes, with a particular focus on key performance parameters such as quantum efficiency, operational lifetime, chemical inertness, thermal emittance, response time, dark current, and work function. In addition to these primary attributes, this study examines essential parameters such as surface roughness, morphology, injector compatibility, manufacturing techniques, and the impact of chemical environmental factors on overall performance. The aim is to provide researchers with detailed insights to make well-informed decisions on materials and device selection. The holistic approach of this work associates, in tabular format, all photo-emissive, optical, mechanical, physical, and chemical properties of bulk and thin-film metallic photocathodes with experimental data, aspiring to provide unique tools for maximizing the effectiveness of laser cleaning treatment. Full article

Background/Objectives: In the last few decades, the dengue virus, a prevalent flavivirus, has demonstrated various epidemiological, economic, and health impacts around the world. Dengue virus serotype 2 (DENV2) plays a vital role in dengue-associated mortality. The RNA-dependent RNA polymerase (RdRp) of DENV2 is a charming druggable target owing to its crucial function in viral reproduction. In recent years, streptomycetes natural products (NPs) have attracted considerable attention as a potential source of antiviral drugs. Methods: Seeking prospective inhibitors that inhibit the DENV2 RdRp allosteric site, in silico mining of the Streptome database was executed. AutoDock4.2.6 software performance in predicting docking poses of the inspected inhibitors was initially conducted according to existing experimental data. Upon the assessed docking parameters, the Streptome database was virtually screened against DENV2 RdRp allosteric site. The streptomycetes NPs with docking scores less than the positive control (68T; calc. −35.6 kJ.mol⁻¹) were advanced for molecular dynamics simulations (MDS), and their binding affinities were computed by employing the MM/GBSA approach. Results: SDB9818 and SDB4806 unveiled superior inhibitor activities against DENV2 RdRp upon MM/GBSA//300 ns MDS than 68T with ΔG_binding values of −246.4, −242.3, and −150.6 kJ.mol⁻¹, respectively. A great consistency was found in both the energetic and structural analyses of the identified inhibitors within the DENV2 RdRp allosteric site. Furthermore, the physicochemical characteristics of the identified inhibitors demonstrated good oral bioavailability. Eventually, quantum mechanical computations were carried out to evaluate the chemical reactivity of the identified inhibitors. Conclusions: As determined by in silico computations, the identified streptomycetes NPs may act as DENV2 RdRp allosteric inhibitors and mandate further experimental assays. Full article

Fibrate pharmaceuticals (fibrates), as a widespread class of emerging contaminants, pose potential risks to both ecological systems and human health. The photocatalytic system based on nitrogen (N) and fluorine (F) co-doped TiO₂ nanotube arrays (NF-TNAs) provides a renewable solution for fibrate pharmaceutical removal from water, powered by inexhaustible sunlight. In this study, the degradation of two typical fibrates, i.e., bezafibrate (BZF) and ciprofibrate (CPF), under simulated sunlight irradiation through NF-TNAs were investigated. The photocatalytic degradation of BZF/CPF was achieved through combined radical and non-radical oxidation processes, while the generation and reaction mechanisms of associated reactive oxygen species (ROS) were examined. Electron paramagnetic resonance detection and quenching tests confirmed the existence of h⁺, •OH, O₂^•−, and ¹O₂, with O₂^•− playing the predominant role. The transformation products (TPs) of BZF/CPF were identified through high-resolution mass spectrometry analysis combined with quantum chemical calculations to elucidate the degradation pathways. The influence of co-existing ions and typical natural organic matters (NOM) on BZF/CPF degradation were also tested. Eventually, the ecological risk of BZF/CPF transformation products was assessed through quantitative structure–activity relationship (QSAR) modeling, and the results showed that the proposed photocatalytic system can largely alleviate fibrate toxicity. Full article

The mechanism by which voltage-gated ion channels open and close has been the subject of intensive investigation for decades. For a large class of potassium channels and related sodium channels, the consensus has been that the gating current preceding the main ionic current is a large movement of positively charged segments of protein from voltage-sensing domains that are mechanically connected to the gate through linker sections of the protein, thus opening and closing the gate. We have pointed out that this mechanism is based on evidence that has alternate interpretations in which protons move. Very little literature considers the role of water and protons in gating, although water must be present, and there is evidence that protons can move in related channels. It is known that water has properties in confined spaces and at the surface of proteins different from those in bulk water. In addition, there is the possibility of quantum properties that are associated with mobile protons and the hydrogen bonds that must be present in the pore; these are likely to be of major importance in gating. In this review, we consider the evidence that indicates a central role for water and the mobility of protons, as well as alternate ways to interpret the evidence of the standard model in which a segment of protein moves. We discuss evidence that includes the importance of quantum effects and hydrogen bonding in confined spaces. K⁺ must be partially dehydrated as it passes the gate, and a possible mechanism for this is considered; added protons could prevent this mechanism from operating, thus closing the channel. The implications of certain mutations have been unclear, and we offer consistent interpretations for some that are of particular interest. Evidence for proton transport in response to voltage change includes a similarity in sequence to the H_v1 channel; this appears to be conserved in a number of K⁺ channels. We also consider evidence for a switch in -OH side chain orientation in certain key serines and threonines. Full article

In this position paper, we argue that the conventional understanding of ‘information’ (as generally conceived in science, in a digital fashion) is overly simplistic and not consistently applicable to living systems, which are open systems that cannot be reduced to any kind of ‘portion’ (building block) ascribed to the category of quantity. Instead, it is a matter of relationships and qualities in an indivisible analogical (and ontological) relationship between any presumed ‘software’ and ‘hardware’ (information/matter, psyche/soma). Furthermore, in biological systems, contrary to Shannon’s definition, which is well-suited to telecommunications and informatics, any kind of ‘information’ is the opposite of internal entropy, as it depends directly on order: it is associated with distinction and differentiation, rather than flattening and homogenisation. Moreover, the high degree of structural compartmentalisation of living matter prevents its energetics from being thermodynamically described by using a macroscopic, bulk state function. This requires the Second Principle of Thermodynamics to be redefined in order to make it applicable to living systems. For these reasons, any static, bit-related concept of ‘information’ is inadequate, as it fails to consider the system’s evolution, it being, in essence, the organized coupling to its own environment. From the perspective of quantum field theory (QFT), where many vacuum levels, symmetry breaking, dissipation, coherence and phase transitions can be described, a consistent picture emerges that portrays any living system as a relational process that exists as a flux of context-dependent meanings. This epistemological shift is also associated with a transition away from the ‘particle view’ (first quantisation) characteristic of quantum mechanics (QM) towards the ‘field view’ possible only in QFT (second quantisation). This crucial transition must take place in life sciences, particularly regarding the methodological approaches. Foremost because biological systems cannot be conceived as ‘objects’, but rather as non-confinable processes and relationships. Full article

Rovelli’s relational interpretation of quantum mechanics tells us that the description of a system in the formalism of quantum mechanics is not an absolute but is relative to the observer itself. The interpretation goes further and proposes a set of axioms. In standard non-relational language, one of them states that an observer can only retrieve a finite amount information from a system by means of measurement. Our contribution starts with the observation that quantum mechanics, i.e., quantum field theory (QFT) in dimension 1, radically differs from QFT in higher dimensions. In higher dimensions, boundary data (or initial data) cannot be characterized by finitely many measurements. This calls for a notion of measuring scale, which we provide. At a given measuring scale, the observer has partial information about the system. Our notion of measuring scale generalizes the one implicitly used in Wilsonian QFT. At each measuring scale, there are effective theories, which may be corrected, and if the theory turns out to be renormalizable, the mentioned corrections converge to determine a completely corrected (or renormalized) theory at the given measuring scale. The notion of a measuring scale is the cornerstone of Wilsonian QFT; this notion tells us that we are not describing a system from an absolute perspective. An effective theory at that scale describes the system with respect to the observer, which may retrieve information from the system by means of measurement in a specific way determined by our notion of measuring scale. We claim that a relational interpretation of quantum physics for spacetimes of dimensions greater than 1 is Wilsonian. Full article

Graphene quantum dots (GQDs) obtained by microwave-induced pyrolysis of glutamic acid and triethylenetetramine (trien) are fairly stable, emissive, water-soluble, and positively charged nano-systems able to interact with negatively charged meso-tetrakis(4-sulfonatophenyl) porphyrin (TPPS₄). The stoichiometric control during the preparation affords a supramolecular adduct, GQDs@TPPS₄, that exhibits a double fluorescence emission from both the GQDs and the TPPS₄ fluorophores. These supramolecular aggregates have an overall negative charge that is responsible for the condensation of cations in the nearby aqueous layer, and a three-fold acceleration of the metalation rates of Cu²⁺ ions has been observed with respect to the parent porphyrin. Addition of various metal ions leads to some changes in the UV/Vis spectra and has a different impact on the fluorescence emission of GQDs and TPPS₄. The quenching efficiency of the TPPS₄ emission follows the order Cu²⁺ > Hg²⁺ > Cd²⁺ > Pb²⁺ ~ Zn²⁺ ~ Co²⁺ ~ Ni²⁺ > Mn²⁺ ~ Cr³⁺ >> Mg²⁺ ~ Ca²⁺ ~ Ba²⁺, and it has been related to literature data and to the sitting-atop mechanism that large transition metal ions (e.g., Hg²⁺ and Cd²⁺) exhibit in their interaction with the macrocyclic nitrogen atoms of the porphyrin, inducing distortion and accelerating the insertion of smaller metal ions, such as Zn²⁺. For the most relevant metal ions, emission quenching of the porphyrin evidences a linear behavior in the micromolar range, with the emission of the GQDs being moderately affected through a filter effect. Deliberate pollution of the samples with Zn²⁺ reveals the ability of the GQDs@TPPS₄ adduct to detect sensitively Cu²⁺, Hg^2+, and Cd²⁺ ions. Full article