Abstract: Dissolving microneedles (DMN) could be considered as a promising platform for transdermal delivery of naltrexone hydrochloride (NTX), providing a minimally inva-sive alternative to conventional administration routes. In the present study, DMN patches with an advanced design were developed via a two-step micromoulding tech-nique. The systems were composed of drug-free polyvinylpyrrolidone (PVP) and poly-vinyl alcohol (PVA) blend microneedle tips, combined with a drug-loaded backing layer based on PVP and the thermoresponsive polymer Poloxamer 407. The influence of polymer concentration into DMN tips and backing layer composition on morpholo-gy, mechanical properties, drug release and permeation was evaluated. Mechanical studies as well as SEM observation revealed that intermediate polymer concentration (formulation MN-20%/2:1), used for DMN tips preparation, provided optimal mi-croneedle geometry, superior structural integrity and penetration efficiency. Incorpo-ration of NTX into backing layer allowed high and uniform drug loading. In vitro per-meation studies demonstrated significantly enhanced NTX delivery from DMN sys-tems compared to simple matrix patches, with the thermoresponsive backing layer contributing to controlled drug release. These findings highlight the importance of polymer composition in DMN design and demonstrate the potential of the developed systems as an effective platform for transdermal delivery of NTX.

Abstract: Metabolic disorders, including obesity, type 2 diabetes mellitus (T2DM), dyslipidemia, and metabolic dysfunction–associated fatty liver disease (MAFLD), represent a major and escalating global health burden. These conditions are now recognized as systemic disorders arising from dysregulated inter-organ communication among metabolically active tissues. Central mechanisms include insulin resistance, chronic low-grade inflammation, oxidative stress, mitochondrial dysfunction, and neuroendocrine dysregulation. Exercise is increasingly recognized as a potent multisystem therapeutic intervention. Beyond energy expenditure, it induces coordinated molecular adaptations across tissues, including improved mitochondrial function, reduced inflammation, and enhanced metabolic flexibility. Exercise-induced signaling molecules (exerkines) and gut microbiota remodeling further mediate systemic metabolic benefits. This review synthesizes current evidence on exercise as an integrative therapy for metabolic disorders, with emphasis on molecular mechanisms, organ-specific adaptations, and clinical applications. Emerging roles of membrane microdomains such as caveolae are discussed as potential regulators of metabolic signaling, although their role in exercise adaptation remains incompletely defined.

Abstract: Rho GTPases—including RhoA, Rac1, and Cdc42—are key molecular switches that regulate cytoskeletal dynamics and transduce biochemical and mechanical signals essential for skeletal and dental tissue development. These small GTPases orchestrate fundamental cellular processes such as proliferation, migration, polarity, and differentiation, thereby guiding the morphogenesis, homeostasis, and regeneration of bone and teeth. In bone, Rho GTPases modulate osteoblast proliferation and matrix mineralization, osteoclast-mediated bone resorption, and mechanotransductive responses to physical stimuli. They are also critical for the behavior and fate specification of skeletal stem cells, integrating environmental cues to balance self-renewal and lineage commitment. In dental tissues, Rho GTPases regulate epithelial–mesenchymal interactions, odontoblast and ameloblast polarization, and the formation of enamel and dentin. Additionally, they play vital roles in craniofacial suture development, where their spatially and temporally controlled activity maintains suture patency and regulates ossification. Dysregulation of Rho GTPase signaling is implicated in a variety of pathological conditions, including osteoporosis, craniosynostosis, and dentinogenesis and amelogenesis imperfecta. Despite their therapeutic potential, targeting Rho GTPases remains challenging due to their pleiotropic functions and broad tissue distribution. This review highlights the mechanistic roles, regulatory networks, and developmental relevance of RhoA, Rac1, and Cdc42 in skeletal and dental biology, and discusses emerging strategies for modulating their activity in regenerative and disease contexts.

Abstract: Elementary particles exhibit intrinsic phase recurrences, \rev{implicit in the undulatory description}, so each can serve as a ``virtually perfect'' reference relativistic clock. From this perspective, Rovelli's ``timeless'' viewpoint is best read not as denying time, but as denying the fundamentality of any preferred external time coordinate: time persists as internal cyclic variables carried by particles, covariantly modulated by energy exchange and relativistic transformations. \rev{Then,} macroscopic flow arises from records and thermodynamic coarse-graining. This Letter shows that cyclic internal times of elementary systems are fully compatible with the ordinary non-compact relativistic time flow observed in nature. In particular, it identifies the fundamental ``internal'' variables underlying physical relativistic time with the particles' intrinsic cyclic times and their relational covariant modulations. Supported by theoretical and phenomenological results established in previous works, intrinsic temporal periodicity constitutes the fundamental principle of Elementary Cycles Theory and acts as exact quantization condition.

Abstract: The ongoing expansion of Highly The ongoing expansion of Highly Pathogenic Avian Influenza Virus (HPAIV) H5N1 is driving unprecedented wildlife mortality and raising global health concerns. To date, Oceania remains the last region free of HPAIV, offering a critical opportunity to anticipate and mitigate future emergence. Here, we assess the risk of HPAIV introduction and spread within seabird communities of New Caledonia, a key biodiversity hotspot of the South Pacific located along major transoceanic migratory routes. We compiled a comprehensive list of seabird species previously exposed to HPAIV and evaluated their likelihood of occurrence in New Caledonia using literature and global biodiversity databases. Species were classified as breeding or non-breeding, and their potential roles in virus dynamics were quantified using trait-based indices. Additionally, seabird migratory connectivity between New Caledonia and surrounding regions was estimated. Among 71 retained seabird species, several long-distance migrant species—particularly within Procellariidae and Charadriiformes—emerged as potential high-risk vectors, although often with low probability of occurrence locally. In contrast, highly colonial breeding species, including Thalasseus bergii and Fregata minor, showed the greatest potential to amplify local transmission. Network analyses revealed that the strongest ecological connections occur with nearby regions not yet affected by HPAIV, whereas links to infected areas involve distances > 2000 km, potentially constraining virus emergence in the South Pacific. Our results identify priority species and critical knowledge gaps, providing a framework to guide targeted surveillance and proactive management strategies in the South Pacific.

Abstract: Dixit et al. proposed an asymptotic drag scaling for zero-pressure-gradient flat-plate turbulent boundary layers based on the approximation M∼U_τ²δ, where M is the kinematic momentum rate through the boundary layer, U_τ is the friction velocity, and δ is the boundary-layer thickness. In the present paper, an explicit Reynolds-number-dependent correction to this approximation is derived from the logarithmic mean-velocity profile. Integration of the log law across the layer yields M∼U_τ²δf(Re_τ), where Re_τ=δU_τ/ν is the friction Reynolds number and f(Re_τ) is given analytically. Application of the correction to the dataset compiled by Dixit et al. shows that the corrected scaling gives an exponent closer to the asymptotic value −1/2 than the uncorrected formulation. The correction should be viewed as a leading-order amendment, since the derivation uses the logarithmic law outside its strict range of validity.

Abstract: Hydrogen-blended natural gas (HBNG) is widely regarded as a transitional option for decarbonizing urban gas systems. However, the coupled evolution from buried pipeline leakage to pre-ignition flammable cloud formation remains poorly integrated across research stages. This review synthesizes experimental, numerical, and data-driven studies on the sequential processes of leak source-term dynamics, subsurface migration through porous media, surface breakthrough and escape, accumulation in semi-enclosed spaces, and pre-ignition flammable cloud development. Existing studies indicate that hydrogen blending alters the density, diffusivity, flammability limits, and ignition sensitivity of the gas mixture, thereby affecting the breakthrough time, stratification behavior, and pre-ignition early warning windows. The hazard evolution is jointly governed by pipeline pressure, leak orifice size, burial depth, soil heterogeneity, soil moisture content, spatial confinement, and ventilation conditions. Six major knowledge gaps are identified: the fragmentation of physical evolution stages in current research, the lack of full-scale multi-physics coupled experimental datasets, insufficient characterization of in-situ heterogeneous soil conditions, bottlenecks in high-resolution transient gas cloud measurement, inadequate integration of mechanistic findings into quantitative risk assessment frameworks, and the lag in full-lifecycle integrity management of hydrogen-blended pipeline networks. Based on the identified gaps, this review proposes a coherent, mechanism-informed analytical framework for urban HBNG pipeline safety. This framework emphasizes the incorporation of dynamic mechanistic parameters into high-consequence area zoning, sensor placement, ventilation interlocking, and full-lifecycle integrity management, thereby supporting safer engineering deployment.

Abstract: Biomass valorization plays a vital role in achieving carbon neutrality and circular economy frameworks. Owing to its carbon rich structure, biomass represents a promising feedstock to produce bio-based hydrocarbons via biological and thermochemical pathways. While biological conversion routes have been extensively studied, their deployment at commercial scale is constrained by high capital costs and low product yields. In contrast, thermochemical conversion technologies are increasingly being explored as viable largescale biomass valorization routes. This review presents a comprehensive assessment of thermochemical pathways, with particular emphasis on hydrothermal liquefaction (HTL). HTL enables the efficient conversion of wet and heterogeneous lignocellulosic biomass without energy intensive drying pretreatments. The review critically examines the formation and physicochemical properties of the two main HTL products, namely liquid biocrude and solid hydrochar. Special attention is devoted to challenges associated with biocrude quality, particularly its high oxygen content, and corresponding upgrading strategies. Additionally, the diverse applications of hydrochar for energy recovery, soil amendment, and heterogeneous catalyst synthesis are discussed. The article also compares the technology readiness levels of thermochemical conversion routes and highlights the growing role of artificial intelligence and machine learning in process modelling and optimization. Finally, future research directions are identified, emphasizing design by specification strategies and physics informed AI to enable scalable, autonomous biomass conversion technologies.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) presents unique treatment challenges often due to the development of anti-cancer drug resistance. Previously, we demonstrated that CRISPR-directed gene ablation disabled the master regulator gene NRF2, a tran-scription factor known to control drug resistance in squamous cell carcinoma tumor cells restored chemosensitivity. In this short study, we evaluated a broad range of CRISPR/Cas9 molecules for their capacity to elicit similar response in PDAC cells. Synthetic single guide RNAs (sgRNAs) were designed to target multiple functional domains encoded by NRF2. These molecules were delivered to cells via nucleofection with outcomes analyzed by genotypic, phenotypic and functional assays. We observed targeting efficiencies ranging from 25% to 100% with a high level of random insertions and deletions (indels). sgRNAs targeting exons 2, 3 and 4 demonstrated produced a high degree of genotypic, phenotypic and functional outcomes. Targeted disruption of exons 3 and 4 reveals significant loss of cell viability while overcoming dug resistance through the restoration of sensitivity to Gemcitabine (>1.75 uM). Our study identifies do-main-specific sites within NRF2 that, when disabled, restore sensitivity to Gemcitabine potentiating a more in-depth analyses of this novel augmentative therapeutic approach.

Abstract: Using zebrafish as a model, we characterized novel long non-coding RNAs linked to caudal fin regeneration and positional memory, uncovering evolutionarily conserved candidates with potential cross-species relevance. RNA-seq data deposited in the NCBI database were compared at various important time points (0h post-amputation (hpa), 12 hpa, 1 day post-amputation (dpa), two dpa, three dpa, and seven dpa) and fin parts (proximal, middle, and distal) to uncover major regulatory lncRNAs. Using HISAT2, StringTie, FEELnc, Conservation Analysis, and WGCNA, our analysis revealed 107 lncRNAs associated with specific regeneration time points and 229 lncRNAs involved in positional memory during the regenerative process. We identified 13 common genomic regions that are complete or partial lncRNAs, indicating a functional connection between regeneration and positional identity, and expressed differently at each time point and each position. Additionally, a comparison with regeneration-associated mRNAs revealed that these 13 regions play critical roles in both processes, providing insights into the molecular mechanisms of regenerative precision. RT-PCR validation confirmed position-specific differential expression of these overlapping regions despite identical injury, suggesting roles in regenerative regulation and evolutionary adaptation.

Abstract: Irrigated agriculture is the dominant water user in Central Asia and is critical for regional food security and livelihoods. Much of the irrigation infrastructure, developed during the Soviet era, enabled large-scale agricultural expansion but contributed to environmental degradation, including the desiccation of the Aral Sea. Since 1991, countries have implemented reforms to improve water governance and efficiency. This review integrates historical policy analysis with Earth observation–based assessments of land use, vegetation dynamics, evapotranspiration, and hydroclimatic trends. Satellite evidence indicates that irrigation demand remains high and has intensified in some regions despite modernization efforts. Meanwhile, climate change—through rising temperatures, reduced snow storage, and increased variability—further pressures water resources. Although emerging technologies such as remote sensing and digital water management offer opportunities for improvement, achieving sustainable irrigation will require stronger institutional reforms, improved basin-scale water accounting, and enhanced transboundary cooperation.

Abstract: Image classification is one of the earliest and most fundamental approaches in computer vision (CV). In the literature, a wide variety of methods have been proposed, ranging from handcrafted feature-based methods to deep-learning-based approaches such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Each new method has been developed to address the shortcomings of previous methods and achieve higher performance. Image classification is a broad field of study in contemporary scientific research. In this study, image classification methods are comprehensively examined across five distinct application domains: (1) General-purpose vision tasks, (2) Healthcare and medical imaging, (3) Agriculture and environmental monitoring, (4) Remote sensing and Earth observation, (5) Industrial automation and quality inspection. The study first explains classical image classification techniques based on handcrafted features and the corresponding classifiers. It then addresses CNN and ViT models, which are widely used in the literature, analyzing them in terms of architectural innovations, parameter efficiency, and computational complexity. This review covers studies published between 2014 and 2026, with a particular focus on recent developments from 2022 to 2026. Ten datasets were cataloged for each domain; the datasets' characteristics, class distributions, and primary application areas were examined in detail. Additionally, 50 representative real-world applications across these domains were analyzed. The study also addresses the challenges encountered in image classification, and finally discusses future directions.

Abstract: This paper proposes Functional Consciousness (FC) as a measurable architectural property: the observable capacity of a system to access and reason about internal representations of its own states. We introduce a computationally tractable metric on FC that operationalizes core tenets of major consciousness theories through self-models and their associated reasoning power, measured through informational richness and state-space expansion under inference. The resulting Functional Consciousness Score (FCS) is applied to benchmark systems with known internal structure, including a Waymo L4 autonomous vehicle. To extend the framework to black-box systems, we present Functional Self-Model Analysis (FSMA), an abductive methodology for inferring self-models from behavioral evidence. Applied to stream-of-consciousness literature, FSMA yields a catalog of self-models that serves as a reference for estimating functional consciousness in more complex biological and artificial agents. The resulting scores align with intuitive gradients of cognitive sophistication while remaining operationally grounded. Finally, we compare FC with major theories of consciousness and argue that several of their central functional claims become partially measurable within this framework.

Abstract: Quantum computing introduces a new class of cyber threats that challenge existing detection paradigms. While significant research has focused on post-quantum cryptography (PQC) and the long-term risk of cryptographic breakage, far less attention has been given to the detection of quantum-enabled attacks during the pre-cryptographically-relevant quantum (pre-CRQC) era. Current intrusion detection systems, network monitors, and cryptographic telemetry tools lack the conceptual models and operational indicators needed to identify adversaries who leverage quantum acceleration, quantum-optimized reconnaissance, or hybrid-mode downgrade strategies. This paper proposes QEADT-1, a systems-level taxonomy for detecting or monitoring quantum-enabled attack patterns against classical and PQC-transitioning infrastructure during the pre-CRQC era, addressing a gap in the literature, which has focused predominantly on prevention through PQC migration and on QKD or quantum-hardware concerns rather than operational detection. We classify six major attack classes, identify their underlying mechanisms, and map each to proposed observable indicators and required telemetry sources. A SOC-oriented detection matrix is introduced to operationalize the taxonomy as a synthesized detection model, followed by a systems architecture for quantum-attack monitoring that is designed to integrate with ongoing PQC migration. This work provides a foundational framework for organizations seeking to detect quantum-enabled threats before cryptographically relevant quantum computers emerge.

Abstract: This article reviews the research of Fedor Vasilievich Shugaev, Doctor of Physical and Mathematical Sciences and Professor at the Faculty of Physics of M. V. Lomonosov Moscow State University. Over a career at MSU spanning more than six decades, Professor Shugaev has published 146 journal articles, 4 monographs, 46 conference papers and 83 invited talks, and supervised twelve Candidate-of-Sciences dissertations and twenty diploma theses. The main research lines covered below are the propagation and reflection of shock waves; shock-wave interaction with vortices, acoustic disturbances and turbulent fluctuations; shock-wave dynamics in low-temperature and discharge plasmas; the geometry and stability of magnetised and astrophysical bow shocks; Navier–Stokes-based methods for vortex acoustics; laser-beam propagation through the turbulent atmosphere; and a recent cycle of work in theoretical astrophysics.

Abstract: Sub-inhibitory (sub-MIC) antibiotics modulate bacterial quorum-sensing (QS) networks, but whether this modulation involves direct receptor engagement or indirect stress-mediated mechanisms remains unresolved. To address this, we trained a Random Forest classifier (ECFP4; scaffold-split AUC = 0.958; Y-randomization separation = 0.482) on 3,324 ChEMBL-curated compounds to predict engagement with LuxR-family N-acyl-homoserine lactone (AHL) receptors across six bacterial species. Clinical antibiotics (n = 54) scored near zero (mean P(QS) = 0.014), including those with documented sub-MIC QS effects (p = 0.36 vs. undocumented), suggesting that sub-MIC modulation operates via transcriptional reprogramming rather than direct binding. Large-scale screening of 36,132 antibacterial compounds (Broad Institute) confirmed a negative correlation between P(QS) and antibacterial activity (Spearman ρ = −0.086; p < 10−60), robust across species-stratified and MW-stratified analyses. Screening of 731,587 natural products (NPs) from the COCONUT database identified 41 non-AHL candidates with P(QS) > 0.3 within the applicability domain, including the confirmed QS inhibitor Honaucin A and the marine antibiotics Korormicins. Independent pharmacophore analysis against a 10-AHL reference panel confirmed greater similarity of NP candidates to AHLs relative to antibiotics (Gobbi-Tanimoto: 0.108 vs. 0.037; Mann-Whitney p = 0.006). The results demonstrate quantitative chemical orthogonality between antibacterials and QS modulators and identify NPs as priority hypotheses for experimental validation of dual function — QS modulation at sub-MIC and antibacterial activity at elevated concentrations.

Abstract: Micro-nonuniformity, as a fundamental natural property, is widespread across a range of microscopic systems, such as polymer systems, biomacromolecular systems, and nanosystems; however, the construction of micro-nonuniform molecular systems has not yet been realized at the level of organic molecules with well-defined structural compositions. Inspired by the "chemical space" concept, I recently reported a study of the single-molecule mixture state; in this paper, I provide a detailed discussion of micro-nonuniformity and the concept of a "single-molecule mixture".

Abstract: This paper develops the mathematical foundations of the Single Monad Model of the Cosmos and the Duality of Time Theory, with a central focus on the emergence of Lorentzian geometry from a unified generative origin. Starting from a dual-time architecture—comprising generative inner time, completed outer time, and a completion–projection interface—the framework constructs a precise pipeline from unicity to observable multiplicity. At the generative level, admissible histories form a monadic structure acting on states, while completion and observation induce a quotient-based descent to irreversible observable dynamics. Quadratic carrier algebras provide the minimal algebraic setting, separating a compact circular branch associated with recurrence and phase from a split hyperbolic branch associated with causal readout. A key representation-theoretic result shows that compact inner-time symmetry enforces canonical complex Hilbert structure on irreducible sectors, while invariant-form rigidity excludes Lorentzian signatures from these phase sectors. The central new contribution is a stabilization interface that maps completed histories to probability measures on candidate event structures. From these stabilized statistics, the paper reconstructs effective causal order and volume, and proves that, in a manifoldlike regime, these data determine a Lorentzian geometry up to coarse equivalence. This establishes a theorem-bearing bridge from generative structure to spacetime geometry. The resulting framework organizes complex Hilbert structure, observable irreversibility, and Lorentzian geometry within a single constrained emergence chain: compact recurrence yields phase and complex structure; completion yields irreversible observables; stabilization yields persistent event statistics; and reconstructed order plus volume yields spacetime geometry. The analysis is structural and constraint-driven, showing that phase and causal geometry arise at distinct levels and are necessarily carried by different quadratic branches.

Abstract: Background: Direct annual national series on AI adoption in higher education are not consistently available for Uzbekistan, yet the diffusion of AI-enabled learning depends on measurable digital and economic preconditions. Methods: Using annual data for 2000–2023, this study models tertiary enrollment as a macro-level proxy for the expansion of AI-ready higher education, with internet use, mobile subscriptions, and real GDP per capita as explanatory factors in a trend-augmented ARDL/UECM framework. Trend-aware unit-root testing, lag selection, bounds testing, and residual diagnostics are implemented as one closed empirical sequence. Results: The preferred ARDL(1,3,1,1) specification supports cointegration, a significant error-correction mechanism, a positive long-run role for mobile access, and a negative internet coefficient after controlling for mobile inclusion, income, and structural trend. Conclusions: AI readiness in higher education should be interpreted as a conversion problem rather than a simple connectivity problem.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.