Abstract: Central nervous system (CNS) disorders constitute a significant global health challenge; however, the development of therapeutic agents is considerably impeded by the difficulty in delivering effective concentrations within the brain. This comprehensive review delineates the current landscape of computational modeling techniques employed to address the formidable challenges associated with CNS drug delivery, with a particular emphasis on the anatomical barriers and physiological transport mechanisms pertinent to major neurological diseases. We categorize modeling approaches ranging from the atomistic scale, including molecular dynamics simulations of drug-blood-brain barrier (BBB) interactions, to macroscopic continuum and Physiologically Based Pharmacokinetic (PBPK) models that elucidate systemic distribution and overall brain exposure. We critically assess these models concerning established delivery routes, such as intranasal and intrathecal administration, as well as emerging methods, including focused ultrasound-mediated BBB opening and targeted nanoparticle delivery. This review underscores the growing importance of integrating complex physiological phenomena, such as glymphatic flow and cerebrospinal fluid (CSF) dynamics, into predictive models. Finally, we explore the emerging opportunities involving multiscale digital twins of the CNS that integrate molecular interactions, vascular hemodynamics, CSF and perivascular flow, and parenchymal transport within patient-specific anatomical geometries. The role of machine learning and surrogate modeling in expediting the prediction of drug transport parameters and optimizing delivery strategies is also examined. By providing a structured overview of current computational tools, this review aims to guide researchers in the design of more robust computational platforms for CNS drug delivery.

Abstract: Representational alignment, defined as correspondence between distinct representations of the same underlying structure, is usually evaluated using coordinate-level similarity in high-dimensional spaces, together with correlation-based measures, subspace alignment techniques, probing performance and mutual predictability. However, these approaches do not specify a baseline for the level of agreement induced solely by dimensional compression, shared statistical structure or symmetry. We develop a methodological framework for assessing representational alignment using the Borsuk-Ulam theorem as a formal constraint. Representations are modeled as continuous maps from a state space endowed with a minimal symmetry into lower-dimensional descriptive spaces. In this setting, the Borsuk-Ulam theorem provides a lower bound on the identification of symmetry-paired states that must arise under dimensional compression. Building on this bound, we define representational alignment in terms of shared induced equivalence relations rather than coordinate-level similarity. Alignment is quantified by testing whether distinct models collapse the same symmetry-related states beyond what is guaranteed by topological necessity alone. The resulting metrics are architecture-independent, symmetry-explicit and compatible with probe-based comparisons, enabling controlled null models and scale-dependent analyses. Our framework supports testable hypotheses concerning how alignment varies with representation dimension, compression strength and symmetry structure, and applies to both synthetic and learned representations without requiring access to internal model parameters. By grounding alignment assessment in a well-defined topological constraint, this approach enables principled comparison of representations while remaining neutral with respect to the semantic or ontological interpretation of learned features.

Abstract:

Antimicrobial resistance (AMR) is recognised as a major global public health threat, with the environment increasingly acknowledged as a key reservoir and dissemination pathway for resistant bacteria and resistance genes. In this study, 148 surface water samples were collected between 2023 and 2024 from six rivers and three canals discharging wastewater into two lake waters in southern Italy to assess the occurrence and genomic features of extended-spectrum β-lactamase (ESBL)-, AmpC- and carbapenemase-producing Escherichia coli and Klebsiella pneumoniae. Relevant isolates were obtained using selective culturing, and tested for antimicrobial susceptibility by broth microdilution. Major β-lactam resistance genes were detected by Real-Time PCR. Whole-genome sequencing (WGS) was performed on presumptive carbapenemase-producing isolates. ESBL- and/or carbapenemase-producing Enterobacterales were detected in 67.6% of samples, yielding 176 non-duplicate isolates. The most prevalent gene was bla_CTX-M, detected in 79.3% of positive isolates (96/121), while carbapenemase genes were detected in 20.6% (25/121) of isolates, mainly bla_OXA-48 and bla_VIM. WGS analysis revealed occurrence of clinically relevant high-risk clones, such as K. pneumoniae ST512/ST307 carrying bla_KPC-3 and E. coli ST10 harboring bla_OXA-244. These findings demonstrate widespread contamination of surface waters with clinically relevant resistant Enterobacterales and highlight the importance of integrating environmental compartments into One Health AMR surveillance frameworks.

Abstract: In this paper a folding wing based on gear meshing deformation mechanism is developed, focusing on structural analysis and further optimization of the folding wing. Compared with existing folding wing concepts, the deformation mode of this wing is easier to manufacture and implement in engineering. A dynamic contact finite element model of gear meshing is established in ABAQUS, achieving the transmission of motion. The meshing simulation on the gear pair and dynamic strength analysis on the gear mechanism is conducted to obtain stress analysis. The results shows that the mechanism meets the strength requirements. Further dynamic numerical simulations are conducted on the outboard wing to determine the hazardous area of the load, indicating that the folding wing meets the strength requirements. At the same time, the analysis is conducted on the displacement at the tip of the outboard wing, indicating that the folding motion is relatively gentle. Finally, based on the stress analysis results, a weight reduction topology design is carried out for the spoke area of the gear and the rib structure of the folding wing using the variable density method. While ensuring strength, the optimal distribution of materials is sought by using as little material as possible, and the model is reconstructed according to the optimization results. The optimization results show that the weight reduction effect is significant.

Abstract: This paper presents the implementation of an early stage fault detection and health monitoring system for electric motors and their drive units. The study focuses on developing a cost-effective system capable of identifying abnormal behavior in both drive electronics and mechanical components before a major failure occurs. The proposed design integrates multiple sensing parameters such as vibration, acoustic signals, and electrical quantities including voltage and current. These inputs are processed using data-driven techniques to assess motor condition and identify fault patterns. A microcontroller-based platform is used for real-time monitoring and signal processing, providing early warnings through an intuitive serial interface. Experimental observations confirm that this approach can effectively detect drive faults, motor imbalance, and bearing wear at an early stage, reducing downtime and maintenance costs. This work demonstrates a practical and scalable method to enhance the reliability and operational safety of motor-driven systems, contributing to improved industrial efficiency and predictive maintenance strategies.

Abstract:

The Brown Planthopper, Nilaparvata lugens (Stål.) (Hemiptera: Delphinidae), is one of the most destructive pests of rice. Its reproductive and developmental traits are influenced by various environmental and biological factors including endosymbiotic microorganisms. Arsenophonus, a widespread endosymbiotic bacterium of insects, can affect host fitness and metabolic processes. This study investigates the role of Arsenophonus in modulating the developmental and reproductive traits of N. lugens fed on transgenic cry30Fa1 rice (KF30-14) and its parent variety Minghui 86 (MH86). Life table analysis revealed that Arsenophonus infection (Ars⁺) increased the development time and reduced the reproductive capacity of N. lugens, especially those feeding on KF30-14. The first-instar nymphs in MH86 Ars⁺ (infected) exhibited slower development compared to MH86 Ars^- (uninfected). Similarly, the third and fourth-instar nymphs in KF30-14 Ars⁺ exhibited prolonged development time compared to KF30-14 Ars^-. In addition, KF30-14 Ars⁺ females had significantly reduced reproductive capacity, smaller ovarian tubules and lower relative expression levels of reproduction-related genes including Trehalose transporter (Tret), Vitellogenin (Vg) and Cytochrome P450 hydroxylase (cyp314a1), while Juvenile hormone acid methyltransferase (JHAMT) expression was upregulated. RNA sequencing and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed significant enrichment of genes involved in lipid, amino acid, and vitamin metabolisms, with Long-chain acyl-CoA synthetase implicated as a key regulator of lipid metabolism and reproductive fitness. These results highlight the complex interactions between endosymbionts, host plants and pest biology, offering a solid foundation for sustainable approaches to control N. lugens in rice production systems.

Abstract: Image-to-PointCloud (I2P) place recognition is crucial for autonomous systems, facing challenges from modality discrepancies and environmental variations. Existing feature fusion strategies often fall short in complex real-world scenarios. We propose AttnLink, a novel framework that significantly enhances I2P place recognition through a sophisticated attention-guided cross-modal feature fusion mechanism. AttnLink integrates an Adaptive Depth Completion Network to generate dense depth maps and an Attention-Guided Cross-Modal Feature Encoder, utilizing lightweight spatial attention for local features and a context-gating mechanism for robust semantic clustering. Our core innovation is a Multi-Head Attention Fusion Network, which adaptively weights and fuses multi-modal, multi-level descriptors for a highly discriminative global feature vector. Trained end-to-end, AttnLink demonstrates superior performance on KITTI and HAOMO datasets, outperforming state-of-the-art methods in retrieval accuracy, efficiency, and robustness to varying input quality. Detailed ablation studies confirm the effectiveness of its components, supporting AttnLink's reliable deployment in real-time autonomous driving applications.

Abstract: Si MOSFETs are widely used in power conversion systems; however, long-term operation under repetitive switching and electro-thermal stress leads to progressive degradation and eventual failure. Two representative failure modes are commonly observed: gate-oxide degradation and packaging-related degradation, which often exhibit different evolution patterns. This paper proposes an AI-based diagnosis and prognostics framework that jointly leverages steady-state time-series information and fixed-length features extracted from turn-off transients. The study utilizes the NASA Open Accelerated-Aging dataset and reorganized/preprocessed data supported by MATLAB/Simulink measurement cir-cuit modeling. Physics-informed rule-based labeling is applied to discriminate normal, gate-oxide, and packaging-related conditions based on degradation indicators such as R_{ds_on} evolution. The trained model is further interpreted via permutation importance to quantify whether gradual/abrupt degradation indicators and transient features contribute to decision-making. Performance is assessed on held-out tests and synthesized cases sampled from baseline operating distributions to examine consistency under previously unseen conditions.

Abstract: This paper provides a analytical proof of the Riemann Hypothesis using a differential interaction operator Φ(s,δ) on the Hilbert space l²(N). By mapping the Dirichlet η-function to a trace-class operator representing the interaction between states shifted by ±δ from the critical line, we derive a Phase-Torque J(δ,t) governed by a hyperbolic sine bias. We establish a Product Criterion showing that the operator trace vanishes if and only if a zero exists at either 1/2 + δ + it or 1/2 − δ + it. Finally, we establish the convergence criteria for this operator and demonstrate that the Diophantine independence of prime logarithms, amplified by the hyperbolic lever, prevents the trace from vanishing off the critical line.

Abstract: The transition to a circular bioeconomy in developing economies is frequently hindered by the operational failure of biogas facilities due to feedstock supply discontinuity. Whilst biochemical potential is traditionally the primary selection criterion, this study postulates that logistic reliability serves as the governing constraint for sustainable implementation. To validate this paradigm shift, a spatially explicit decision-making framework integrating Shannon Entropy and TOPSIS was developed and applied to a representative tropical agro-industrial region. By evaluating conflicting criteria covering logistic availability, technical efficiency, and biochemical stability, the model identified Annual Residue Production as the dominant weighting factor. Results established Cattle Manure as the universal baseload substrate essential for mitigating seasonality risks, outweighing higher-yielding but intermittent agricultural residues. Spatial analysis further revealed distinct bioenergy clusters dictating tailored technological strategies: a high-potential Rice-Livestock cluster suitable for centralised industrial plants overseeing recalcitrant biomass, and a Cassava-Livestock cluster favourable for decentralised, low-tech digestion. This data-driven approach demonstrates that successful substrate selection must transcend theoretical yield maximisation to prioritise supply chain reliability, providing a robust roadmap for de-risking bioenergy investments in tropical regions.

Abstract: Tourism destination marketers increasingly rely on video advertising, yet the psycho-logical mechanisms linking perceived advertising design and destination familiarity to engagement remain underspecified in tourism contexts. Drawing on narrative trans-portation and advertising stimulation perspectives, this study examines how perceived advertising design and destination familiarity relate to narrative transportation and advertising stimulation, and how these mechanisms relate to engagement. Using a survey of 915 Portuguese respondents and structural equation modelling in AMOS, we estimate a model comprising advertising design, destination familiarity, narrative transportation, advertising stimulation, and engagement. Results show that perceived advertising design is positively associated with narrative transportation (β=0.451, p< 0.01) and advertising stimulation (β=0.158, p< 0.01). Destination familiarity is also positively associated with narrative transportation (β=0.215, p< 0.01) and advertising stimulation (β=0.104, p< 0.01). Narrative transportation strongly predicts advertising stimulation (β=0.659, p< 0.01), whereas narrative transportation Tourism destination marketers increasingly rely on video advertising, yet the psycho-logical mechanisms linking perceived advertising design and destination familiarity to engagement remain underspecified in tourism contexts. Drawing on narrative trans-portation and advertising stimulation perspectives, this study examines how perceived advertising design and destination familiarity relate to narrative transportation and advertising stimulation, and how these mechanisms relate to engagement. Using a survey of 915 Portuguese respondents and structural equation modelling in AMOS, we estimate a model comprising advertising design, destination familiarity, narrative transportation, advertising stimulation, and engagement. Results show that perceived advertising design is positively associated with narrative transportation (β=0.451, p< 0.01) and advertising stimulation (β=0.158, p< 0.01). Destination familiarity is also positively associated with narrative transportation (β=0.215, p< 0.01) and advertising stimulation (β=0.104, p< 0.01). Narrative transportation strongly predicts advertising stimulation (β=0.659, p< 0.01), whereas narrative transportation does not show a sig-nificant direct association with engagement (β=0.086, n.s.). Advertising stimulation is positively associated with engagement (β=0.288, p< 0.01). Findings suggest that, in this context, affective activation (stimulation) may be a more proximal correlate of self-reported engagement than narrative immersion alone, warranting careful inter-pretation given the cross-sectional, self-report design. does not show a sig-nificant direct association with engagement (β=0.086, n.s.). Advertising stimulation is positively associated with engagement (β=0.288, p< 0.01). Findings suggest that, in this context, affective activation (stimulation) may be a more proximal correlate of self-reported engagement than narrative immersion alone, warranting careful inter-pretation given the cross-sectional, self-report design.

Abstract: We present a geometric reinterpretation of cosmic expansion in which expansion is treated as an effective spatial dimension whose projection governs observed distances, time evolution, and physical interactions. By modelling the actual path followed by light through this expanded geometry, we introduce a spiral distance that reproduces observed luminosity and angular-distance relations without requiring accelerated expansion or an additional dark-energy component.Within this framework, gravity emerges as a local suppression of expansion, producing time dilation and curvature consistent with general relativity in the weak-field limit. Expansion is shown to be closely tied to the flow of time itself, with proper time corresponding to progression along the expansion direction and deviations from this trajectory giving rise to gravitational and kinematic time dilation. When applied consistently to both Type Ia supernova luminosity data and the angular scale of the cosmic microwave background, the framework naturally reduces the apparent discrepancy between late- and early-universe determinations of the Hubble constant.Extending the model to the quantum domain, we propose that wave–particle duality, spin, and probabilistic behaviour arise from partial delocalization within a finite temporal window. Electric charge is interpreted as a time-phase asymmetry associated with motion in the expansion dimension, with the electromagnetic coupling strength naturally linked to a dimensionless geometric ratio consistent with the fine-structure constant. Quantum entanglement is reinterpreted as a shared time-phase structure, preserving all experimentally verified predictions of quantum mechanics while providing an intuitive geometric explanation for nonlocal correlations without violating relativistic causality.The framework suggests several testable signatures, including limits on entanglement across extreme temporal separations, time-domain interference effects, and cross-scale correlations between quantum phenomena and gravitational time dilation. While fully compatible with existing observations, this approach offers a unified geometric interpretation connecting cosmology, gravity, time, and quantum behaviour, and motivates further theoretical development and experimental investigation.

Abstract: We demonstrate deterministic localization of cryptographic hash preimages within specific layers of deep neural networks trained on information-geometric principles. Using a modified Spin-Glass architecture, MD5 and SHA-256 password preimages are consistently identified in layers ES15-ES20 with >90% accuracy for passwords and >85% for hash values. Analysis reveals linear scaling where longer passwords occupy proportionally expanded layer space, with systematic replication in higher-dimensional layers showing exact topological correspondence.Critically, independent network runs with fresh initialization maintain 41.8% information persistence across 11 trials using unique hash strings and binary representations. Layer-to-layer correlations exhibit non-linear temporal coupling, violating fundamental assumptions of both relativistic causality and quantum mechanical information constraints. Pearson correlations between corresponding layers across independent runs approach ±1.0, indicating information preservation through mechanisms inconsistent with substrate-dependent encoding.These findings suggest the cryptographic "one-way property" represents a geometric barrier in information space rather than mathematical irreversibility. Hash function security may be perspectival accessible through dimensional navigation within neural manifolds that preserve topological invariants across initialization states. Results challenge conventional cryptographic assumptions and necessitate reconceptualization of information persistence independent of physical substrates.

Abstract: Lithium remains endorsed as first-line treatment for bipolar disorders across major clinical guidelines, yet robust evidence demonstrates its progressive decline in use in psychiatric practice across numerous countries. To justify this decline, concerns regarding lithium's efficacy, safety profile, and monitoring requirements are frequently cited. Yet, these apprehensions largely stem from misunderstanding of lithium's clinical uses. In fact, when patients are selected for lithium stabilization according to a characteristic clinical profile and not just a bipolar verdict, lithium continues demonstrating excellent efficacy compared to all other psychiatric medications currently available. Moreover, after sufficient clinician and patient education regarding lithium stabilization principles, monitoring requirements stop being burdensome. Furthermore, among lithium-responsive patients, adverse effects are typically mild and clinically manageable, except for glomerular filtration rate decline, which tends to develop after decades of continuous administration. Thus, it may be possible to reverse this unfortunate decline in lithium's use by teaching clinicians to identify the patient profile responsive to lithium stabilization, by investigating intermittent lithium administration to mitigate renal complications, and by implementing educational programs regarding optimal lithium utilization for psychiatrists, patients, and their families.

Abstract: Measles resurgence threatens elimination achievements in the Americas. We conducted a nationwide analysis of Mexico's 2025 measles outbreak, integrating individual-level surveillance data from the Special Surveillance System for Febrile Exanthematous Dis-eases with municipal-level social determinants from eight national databases, comple-mented by molecular surveillance data. We analyzed 6151 confirmed cases (epidemio-logical weeks 8–52, 2025) using spatial autocorrelation (Moran’s I, LISA), effective re-production number estimation, negative binomial regression, and logistic regression for risk factors. Cases concentrated in Chihuahua (73%), with 45 LISA hot-spot municipalities containing 71.68% of cases. Molecular surveillance confirmed two independent intro-ductions: D8/MVs/Ontario.CAN/47.24 (98.1%) linked to the Canadian outbreak, and B3 (1.9%) in Oaxaca. Transmission followed a three-stage pattern: introduction through seasonal agricultural worker networks, amplification in undervaccinated communities, and diffusion to marginalized indigenous populations. Vaccine effectiveness was 98.2%, with 83.4% of cases in pockets of susceptibles (municipalities with ≥80% unvaccinated). Risk factors for complications included age < 5 years (aOR 3.59), indigenous status (aOR 2.35), and unvaccinated status (aOR 2.03). Indigenous individuals comprised 30% of cases but 76% of deaths. This outbreak demonstrates that national vaccination thresholds are insufficient when marginalized populations remain systematically underserved.

Abstract: This research aims to compare the use of AI-powered technologies in the energy sector and discuss their role in enhancing the efficiency and sustainability of urban energy systems. The energy sector is both broad and specialized, with many technologies already developed. However, it continues to face challenges such as the simultaneous integration of various systems, cybersecurity concerns, and the further adaptation of renewable energy sources. AI has the potential to help address these issues. Additionally, the study will explore the risks associated with the transition to green energy and the widespread implementation of AI. The methods employed in this research include the analysis of statistical data and insights from various scientists. Therefore, this study seeks to provide a comprehensive approach to optimizing energy usage in cities through the utilization of AI.

Abstract: The root of the animal tree—whether sponges (Porifera) or ctenophores (Ctenophora) represent the earliest-branching lineage—remains a key unresolved question in evolutionary biology. This review synthesizes evidence from molecular sequences, rare genomic events, morphology, embryology, and paleontology. While molecular sequence data provide extensive coverage, they are susceptible to methodological errors and confounding evolutionary processes. Rare molecular events, such as chromosomal fusion-with-mixing, provide deeper resolution due to their low convergence potential and high irreversibility. Morphological and embryological traits, historically underestimated, benefit from advances in imaging and comparative gene expression. Fossil records, though direct, remain fragmentary and biased. To explain persistent conflicts among data types, we propose the concepts of a Resolution Limit and the Deep Basal Problem, which formalize why early divergences are so difficult to resolve. We introduce Highly Anti-Convergent and Highly Irreversible Marginal Instances (HACHIMIs) as a promising class of phylogenetic signals. In conclusion, while traditional datasets tend to support the Porifera-sister hypothesis, high-resolution data increasingly favor Ctenophora-sister. More broadly, this review argues that resolving deep phylogenies requires integrative methodological frameworks, not just more data.

Abstract: DNA methylation is a conserved regulatory mechanism of gene expression, genome stability, and development. DNA methylation modifications relate to effective induc-tion of defense responses for plant priming. In the Green Deal era, using plant defense inducers, compounds that activate defense and prime plants against imminent patho-gens attacks, is a safe and environmentally sustainable approach to support plants against pathogens. Here, salicylic acid loaded in chitosan nanoparticles, influenced hypomethylation on specific genomic regions that corresponded to defense-related genes, such as pectin lyases, defensins and leucine-rich repeat transmembrane protein kinases against the biotrophic fungal pathogen Podosphaera xanthii. A genomic region of the promoter of SKP1A, a core member of the SCF E3 ubiquitin ligase complex, was found to be a significantly hypomethylated DMR. Examination of this DMR revealed the presence of salicylic acid-, auxin-, and defense-related cis-elements. Investigation of proteins associated with the above cis-elements showed significant expression upreg-ulation after salicylic acid application. Moreover, association of the identified DMR with transcriptomics showed significant enrichment of the salicylic acid pathway. Overall, these findings shed light on the epigenetic mechanisms that underly salicylic acid- re-lated defense priming in plants.

Abstract: Background/Objectives: Cardiovascular disease is the leading cause of morbidity and mortality worldwide, of which the myocardial Infarction is the most prevalent. However, the underlying pathophysiological mechanisms remain incompletely understood, but are tightly regulated by several cellular mechanisms, including long-non-coding. This study aimed to determine if MEG3 and ATF4 are involved in this pathology. Methods: A cross-section study was conducted at the Instituto Nacional de Cardiología Ignacio Chávez, patients with first time diagnosis STEMI and hemodynamic stability were categorized into with and without major adverse cardiovascular events, the most important clinical and biochemical parameters were collected, which were analyzed and subsequently correlated with MEG3 and ATF4. Results: Forty-two patients with a median age of 54 years (86% men) were included and classified with and without MACE. The expression of MEG3 in MACE group and No MACE (0.8974, 0.4186–1.4131 vs. 1.2259, 0.5516–2.3964; p = 0.0342), and ATF4 in MACE group and No MACE (2.8950, 0.7559–4.3287 vs. 2.3498, 1.0821–3.6903; p = 0.0396), ROC curve MEG3 showed an AUC of 0.6490 (0.4760 to 0.8221; p = 0.0924), in contrast ATF4 demonstrated an AUC of 0.7127 (0.5862 to 0.8393; p = 0.0107). Finally, correlation analyses revealed MEG3 was associated with CK-MB (r = 0.3978, 0.0630 to 0.6520; p = 0.0219), and ATF4 was correlated cTnT (r = 0.3328, 0.0284 to 0.5810; p = 0.0335) and with LVEF (r = –0.4283, –0.6503 to –0.1390; p = 0.0052). Conclusions: The dysregulation of MEG3 and transcription factor ATF4 are involved in pathophysiological mechanisms.

Abstract: Quantum batteries aim to exploit collective and coherent quantum effects to enhance energy storage and charging performance. In this context, the Dicke model provides a paradigmatic platform in which an ensemble of two-level systems interacts collectively with a single cavity mode, potentially enabling superlinear scaling of the charging power. Here, we present a controlled numerical comparison between a collective Dicke quantum battery and a parallel, non-collective benchmark composed of independent two-level systems charged by separate cavity modes. By simulating the open-system dynamics using Lindblad master equations, we analyze the stored energy, optimal charging time, and average charging power as functions of the system size. We identify a clear crossover from superlinear to linear scaling of the charging power controlled by dissipation: collective advantages persist only when coherent light--matter coupling dominates over losses, approximately when $g \gtrsim \kappa + \gamma$. These results delineate the operational regimes in which collective quantum batteries can outperform non-collective architectures and clarify the limitations imposed by environmental decoherence.