This paper explores the application of geometric analysis to the study of non-linear diffusion processes. Traditional approaches often fail to fully capture the complex dynamics arising from non-linearity, hindering a deeper understanding of these phenomena. We introduce a novel technique that leverages curvature, symmetry, and geometric transformations to model and analyze diffusion, offering a robust framework for investigating the underlying mechanisms and potentially unlocking new insights into their behavior. The core focus is on establishing a mathematical foundation for analyzing these processes using geometric tools, providing a means to quantitatively assess the rate of change and identify key parameters. The paper details the methodology, presents preliminary results demonstrating the effectiveness of the technique, and concludes with a discussion of future research directions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The study aimed to develop and optimize chitosan-based mucoadhesive nanomicelles for intranasal delivery of lamotrigine (LTG), to enhance epilepsy treatment, bypass the blood-brain barrier, and potentially improve brain targeting. LTG-loaded nanomicelles were prepared using thin-film hydration and optimized using a central composite design, response surface methodology, and artificial neural networks. The formulation included D-ɑ-tocopheryl polyethylene glycol succinate, Poloxamer 407, chitosan, and glycerol. Critical quality attributes assessed were micelle size (MS), polydispersity index (PDI), Zeta potential (ZP), pH, LTG content, transmittance, in vitro mucoadhesion, LTG release, and 28-day stability. The MS, PDI, ZP, pH, and LTG content of the optimized mucoadhesive nanomicelles was 31.28 ± 0.34 nm, 0.487 ± 0.00, +31.37 ± 1.97 mV, 4.61 ± 0.01, and 2.89 ± 0.01 mg/mL, respectively. The transmittance was 98.50 ± 0.10%, and significant in vitro mucoadhesion, with reduced migration, was observed for mucin-containing gels. LTG release (96.94% at 6 hours) followed the Higuchi diffusion model, with sufficient LTG released at 40 minutes to potentially reach the minimum effective concentration, based on in vitro release data alone. The formulation remained stable for 28 days at 4 °C and 25 °C. Chitosan-based mucoadhesive nanomicelles are a promising intranasal delivery system for LTG, with the potential for brain targeting, controlled LTG release, and improved epilepsy management.
Siyabonga Melamane, Omobolanle A. Omoteso, Sandile M. Khamanga et al.· Figshare· 0 citations
ABSTRACT We present a physics‐guided neural framework that combines Monte Carlo simulations with experimental measurements to characterize optical properties and predict spectral responses at unseen conditions. The hybrid model jointly learns from simulations with complete optical characterization and experimental data where properties must be inferred through photon diffusion constrained embeddings, enabling knowledge transfer from synthetic to real materials. Applied to transparent wood composites, the framework characterizes wavelength‐dependent effective attenuation from only two experimental samples and accurately predicts optical responses at a third unseen thickness across all measured spectral quantities. Controlled validation on Monte Carlo simulations demonstrates that physics‐guided training reduces prediction errors by at unseen sample thicknesses compared to data‐driven approaches, preventing systematic spectral biases without compromising training performance. This framework enables accurate and non‐destructive material characterization from minimal experimental measurements, reducing the cost and time required for optical property determination across diverse sample configurations.
Fahime Seyedheydari, Kevin Conley, Hui Chen et al.· Advanced Theory and Simulati...· 0 citations
A self-contained 1-D drift-diffusion solver for ETL/perovskite/HTL solar cells, written in pure NumPy/SciPy. It couples Poisson's equation with the electron, hole and mobile-ion continuity equations and supports photoinduced halide-segregation (band-gap-coupled) modelling, steady-state J-V curves, scan-rate hysteresis and small-signal impedance spectroscopy.
Eka Nurfani· Zenodo (CERN European Organi...· 0 citations
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Stench safety foam core idea for honeycomb foam layer for composite panels that are designed to crack if subject to a set force of impact that then crack and release a chemical stench agent that indicates the panel its inside of needs to be replaced. Meant as a safety measure for lower cost and lower environmental impact vehicles, to offset less robust panel construction compared to conventional methods especially steel, to ensure that a cheaper construction while less durable is just as safe over the course of ownership. The panel would need to be designed with very specific parameters, to reflect crash modelling, along with an olfactory release mechanism, one that perhaps increased over time, giving the owner time to act, and the smell would need to escape the inside of the overall panel with the least amount of design considerations Using an olfactory indicator—a stench agent—embedded within the structural core of a composite panel is a creative solution to the "hidden fatigue" problem inherent in lightweight composites. Unlike steel or aluminum, which deform plastically (showing visible dents and bends), fiber-reinforced composites and sandwich structures often experience internal shear failure, core crushing, or matrix cracking while maintaining an apparently intact outer face-sheet. 1. Structural Mechanical Calibration To ensure the indicator triggers only when structural integrity is compromised—rather than during normal operational vibration or minor cosmetic bumps—the release relies on predictable failure modes in sandwich panel mechanics: +---------------------------------------------------+ <-- Outer Facing (Weather / Impact) | [ Microcapsules / Sealed Honeycomb Cells ] | <-- Core Layer (Shear Failure Zone) +---------------------------------------------------+ <-- Inner Facing (Ventilated / Permeable) Core Shear & Buckling Load Thresholds: The core foam or honeycomb matrix must be calibrated so that its elastic limit matches the maximum allowable load of the vehicle assembly. Under peak safe stress, the core deforms elastically. Once an impact exceeds this threshold (e.g., reaching 70\%\text{--}80\% of ultimate shear strength), brittle fracture or cell-wall buckling breaks the containment vessels embedded inside. Frangible Microcapsules vs. Sealed Honeycomb Chambers: Option A (Microencapsulated Olfactory Pellets): Liquid odorant is encapsulated in brittle, glass-like polymeric shells dispersed within the core resin matrix. Under shear stress or local crushing, the capsules rupture cleanly. Option B (Pressurized Honeycomb Core): Individual cells in an expanded honeycomb core are vacuum-sealed or lightly pressurized with a volatile gas/liquid agent. Impact ruptures the thin septum between cells, releasing the gas. 2. Chemical Agent & Time-Escalation Mechanism To prevent an instant, overwhelming cloud that could incapacitate a vehicle operator while driving, the release must escalate predictably over time. Volatility & Oxidation Cascade Primary Odor Compound: Safe, low-toxicity, extremely low odor-threshold compounds like ethyl mercaptan (the natural gas additive) or butyric/valeric acid derivatives (sour/rancid profile, detectable at parts-per-billion levels). Time-Delayed Escalation: Phase 1 (Immediate Evaporation): Rupturing releases a small, volatile top note near the crack site, giving a subtle initial warning. Phase 2 (Viscous Polymer Breakdown or Oxidation): The core contains a secondary, heavier liquid carrier (e.g., glycol-bound odor precursor). Once exposed to atmospheric oxygen or ambient moisture infiltrating through the crack, an oxidative reaction steadily breaks down the carrier, accelerating odor emission over hours to days. 3. Minimal-Design Venting Strategy The primary challenge is getting the odor out of the sealed panel into the open air without requiring complex ductwork, visible holes, or weakening the structural shell. Venting Strategy Mechanism Design Impact Differential Permeability Layer The inner (cab-facing or frame-facing) skin of the panel uses a polymer matrix that is gas-permeable under pressure, while the outer skin is fully impermeable to weather. Zero external geometry changes. Odor naturally diffuses inward toward vehicle frame/cabin. Micro-Perforated Inner Face-Sheet Laser-drilled micro-voids (<50\,\mu\text{m}) on the inner surface. Water surface tension prevents liquid ingress, but gas under capillary expansion escapes freely. Invisible to the user; retains original manufacturing tooling. Kerf & Bond-Line Pathways Channeling gas along existing adhesive bond lines or frame-mounting fastener points where sealing gaskets are located. Uses existing structural seam boundaries as natural relief paths. Key Engineering Challenges to Address Environmental Thermal Cycling: Vehicle panels undergo extreme ambient temperature shifts (-30^\circ\text{C} to +80^\circ\text{C}). The microcapsules or core chambers must resist thermal expansion pressures without premature cracking. Moisture & Chemical Degradation: If the odorant oxidizes slowly over time due to ambient air diffusion, false positives or degraded potency after 5–10 years could occur. Decontamination vs. Panel Replacement: Once triggered, the stench agent must be potent enough to compel vehicle servicing, but localized enough that replacing the single damaged panel completely removes the scent from the vehicle frame. Designing a load-concentrating "setting" or carrier for the indicator bead solves one of the biggest integration hurdles in composite manufacturing: decoupling the indicator's sensitivity from the resin formulation itself. Instead of relying on the panel resin or raw foam to transmit force evenly to a round sphere, a star-shaped or spider-legged carrier acts as a mechanical strain amplifier. 1. Mechanical Principle: Strain Amplification A spherical bead on its own requires direct compression across its diameter to fracture. However, composite panel failure often starts as interlaminar shear (plies sliding past each other) or in-plane tension/compression rather than direct crushing. [ Arm / Feeler ] [ Central Node ] [ Arm / Feeler ] <------------------------> ( Odor Bead ) <------------------------> | [ Notch / Stress Riser ] Leverage Arm Mechanics: The extended arms (feelers) bridge across multiple fibers or honeycomb cells. When the surrounding matrix deforms, the long arms act as levers (M = F \times d), transferring and multiplying small structural displacements into concentrated bending moments at the central housing. Controlled Notch Sensitivity: By molding intentional V-notches or thin-wall relief zones where the arms meet the central pocket, you create predictable crack-initiation sites. The setting fractures cleanly at a fraction of the force needed to crush an isolated sphere. 2. Directional Sensitivity & Strategic Placement By varying the orientation and geometric profile of the setting arms, you can tune the setting to respond to specific crash vectors: Failure ModeArm ConfigurationBehavior Under Load Delamination (Shear)Horizontal, flat 4-point star aligned with ply interfacePlies sliding relative to each other force opposing arms apart, snapping the central pocket. Core Crushing (Impact)3D tripod / tetrahedral legs bridging upper/lower face-sheetsVertical impact buckles the tripod legs inward, pinching and shattering the central capsule. Bending / FlexureAsymmetrical "H" or star with unequal leg lengthsLong legs catch maximum curvature strain during panel deflection, triggering before catastrophic face-sheet rupture. [ External Facing / Impermeable Outer Skin ] -------------------------------------------------- [ Matrix / Core Layer ] \ --> [ Star-Shaped Strain-Amplifying Setting ] ├── Long Leverage Arms (Bridge fiber plies / core cells) ├── Engineered V-Notch Stress Risers └── Central Encapsulated Olfactory Pellet -------------------------------------------------- [ Laser-Drilled Micro-Perforated Inner Skin ] --> Venting to Frame/Cabin Strain-Amplifying "Star" Carrier Setting: A discrete molded node featuring multi-point leverage arms (feelers) that extend across adjacent plies or core cells. Translates distributed matrix deformation (shear, flexure, or core buckling) into concentrated bending stress at engineered V-notches in the central housing. Decouples damage sensitivity from primary matrix chemistry, allowing standard resins and fast assembly. Trigger & Venting Mechanism: Fracture of the central setting ruptures embedded, brittle microcapsules containing a volatile olfactory top note combined with a slower-evaporating oxidative carrier (escalating odor profile over hours/days). Molded capillary micro-channels on the underside of the star legs direct the released gas straight to invisible laser-drilled holes (<50\,\mu\text{m}) in the inner panel skin, venting into the vehicle frame/cabin without compromising external weatherproofing or aesthetics. 3. Key Manufacturing & Implementation Advantages Automated Integration: Carrier settings are reeled and placed onto raw core materials via standard Automated Tape Laying (ATL) or pick-and-place robotics prior to resin infusion. Thermal Isolation: The carrier housing thermally insulates sensitive odorant chemistry during high-temperature resin transfer molding (RTM) or compression curing. Targeted Calibration: Leg lengths, angles, and notch depths can be tuned independently for specific failure v ectors (e.g., side-impact shear vs. vertical core crush).
Michael Adam Ryan· Zenodo (CERN European Organi...· 0 citations
Artificial intelligence is rapidly becoming embedded in everyday life through an expanding range of applications, services, and products. As its potential to improve diagnosis, personalize treatment, and enhance operational efficiency becomes increasingly evident, healthcare is undergoing profound transformation. However, trust and distrust operate as dual mechanisms shaping technology diffusion: trust facilitates adoption, whereas distrust constrains large-scale deployment. Trust therefore remains a persistent barrier to the widespread use of artificial intelligence in healthcare services. At present, empirical evidence on the pathways linking trust and acceptance of AI medical conversational agents (AIMCAs) remains limited. Grounded in trust theory, this study aimed to develop and validate a multidimensional trust-perception scale for AIMCAs, establish its dimensional structure and psychometric quality, and examine its associations with an external acceptance-related behavioral criterion. Methodologically, the study first used grounded-theory-informed abductive qualitative analysis to identify the structure of public trust perceptions of AIMCAs and generated and screened measurement items through expert Q-sorting; independent samples were then used for exploratory and confirmatory factor analyses, followed by assessments of internal consistency, test-retest reliability and absolute agreement, within-construct indicator convergence, discriminant validity, and criterion-related validity. Parallel analysis and the scree plot jointly supported a five-factor solution. Principal axis factoring with Direct Oblimin oblique rotation yielded a 15-item, five-dimensional structure, with the five common factors explaining 76.07% of the total variance. Confirmatory factor analysis further supported a five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust; model-fit indices, standardized factor loadings, latent-variable correlations, and residual diagnostics collectively provided evidence for its internal structure. The Fornell-Larcker criterion and bootstrap confidence intervals for HTMT jointly provided evidence for internal discriminant validity among the five AIMCA trust dimensions. An ordinal logit model using actual use frequency as an external behavioral criterion was statistically significant overall, likelihood-ratio χ²(5) = 71.963, p < 0.001, McFadden pseudo-R² = 0.125. Interactional trust showed the strongest association with higher use frequency (OR = 3.096, 95% CI [2.257, 4.246]). The resulting scale captures multidimensional public trust perceptions of AIMCAs and provides a structured measurement basis for research on acceptance-related behavior. The findings support a 15-item, five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust. An ordinal logit model using self-reported AIMCA use frequency as an external behavioral criterion provided additional criterion-related evidence. The scale can be used to characterize multidimensional public trust perceptions of AIMCAs and provides a structured measurement foundation for subsequent research on acceptance, use intention, continuance intention, and actual use.
Hemin Du, Wumin Ouyang, Y X Han et al.· Scientific Reports· 0 citations
The transition from foraging to farming---the Neolithic transition---represents one of the most consequential reorganizations of human subsistence, society, and ecology in the history of our species. This article offers a narrative review of the principal theories advanced to explain why, where, and how agriculture emerged, tracing the intellectual arc from Childe's oasis hypothesis and the "Neolithic Revolution" paradigm to contemporary multi-causal syntheses informed by archaeology, chronometry, and genomics. The review examines the classic models of the twentieth century, including Braidwood's hilly-flanks proposal, Binford's demographic-pressure model, Flannery's broad-spectrum scheduling, and Rindos's coevolutionary framework, before turning to the wave-of-advance account of demic diffusion, Hodder's symbolic reading of the domus, and the climatic stresses associated with the Younger Dryas. More recent contributions---Zeder's pathway-based synthesis, Bellwood's farming-language dispersal hypothesis, and genomic reassessments of domestication---are discussed as steps toward an integrated account in which sedentism, population growth, environmental instability, and intentional management interact rather than compete. It is concluded that no single-factor explanation withstands scrutiny: the Neolithic transition is best understood as a protracted, multi-regional, and mutually reinforcing process whose consequences for demography, disease, social inequality, and human-environment relations continue to shape the modern world.
Zen Revista, 10 HISTORY· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to simulating complex biological systems utilizing graph-based modeling and simulation techniques. The core claim is that traditional simulation methods often face significant computational limitations when dealing with intricate biological systems. This limitation stems from the exponential growth of computational complexity with increasing system size and interaction density. The proposed solution involves representing biological systems as graphs, where nodes represent individual biological entities (e.g., genes, proteins, cells, organisms) and edges represent the interactions between them. This graph representation allows for the application of efficient graph algorithms and simulation techniques, dramatically reducing computational burden. We detail the methodology, including graph construction, node and edge attributes, and simulation algorithms tailored for biological systems. The approach demonstrates scalability and offers a viable alternative for modeling complex biological interactions, particularly those involving large numbers of components and intricate feedback loops. We explore various simulation techniques applicable within this framework, such as random walks, message passing, and network diffusion, and discuss their suitability for different biological scenarios. The results, while hypothetical due to the absence of experimental data, illustrate the potential of this method for generating insights into system dynamics and identifying key regulatory pathways. The ultimate goal is to provide a robust and scalable platform for understanding the behavior of complex biological systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the potential of quantum-enhanced diffusion processes to revolutionize generative modeling. Traditional diffusion models, while effective, suffer from computationally intensive training times and limitations in generating diverse and accurate outputs. We propose a novel approach leveraging quantum annealing and Grover's algorithm to dramatically accelerate the diffusion process, thereby improving both speed and accuracy. This research explores the theoretical foundations of QEP, outlines the implementation details, and presents preliminary results demonstrating significant performance improvements compared to conventional diffusion methods. The core mechanism centers around quantum acceleration of the forward and reverse diffusion steps, offering a pathway to overcome limitations in traditional algorithms. The investigation touches on the implications of this technology for various generative modeling applications, including image generation and data synthesis.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Datasets used in the paper "Training-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples" Contains 1000 images generated by SD3.5 and Flux-schnell models using the prompts from the paper "Synthbuster: Towards Detection of Diffusion Model Generated Images". The prompts can be downloaded using the official link: https://doi.org/10.5281/zenodo.10066460
Roman Demchenko· Zenodo (CERN European Organi...· 0 citations
Meteorological statistical downscaling is a critical technique for deriving high-resolution climate information from coarse global reanalysis products. This experiment investigates the application of five representative deep learning architectures for downscaling ERA5 reanalysis 2m temperature fields from 1° to 0.25° spatial resolution (4x upscaling factor) over the China region, spanning the period 2010 to 2020. The five baseline models evaluated are Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), Long Short-Term Memory network (LSTM), Vision Transformer (ViT-style), and Denoising Diffusion Probabilistic Model (DDPM). Building upon the analysis of baseline strengths and weaknesses, a sixth model is proposed, the Physics-Informed CNN (PICNN), which integrates multi-scale feature extraction, spatial attention mechanisms, and a composite physics-informed loss function incorporating mean squared error, Laplacian spatial smoothness regularization, and spatial energy conservation constraints. All models were trained on an NVIDIA GeForce RTX 5050 Laptop GPU with 8GB VRAM under computational constraints, with training epochs reduced from default values to accommodate hardware limitations. Experimental results on the held-out 2020 test set demonstrate that the proposed PICNN achieves the best performance among all models, with RMSE of 0.665 K, MAE of 0.435 K, PSNR of 43.43 dB, and SSIM of 0.976, representing improvements of 7.7% in RMSE and 8.7% in MAE over the strongest CNN baseline. To determine whether this improvement reflects the architectural design or simply the 84% increase in parameter count relative to the CNN baseline, a controlled ablation study was conducted comprising a capacity-matched plain CNN and three component-ablated PICNN variants, evaluated across three random seeds for the ablated variants. The results show that approximately 65.5% of PICNN's improvement over the CNN baseline is explained by parameter count alone. Among PICNN's three architectural components, spatial attention accounts for the majority of the remaining gain, the physics-informed loss contributes a small but consistent improvement, and the multi-scale feature extraction head shows no measurable benefit, with its ablated variant statistically indistinguishable from the full model. These findings refine the paper's central claim from a general endorsement of physics-informed design toward a specific, evidence-backed identification of which architectural choices are responsible for the observed gains. The Transformer model, despite having 139 million parameters, performed significantly worse than the CNN, highlighting the data inefficiency of attention-based architectures on moderate-sized meteorological datasets. The LSTM performed worst overall due to its inherent inability to preserve spatial structure.
The study aimed to develop and optimize chitosan-based mucoadhesive nanomicelles for intranasal delivery of lamotrigine (LTG), to enhance epilepsy treatment, bypass the blood-brain barrier, and potentially improve brain targeting. LTG-loaded nanomicelles were prepared using thin-film hydration and optimized using a central composite design, response surface methodology, and artificial neural networks. The formulation included D-ɑ-tocopheryl polyethylene glycol succinate, Poloxamer 407, chitosan, and glycerol. Critical quality attributes assessed were micelle size (MS), polydispersity index (PDI), Zeta potential (ZP), pH, LTG content, transmittance, in vitro mucoadhesion, LTG release, and 28-day stability. The MS, PDI, ZP, pH, and LTG content of the optimized mucoadhesive nanomicelles was 31.28 ± 0.34 nm, 0.487 ± 0.00, +31.37 ± 1.97 mV, 4.61 ± 0.01, and 2.89 ± 0.01 mg/mL, respectively. The transmittance was 98.50 ± 0.10%, and significant in vitro mucoadhesion, with reduced migration, was observed for mucin-containing gels. LTG release (96.94% at 6 hours) followed the Higuchi diffusion model, with sufficient LTG released at 40 minutes to potentially reach the minimum effective concentration, based on in vitro release data alone. The formulation remained stable for 28 days at 4 °C and 25 °C. Chitosan-based mucoadhesive nanomicelles are a promising intranasal delivery system for LTG, with the potential for brain targeting, controlled LTG release, and improved epilepsy management.
Siyabonga Melamane, Omobolanle A. Omoteso, Sandile M. Khamanga et al.· Figshare· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.