Abstract: Intercellular mitochondrial trafficking has emerged as an important mechanism influencing tumor progression, metabolic adaptability, and cancer cell plasticity. Beyond their classical bioenergetic functions, mitochondria act as central regulators of redox homeostasis, signalling pathways, and epigenetic remodelling. Increasing evidences suggest that mitochondria can be transferred between tumor, stromal, and immune cells through tunnelling nanotubes (TNTs), extracellular vesicles (EVs), gap junctions, and cell fusion within the tumor microenvironment. This dynamic exchange enables metabolically compromised cancer cells to restore oxidative phosphorylation, optimize energy production, and survive under hypoxia and therapeutic stress. Mitochondrial transfer has been increasingly associated with enhanced cellular plasticity and adaptive phenotypic transitions, including the acquisition of stem-like features that contribute to tumor heterogeneity, metastasis, and treatment resistance. In addition to bioenergetic restoration, transferred mitochondrial DNA and metabolites participate in retrograde signalling, linking metabolic state to epigenetic regulation and transcriptional reprogramming. This metabolic epigenetic interplay supports tumor cell adaptation to environmental stress and therapeutic pressure. Although significant progress has been made, the precise mechanisms governing mitochondrial integration and their long-term impact on cellular phenotypes remain incompletely understood. A deeper understanding of these processes may reveal new therapeutic strategies to disrupt tumor adaptability and improve treatment outcomes.

Abstract: We propose a topology-constrained quantized nnUNet framework for efficient and anatomically accurate 3D tooth segmentation, addressing the challenges of spatial distortion introduced by quantization in deep learning models. The proposed method integrates a novel tooth-specific topological loss into quantization-aware training, preserving critical anatomical structures such as tooth count, adjacency relationships, and cavity integrity while maintaining computational efficiency. The system employs an 8-bit quantized nnUNet backbone, where weights and activations are dynamically calibrated to minimize precision loss during inference. Furthermore, the topological loss combines connected-component analysis, adjacency consistency, and hole detection penalties, ensuring anatomical fidelity without modifying the underlying network architecture. The joint optimization objective harmonizes cross-entropy loss, quantization regularization, and topological constraints, enabling end-to-end training with gradient approximations for persistent homology terms. Experiments demonstrate that our approach significantly reduces topological errors compared to conventional quantized models, achieving clinically plausible segmentations on dental CBCT scans. The method retains the hardware efficiency of integer-only inference, making it suitable for deployment in resource-constrained clinical environments. This work bridges the gap between computational efficiency and anatomical precision in medical image segmentation, offering a practical solution for real-world dental applications.

Abstract: Smart microgrids depend on continuous communication between controllers, sensors, and actuators over industrial protocols like Modbus TCP, MQTT, and DNP3, that were designed without built-in security mechanisms. The gateway that aggregates this traffic represents a single point of failure vulnerable to distributed denial-of-service (DDoS) attacks. Most existing detection methods require labeled attack data for training, a condition rarely met in operational OT environments. This paper presents an unsupervised CNN-LSTM model trained exclusively on normal microgrid gateway traffic to predict the next traffic window; anomalies are flagged when prediction error exceeds a threshold derived from the training distribution. A dual-branch architecture processes metric time-series through LSTM layers and flow aggregate features through CNN layers, fusing both representations for prediction. The model is evaluated against three protocol-specific DDoS attack scenarios, Modbus SCADA flooding, MQTT publish storm, and DNP3 response flooding - none of which are seen during training. Compared against an Isolation Forest baseline under identical unsupervised conditions, the CNN-LSTM achieves higher precision and recall on all attack types. The framework is deployed within a web-based monitoring platform that supports real-time detection and anomaly logging.

Abstract: Cities are in the middle of a parking transition. Minimum parking requirements are being reduced or eliminated, curbs are being repriced, and the goal of planning is shifting from supplying more parking to making better use of the parking that already exists. Yet most parking analytics still answer a question that this transition has retired: where should we build more? We argue that the distinctive value of agentic AI in parking is not better prediction of where to build, but the ability to expose contradictions that conventional workflows suppress—when demand says build but policy says restrain; when inherited rules say comply but theory says question; when market logic says maximize but equity says redistribute; and when stated public frustration says “parking crisis” but utilization data say the supply is ample and mispriced. Parking planning should be reconceptualized as a dynamic, theory-grounded, policy-constrained, human-supervised decision process, organized around a loop between parking theory, parking policy, urban data, agent reasoning, human deliberation, and policy revision—and ultimately answering a political question: what kind of city do we want to be? Under this view, an agentic parking system must be able to recommend shared parking, existing-stock reuse, curb and price reform, and deliberate non-construction, not only new supply. Using the Phoenix Parking Lot Planner as a critical demonstration—critical because its current weighted-factor scoring is precisely the kind of reasoning the proposed loop is meant to transcend—we outline a research agenda and five evaluation standards: contradiction detection, intervention comparison, justification quality, restraint capability, and policy traceability. Parking, precisely because it is measurable, theory-rich, policy-contested, and intervention-ready, may be the most realistic near-term testbed for agentic urban planning.

Abstract: Background/Objectives: Radial artery spasm (RAS) is an important complication during transradial coronary angiography that may negatively affect procedural success and reduce patient comfort. The aim of this study was to comparatively evaluate the effects of intravenous (IV) and intra-arterial (IA) heparin administration on the development of RAS. Methods: This prospective, case-control study included a total of 223 patients undergoing transradial coronary angiography. Patients were divided into two groups as received either IV heparin (n = 77) or IA heparin (n = 146). All patients received a standard dose of unfractionated heparin (5000 IU) and an IA spasmolytic cocktail consisting of 2.5 mg verapamil and 100 mcg nitroglycerin. RAS was defined as pain during the procedure, resistance during catheter manipulation, or the need for crossover. Logistic regression analysis and receiver operating characteristic (ROC) curve analyses were performed. Results: RAS developed in 40 of 223 patients (17.9%). The incidence of RAS was significantly higher in the IA heparin group than in the IV heparin group (22.6% [33/146] vs. 9.1% [7/77]; p=0.004). Crossover to femoral access due to severe spasm was observed only in the IA group (6.2% [9/146] vs. 0% [0/77]; p=0.026). Patients who developed RAS were younger, required a greater number of catheters, had longer angiography duration, and were exposed to a higher total radiation dose (p<0.05 for all). Correlation analysis demonstrated a positive association of RAS with the number of catheters used and IA heparin administration, and a negative association with age. In ROC analysis, IA heparin administration, number of catheters used, and angiography duration showed comparable performance in predicting RAS. In multivariable logistic regression analysis, IA heparin administration and the number of catheters used were identified as independent predictors of RAS. Conclusions: During transradial coronary angiography, intravenous heparin administration significantly reduces the frequency of RAS and the associated need for femoral crossover compared with intra-arterial administration. IV heparin may be considered an easily applicable and effective strategy in clinical practice to prevent RAS.

Abstract: Background/Objectives: HIV/AIDS is a chronic disease with an increasing prevalence that requires serious attention, particularly in improving oral health status among people living with HIV/AIDS. Stigma and discrimination remain significant psychosocial challenges, while antiretroviral therapy (ART), as the primary treatment, plays an essential role in maintaining health stability. The Oral Hygiene Index Simplified (OHI-S) is a clinical indicator reflecting oral hygiene status and is particularly relevant among in adolescents living with HIV, as poor oral hygiene increases the risk of opportunistic infections and is influenced by psychosocial factors such as stigma and discrimination Methods: This study aimed to examine the effects of stigma, discrimination, and ART adherence on oral hygiene status (OHI-S) among in adolescents living with HIV/AIDS in Gorontalo City. This study employed an analytical survey with a cross-sectional design. A total of 390 participants were selected using purposive sampling. Data were analyzed using ordinal logistic regression at the 0.05 significance level Results: The results showed that stigma (p = 0.000; OR = 0.028) and discrimination (p = 0.006; OR = 7.32) significantly influenced oral hygiene status. However, ART adherence was not significantly associated with OHI-S (p = 0.708; OR = 0.761). Conclusions: Oral hygiene status among in adolescents living with HIV/AIDS is more strongly influenced by psychosocial factors than clinical factors.

Abstract: Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions.

Abstract: The energy balance model (EBM) has dominated human body weight regulation research for nearly a century, yet its reliance on indirect mass-to-energy conversions introduces propagated uncertainties that obscure the stoichiometric mechanisms governing tissue accretion and loss. A mass balance model (MBM), which tracks macronutrient mass flows directly in grams without intermediary energy-unit transformations, has recently been proposed as a conceptually simpler, mathematically consistent, and mechanistically faithful alternative. However, widespread adoption of the MBM has been hindered by the absence of standardized protocols, validated analytical frameworks, and practical implementation guidance. This paper fills that gap. I provide a comprehensive, step-by-step guide to MBM implementation, organized into five interdependent modules: (1) quantification of mass intake via precise food and beverage weighing with macronutrient composition analysis, (2) respiratory gas exchange measurement by indirect calorimetry for stoichiometric determination of substrate oxidation, (3) 24-hour urine and fecal collection protocols for nitrogen and carbon outflow quantification, (4) body composition assessment methods for independent validation of MBM predictions, and (5) data integration and computational workflows that produce complete daily mass balances for carbon, nitrogen, and water. The mathematical and computational framework is fully specified, including the core dynamic equation, derivation of the mass clearance coefficient, and prediction of body composition trajectories via Forbes's relationship. Translational applications are discussed, including early detection of lean tissue loss, real-time dietary monitoring, personalized protein prescription, and pharmacotherapy evaluation. By equipping researchers and clinicians with the tools necessary to adopt direct mass accounting, this paper aims to accelerate the transition from an energy-centric to a mass-centric paradigm in human metabolism research.

Abstract: This study investigated the influence of marketing infrastructure and digital marketing strategies on the performance of small and medium enterprises (SMEs) in Nigeria, emphasizing the mediating role of digital transformation. While prior research has established the importance of digital marketing strategies in driving performance, much of the focus has been on large firms, with limited attention to SMEs and the transformative effects of digital transformation. To address this gap, data were collected from 400 SME managers and owners registered with the Small and Medium Enterprises Development Agency of Nigeria (SMEDAN) through an online survey. The data were analyzed using partial least squares structural equation modelling (PLS-SEM). Findings revealed that marketing infrastructure and digital marketing strategies do not directly improve marketing performance; rather, digital transformation serves as a critical mediator that enables this relationship. The study concludes that SMEs that embrace digital transformation, by integrating digital technologies across operations, achieve superior marketing outcomes, including enhanced brand awareness, customer acquisition, conversion rates, and customer satisfaction, ultimately leading to higher sales, profitability and and business sustainability.

Abstract: Feline Infectious Peritonitis (FIP) has been transformed from a fatal disease to a treatable condition following the introduction of GS-441524, a nucleoside analogue targeting feline coronavirus replication. However, the widespread use of unregulated compounded formulations and the absence of validated analytical tools for therapeutic drug monitoring (TDM) represent critical gaps in clinical FIP management. This study describes the development and full ICH M10-compliant validation of a high-throughput LC-MS/MS method for the quantification of GS-441524 in feline serum, incorporating an automated protein precipitation protocol and a PBS-BSA surrogate matrix in accordance with 3Rs principles. The method met all acceptance criteria across validated parameters, including linearity (0.1–50 µg/mL), accuracy (bias within ±12.5%), precision (CV ≤10.9%), selectivity, extraction recovery (87.5–107.9%), and stability under clinically relevant storage conditions. Matrix equivalence between PBS-BSA and authentic feline serum was confirmed, enabling routine calibration without animal-derived materials. The validated method was applied to clinical TDM in cats undergoing GS-441524 treatment for FIP, providing preliminary evidence of inter-individual pharmacokinetic variability. The compounded formulations administered to the TDM cohort were independently verified by LC-MS/MS, confirming drug content within ±15% of labelled claims and excluding pharmaceutical quality as a confounding factor in the interpretation of serum drug concentrations.

Abstract: Achieving scalable monitoring of Alternate Wetting and Drying (AWD) for methane mitigation in rice cultivation depends on establishing field benchmarks for drainage behavior and demonstrating that satellite observations can reliably detect corresponding changes in water status. We analyzed about two million high-frequency in situ water-level observations from hundreds of sensors deployed in rice fields across Philippines and Japan to quantify drainage duration from near-surface conditions to 15 cm below the soil surface and to test the sensitivity of open-access PALSAR-2 dual-polarization L-band SAR to vertical water level variations. Across 564 drainage events, the median drainage duration was 19.0 h, and only 0.9% of events exceeded 240 h, indicating that drainage happens generally within a day. Drainage duration varied markedly by region and season, with median values ranging from 10.6 h in Pangasinan wet-season events to 72.6 h in Cagayan dry-season events; multiple drainage events occurred in 48.0% of Philippine dry-season fields but only 21.6% of wet-season fields. PALSAR-2 data shows a statistical significance in detecting inundation at Mid crop growth stage with cross-polarization band, but the significant overlap induce challenges in operational applications. These results provide empirical benchmarks for AWD-related drainage dynamics while showing that dual-polarization PALSAR-2 alone is unlikely to support robust field-scale monitoring of rice-field water status.

Abstract: The proliferation of urban air pollution—particularly fine particulate matter (PM2.5) —demands scalable monitoring approaches that go beyond the sparse networks of reference-grade stations installed in most cities worldwide. Over the past decade, deep learning methods applied to ground-level photographs have emerged as a low-cost complement to conventional sensing: convolutional neural networks (CNNs) and hybrid architectures can extract visually detectable pollution cues—haze opacity, color temperature shifts, and reduced horizon contrast—and map them onto continuous air-quality index (AQI) or PM2.5 estimates. This review synthesizes 40 primary studies and several additional supporting sources published between 2020 and 2025 to characterize the state of the art in image-based AQI estimation, identify the key technical and infrastructural limitations, and outline research directions relevant to data-scarce, under-monitored cities such as Bishkek, Kyrgyzstan. Three interlocking themes structure the review: (1) deep learning architectures and training strategies, from single-modality CNNs to multimodal and spatiotemporal hybrid models; (2) dataset characteristics and their decisive influence on regression accuracy; and (3) the monitoring infrastructure gap in low-income and middle-income cities of Central Asia and comparable regions. The evidence consistently shows that positive R2 values require at least 3,000–5,000 labeled image–pollutant pairs, controlled temporal stratification, and, ideally, auxiliary meteorological inputs. Promising directions include vision transformers, structured state-space models, Grad-CAM interpretability, and cross-city transfer learning. The review concludes with a structured research agenda for image-based air-quality monitoring in Central Asia.

Abstract: In protein solutions, an additive that increases protein-protein attractive interactions is expected to decrease protein crystal solubility and raise temperature of liquid-liquid phase separation (LLPS). In contrast, addition of 0.10-M 4-(2-hydroxyethyl)-1-piperazineethanesulfonate (HEPES) to lysozyme-NaCl aqueous solutions at constant pH (7.4) and ionic strength (0.20 M) decreases solubility but lowers LLPS temperature. This leads to a broadening of LLPS metastability gap in the phase diagram and an enhancement of protein crystallization yield from LLPS. We theoretically examine the effect of HEPES on both solubility and LLPS boundaries using a colloid model. Under the hypothesis that HEPES stabilizes protein-protein contacts in the crystal lattice by physical cross-linking, we apply cell theory to describe the thermodynamic behavior of the crystalline phase and use solubility data to show that HEPES increases protein-protein attraction energy by 2.7%. Since an increase in attraction incorrectly predicts a raise in LLPS temperature, we consider that HEPES also enhances the anisotropic character of protein-protein interactions. To describe the thermodynamic behavior of the solution phase, we start from Barker-Henderson second-order perturbation theory on the hard-sphere reference fluid with square-well potential and local-compressibility approximation. We modify this model so that it can reproduce the correct mathematical expression of the second virial coefficient. This also leads to a better agreement with Monte Carlo simulations. We then approximately incorporate anisotropy by assuming that the square-well attraction energy is a temperature-dependent average over all particle surface with a given fractional coverage of attractive spots. The attraction energy of the attractive spots is set to be the same as that of protein-protein contacts in the crystal. Only fractional coverage (anisotropy) was varied to successfully fit the effect of HEPES on the LLPS boundary.

Abstract: Hyperspectral imaging (HSI) is emerging as a promising tool in scientific endeavours, including non-invasive quantification of pigments, an area of research with many use cases. Here, we tested the efficacy of a low-cost (~USD $200) and open-source HSI device (400 – 1000 nm, a spectral resolution of ~2 nm FWHM) in coral bleaching research. Specifically, we evaluated its ability to quantify the concentration of key photosynthetic pigments (chlorophyll a, chlorophyll c2, diadinoxanthin, peridinin, and total pigment content) was compared against a research-grade (~USD $70,000) commercial hyperspectral camera using coral fragments subjected to varying levels of thermal stress. The low-cost HSI acquired coral reflectance spectra that were similar to the commercial hyperspectral camera, with a mean spectral angle of 11.38 ± 3.82°. However, the low-cost device was unable to resolve differences in spectral magnitude to the same accuracy as the commercial HSI and did not detect differences among coral fragments at different levels of thermal stress. Thus, the current HSI device prototype is better suited for classification and diagnostic applications where spectral shape is of greater importance than spectral magnitude. Partial Least Squares Regression models built from the reflectance spectra of each HSI instrument showed very similar yet moderate performance when predicting key coral pigments (Commercial HSI mean %RMSEP = 22.8%, low-cost HSI = 22.12%). While the current design of the low-cost HSI device has clear limitations, the results show potential in a system that costs a fraction of commercial alternatives. Continued development of low-cost HSI platforms is accelerating rapidly across a variety of fields in environmental research, and improved designs have the potential to enhance coral reef monitoring and restoration efforts globally.

Abstract: This paper proposes a novel Topological Data Analysis (TDA) pipeline to extract robust structural features from functional near-infrared spectroscopy (fNIRS) signals for the classification of Alzheimer's Disease (AD) stages. Alzheimer's disease is increasingly understood as a disconnection syndrome, where the disruption of functional brain net-works precedes gross anatomical atrophy. However, traditional graph-theoretic ap-proaches rely on arbitrary connectivity thresholds, which can obscure critical multi-scale topological information and are sensitive to noise. To address this, our framework lev-erages Persistent Homology (PH) to analyse the topological evolution of brain networks across a continuous range of scales. By modeling 48-channel hemoglobin concentration time-series as high-dimensional point clouds via Granger causality metrics, we construct filtration sequences of Vietoris-Rips complexes. The resulting topological invari-ants—specifically 0-dimensional connected components, 1-dimensional loops, and 2-dimensional voids—are captured in Persistence Diagrams and subsequently vectorized into Persistence Images (PIs) using Gaussian kernel smoothing. This transformation enables the integration of complex topological features into standard machine learning workflows. Our experimental results on 284 recordings demonstrate that this topolo-gy-driven feature extraction method yields high discriminative power, achieving 77% accuracy in multi-class diagnosis (NC vs. MCI vs. AD). This study validates the efficacy of TDA as a sophisticated signal processing tool for revealing intrinsic neurodegenerative patterns in hemodynamic data, offering a potential non-invasive biomarker for early detection.

Abstract: 3D Gaussian Splatting (3DGS) degrades severely in sparse-view scenarios, often collapsing into artifacts due to under-constrained optimization. While incorporating monocular depth priors provides dense supervision, their inherent multi-view inconsistency frequently distorts geometry. To address this, we propose GeoTrack-GS, a geometry-first framework that refines noisy depth priors using reliable self-supervised constraints. Specifically, we leverage sparse feature tracks to enforce macro-level reprojection consistency and introduce a micro-level anisotropic regularizer via K-NN PCA to suppress rank-collapse. On this corrected geometry, we design GT-DCA, a geometry-guided deformable cross-attention module that captures view-dependent appearance without compromising structure. A Decoupled Constraint Stabilization strategy further balances these heterogeneous signals during training. Experiments on LLFF and DTU under 3-9 input views, and on Mip-NeRF 360 under 12 input views, demonstrate that GeoTrack-GS achieves state-of-the-art geometric fidelity while maintaining competitive rendering quality compared to existing baselines, effectively reducing floaters and "waxy" surfaces.

Abstract: Fermentation quality is shaped not just by what microbes produce but by how they communicate. Cocoa pulp juice fermentation is governed by quorum sensing (QS) — bacterial autoinducers (AHLs, AI-2, peptides) and fungal mediators (farnesol, tyrosol) — that coordinate microbial succession and flavour precursor formation. Yet QS states are absent from operational fermentation control: conventional bioreactors act only on macroscopic variables (pH, temperature, dissolved oxygen) and cannot read the molecular language coordinating quality-determining transitions. This translational perspective proposes that a digital twin, implemented as a bioprocess decision suite, provides the missing intelligence layer. It is not a vessel replacement but a QS-aware reasoning system that translates communication states into explainable, audit-ready decisions and enables in silico sensory predesign before a run begins. A key design constraint is polyphenol-mediated AHL quenching and acid-accelerated AI-2 degradation in the cocoa matrix, which we formalise as an architectural specification. Time-series transformer models with attention-based explainability anchor the near-term AI layer, with graph neural networks, Bayesian uncertainty quantification, and reinforcement learning forming a progressive maturation roadmap. A three-tier framework scales deployment from artisanal to biomanufacturing operations.

Abstract: Background: Vitamin D optimizes musculoskeletal function and athletic performance, yet optimal supplementation protocols remain unclear. Methods: In this double-blind RCT, 18 professional female soccer players were randomized during autum preparatory period (August-September) to receive vitamin D₃ (4000 IU/day; n=9) or placebo (n=9) for 8 weeks. Outcomes included serum 25(OH)D/1,25(OH)₂D, hematology, RAST, 5/30-m sprints, and CMJ. Results: At baseline, after summer exposure, four players had 25(OH)D ≤ 30 ng/mL, and fifteen had levels between 30–50 ng/mL; none exceeded 50 ng/mL. After eight weeks of supplementation, no significant differences were observed between groups in 25(OH)D, and metabolites (Δ25(OH)D: EG +12.4±8.2 vs. PG +3.1±6.5 ng/mL; p=0.12), perfomance, or morphology. Training improved RAST (p=0.001) and 30-m sprint (p=0.005). Conclu-sions: Vitamin D₃ supplementation at 4000 IU/day for eight weeks did not significantly improve strength, speed, or CMJ performance in professional female soccer players. Persistently suboptimal vitamin D status suggests that higher doses may be required to improve anaerobic capacity. Further studies in this specific population are warranted, and higher supplementation doses, as observed in studies on male football players, may potentially lead to more pronounced improvements in physical performance tests.

Abstract: FLASH radiotherapy, characterized by ultra-high dose rates, has been shown to reduce normal tissue toxicity while preserving tumor control, yet its underlying mechanism remains unresolved. Existing models based on radiolytic oxygen depletion (ROD) successfully capture dose-rate dependence but fail to explain key experimental features, including threshold-like onset, saturation of the sparing effect, and sensitivity to temporal delivery structure. Here, we propose a mechanistic framework — Memory-modulated Radiolytic Oxygen Depletion (M-ROD) — that extends classical ROD by incorporating a bounded, history-dependent internal state. The dynamical structure of this state — cooperative activation, bounded feedback, and characteristic decay — is consistent with that of cooperative biological regulatory processes, including gene regulatory networks. In this framework, dose-rate–dependent stress activates a nonlinear biological state that evolves through induction, bounded feedback, and decay, modulating radiosensitivity alongside oxygen effects. We show that the framework reproduces the defining characteristics of FLASH, including sharp threshold-like transitions, plateau behavior, and strong dependence on pulse spacing, duty cycle, and irradiation sequence, while reducing to conventional radiobiology under low dose-rate conditions. The pulse-spacing sensitivity that distinguishes M-ROD from memoryless models requires the state to relax on a characteristic timescale τ_M of approximately 10–100 ms; we show that bioelectric membrane dynamics, treated as a passive RC system using parameter values from standard electrophysiology, naturally produce relaxation in this range without parameter tuning. The model predicts that the magnitude of the FLASH effect is governed by the extent of state activation rather than dose rate alone, providing a mechanistic explanation for variability across experiments. These results support the interpretation of FLASH as an emergent state transition in a dynamical biological system and offer experimentally testable predictions that distinguish it from memoryless models.

Abstract: In extremely shallow water environments, the limited water depth is comparable to the maximum bubble radius. The pulsation of an underwater explosion bubble is strongly constrained by both the free surface and the rigid seabed, exhibiting complex nonlinear coupling effects, which are of great significance for the safety assessment and protection design of nearshore engineering. To address this issue, an axisymmetric two-dimensional numerical model based on the Eulerian finite element method (EFEM) with operator splitting technique and the Volume of fluid (VOF) interface-capturing approach is established. Under the assumptions of inviscid and incompressible flow, a systematic numerical investigation is carried out to examine the effects of the water depth parameter λ, position parameter γ)and buoyancy parameter δ on the bubble dynamics and the evolution of free surface structures. The results show that the maximum bubble radius, pulsation period and jet characteristics are all significantly regulated by the above three parameters. Moreover, under multi-period bubble pulsation, different parameter conditions lead to diverse evolution characteristics of free surface structures including the water spike, wrinkles and water skirt. The findings reveal the governing mechanisms of key dimensionless parameters on the nonlinear bubble-multi-boundary coupling dynamics in extremely shallow water explosions, providing an important numerical basis and theoretical reference for the theoretical analysis and safety design of related shallow water explosion engineering problems.