Abstract: Cardiovascular diseases remain the leading cause of mortality worldwide despite significant advances in diagnostic and therapeutic technologies. A substantial body of evidence indicates that myocardial metabolic remodeling and bioenergetic impairment develop long before the onset of overt cardiovascular disease. Conventional electrocardiography, although widely accessible and inexpensive, is traditionally limited to identifying manifest electrical abnormalities and lacks sensitivity for detecting early metabolic stress. Recent advances in artificial intelligence, particularly deep learning models trained on raw electrocardiographic waveforms, have demonstrated the ability to extract latent physiological information embedded within cardiac electrical signals.This study proposes a comprehensive framework for AI-powered electrocardiography aimed at detecting hidden myocardial metabolic stress prior to clinically apparent cardiovascular disease. By integrating multimodal cardiometabolic biomarkers with high-dimensional ECG analysis, this approach seeks to identify early electrophysiological signatures of energetic dysfunction.

Abstract: Glucose and ascorbate transport and their opposite effects on the physiological processes, explain the pathophysiology of the Ebola virus. The virus impairs intracellularly the interferon (IFN) signalling. The present article will focus on the viral factors (VP24, VP35, VP40 proteins, nucleoprotein NP) that operate in the inner of the cell, subsequently to the viral entry. The haemorrhagic fever syndrome could be understood as a state of oxidative stress, driven by hyperglycaemia and the activation of NF-kB pathway and inflammatory cytokines. High glucose levels in plasma contributes to oxidative stress. It has also an inhibitory effect on Interferon (IFN) signalling. Conversely, ascorbate can counteract the IFN blocking exerted by the virus and interfere virus budding. A treatment strategy would focus on the administration of ascorbate and glutathione, glucose or insulin at convenience, in order to maintain constant and normal levels of glucose in plasma, to combat the oxidative and inflammatory stress.

Abstract: Excess sodium intake is a growing public health concern in Malaysia. Reformulation using potassium chloride (KCl) and monosodium glutamate (MSG) offers a potential strategy to reduce sodium while maintaining palatability, although consumer responses to these ingredients remain mixed. This study examined young adults’ preferences for sodium-reduced canned soup and evaluated how flavour, sodium information, price, and additive-related cues influence stated choice, alongside sensory evaluation of sodium-reduced formulations. A cross-sectional mixed-method study was conducted among 211 Malaysian young adults. Participants completed a choice-based conjoint (CBC) experiment comprising six hypothetical purchase tasks that varied across seven product attributes. Multinomial logit models estimated part worth utilities and attribute importance. Sensory evaluation was conducted in a controlled environment using the generalised Labelled Magnitude Scale (gLMS) and Labelled Affective Magnitude (LAM) scale to assess saltiness intensity and pleasantness across soup formulations. Sodium-related attributes accounted for approximately 36% of stated decision weight, with sodium reduction percentage and flavour emerging as the strongest drivers of stated choice. Moderately sodium-reduced formulations incorporating KCl and MSG achieved favourable sensory ratings. Young adults’ acceptance of sodium-reduced soup is shaped primarily by flavour, sodium cues, and affordability. Sensory findings support the feasibility of sodium reduction using KCl and MSG without compromising palatability.

Abstract: Background. Small and medium enterprises (SMEs) dominate East African commerce yet incur persistent time, cost, and liquidity frictions when settling cross-border transactions. Digital payment technologies promise to ease these constraints, but rigorous evidence connecting adoption to measurable trade-process efficiency is sparse. Purpose. This study evaluates whether, and under what institutional conditions, SME digital-payment adoption improves cross-border trade efficiency in Kenya, Uganda, Rwanda, and Tanzania. Methods. A harmonised dataset pools 2023–2025 World Bank Enterprise Surveys microdata with country-level indicators of payment-system interoperability and Trade Facilitation Agreement (TFA) progress. Adoption is captured through the shares of sales and purchases conducted electronically, while efficiency is proxied by customs-clearance days, compliance costs, and perceived predictability. Ordinary least squares models with survey-design inference estimate adoption–efficiency associations; interaction terms test moderation by interoperability and regulatory alignment. Results. Controlling for firm and country heterogeneity, electronic-payment adoption is associated with 1.5 fewer customs-clearance days (p = 0.019), a 2.1-percentage-point fall in compliance costs (p = 0.006), and a significant decline in perceived unpredictability (p = 0.009). Marginal-effects analysis shows that these gains intensify where remittance-corridor costs are low and TFA implementation exceeds 70 percent, underscoring the complementary roles of systemic interoperability and regulatory alignment. Conclusions. Digital-payment adoption yields tangible efficiency dividends for trading SMEs, but only when embedded in supportive payment and trade-governance ecosystems. Policymakers should therefore pair interoperability and regulatory reforms with targeted SME onboarding to translate Africa’s extensive mobile-money infrastructure into sustained trade competitiveness.

Abstract: Background: Intestinal acute radiation syndrome (IARS) represents a life-threatening component of acute radiation syndrome with limited effective countermeasures. Under-standing molecular determinants governing intestinal epithelial resilience to ionizing ra-diation is critical for developing radiation toxicity mitigation strategies. Objectives: This study investigates the role of PIKfyve, a phosphoinositide kinase essential for endolysosomal homeostasis, in modulating radiation-induced intestinal toxicity. Methods: We utilized an inducible intestinal epithelial-specific PIKfyve-knockout mouse model (PIKfyve cKO) subjected to 10 Gy abdominal irradiation. Intestinal toxicity was as-sessed through histopathology, barrier permeability (FD4 assay), apoptosis markers, and transcriptomic profiling. Small intestinal organoids were employed for mechanistic vali-dation. Results: PIKfyve deletion alone did not perturb normal gut architecture but precipitated severe post-irradiation toxicity, including villous atrophy, crypt hypoplasia, and massive crypt-cell apoptosis. Barrier dysfunction was evidenced by elevated serum FD4 and heightened systemic pro-inflammatory cytokines, culminating in markedly increased mortality. Transcriptomic analysis revealed potentiated DNA-damage signaling and am-plified inflammatory cascades in PIKfyve-deficient intestines. Conclusions: These findings identify PIKfyve as a critical guardian of intestinal epithelial integrity against radiation toxicity. Given emerging PIKfyve inhibitors in cancer therapy, our results raise important safety considerations for clinical radiotherapy and position PIKfyve as a potential target for radiation toxicity mitigation.

Abstract: Background: Response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is heterogeneous and early identification of non-responders may help optimize treatment strategies and reduce unnecessary toxicity. This study aimed to develop and internally validate a machine learning model based on radiomic features extracted from baseline magnetic resonance imaging (MRI) to predict treatment response assessed at restaging MRI. Methods: In this retrospective single-center study 86 patients with histologically confirmed LARC who underwent baseline and restaging MRI, neoadjuvant therapy, and surgery, were included. Primary tumors were manually segmented on oblique axial T2-weighted images. A total of 107 radiomic features were extracted using PyRadiomics, with and without N4 bias field correction. Feature selection was performed using LASSO, followed by elasticnet–regularized logistic regression. Model performance was assessed using repeated stratified 5-fold cross-validation. Response was defined according to MRI tumor regression grade (mrTRG) at restaging, dichotomized into responders (mrTRG ≤ 2) and non-responders (mrTRG ≥ 3). Results: The model achieved a mean area under the receiver operating characteristic curve (AUC-ROC) of 0.73, accuracy of 72.5%, sensitivity of 79.2%, and specificity of 50%. Conclusions: Baseline MRI-based radiomics demonstrated to potentially identify patients at higher risk of non-response to neoadjuvant therapy in LARC.

Abstract: Post-endometrial ablation persistent uterine bleeding indicates that no method of endometrial ablation (EA) eliminates the entire endometrium and hysteroscopy shows distorted and scarred uterine cavity in the majority of women. These observations raise concerns regarding presentation, assessment and stage of potential post-ablation endometrial cancer (PAEC) developing in residual endometrium. To address these concerns, we conducted a systematic search for reports of endometrial cancer (EC) associated with or after EA using multiple data bases imputing keywords of EC after EA and possible combinations of first- and second-generation EA techniques associated with EC from its inception in the 1980s through 2025. After excluding irrelevant publications, we identified 86 ECs associated with EA described in 20 case reports (N=20), four case series (N=18), eleven cohort studies (N=21), one registry (N=27) and five reviews. Based on 12 relevant studies, at follow up of 1.9-25 years, 43 ECs were identified in 39,795 women with a history of EA; summary incidence of 0.11% (range 0.0 - 1.59%). Based on the remaining 43 evaluable cases of PAEC, the mode and time to presentation, investigation, diagnosis, and stage of PAEC were not altered by EA. We conclude that EA has a protective effect reducing the risk EC significantly, likely due to quantitative reduction in endometrium that can potentially become malignant and the EA process eliminating occult pre- or malignant endometrial tissues which are vulnerable to ablation techniques. The mode and time to presentation, the diagnostic work-up, including endometrial biopsy and hysteroscopy, and stage of PAEC are not altered by EA.

Abstract: The use of Traditional Chinese Medicine (TCM) is expanding worldwide. In the UK, TCM has developed rapidly since the 1990s, but limited scientific evidence supports its safety, quality, or efficacy. This creates challenges for regulatory governance and public health protection.Objective:To review the development of TCM regulations in the UK and examine how existing regulations address safety, quality, and efficacy through different regulatory instruments, objectives, targets, and enforcement mechanisms. A narrative literature review was conducted, which is supplemented by grey literature searches of government reports and legislative documents published between 1970 and 2020. Thematic and chronological analyses were applied to map regulatory transitions and classify instruments and objectives. Ten key regulations and policy documents were identified, forming a hierarchical and fragmented framework dominated by product-focused oversight. While the system ensures basic safety and quality standards, it lacks consistent mechanisms for enforcement, practitioner regulation, and efficacy assessment. UK-TCM regulation has evolved through a mix of EU and domestic legislation, but gaps in enforcement and practitioner oversight persist. Policymakers should develop proportionate efficacy evaluation methods, enhance enforcement, and establish clearer practitioner standards to ensure safe, evidence-informed practice in post-Brexit UK health policy.

Abstract: Contemporary enterprise IT operations are implemented largely atop Shannon–Turing computing: programs execute read–compute–write cycles over data structures, while governance (fault handling, configuration control, auditability, and continuity) is applied externally through infrastructure platforms, observability stacks, and human processes. This separation scales analytic throughput but accumulates coherence debt: locally expedient commitments whose provenance and revisability degrade until exposed by shocks (failures, security incidents, regulatory demands, or architectural transitions). We synthesize a model evolution that integrates computation with regulation at two distinct levels: (i) Distributed Intelligent Managed Elements (DIME), which modifies the Turing cycle to read–check-with-oracle–compute–write by infusing a signaling overlay and FCAPS (Fault, Configuration, Accountability, Performance, Security) supervision into computation in progress; and (ii) AMOS, which fully decouples the process executor from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state in a governed Knowledge Network. We further present implementation evidence via a microservice transaction testbed that operationalizes dynamic topology as data, a capability-oriented control plane, decoupled application-layer FCAPS from IaaS/PaaS FCAPS, and policy-selectable consistency/availability semantics. We argue that the principal benefit of AMOS is not “circumventing” impossibility results such as CAP, but governing their trade-offs as explicit commitments with auditable lineage and controlled convergence back to coherent state.

Abstract: Machine learning's dominant paradigm—whether model-centric or data-centric—treats intelligence as the extraction of statistical patterns from behavioral records. This approach has delivered remarkable engineering feats. Yet something foundational is missing. Data is not reality: it is a finite record of trajectories through reality. A photograph of a river is not the river's law. This paper argues that the data paradigm conflates measurement with mechanism, capturing where systems have been rather than why they go there. We propose an alternative grounded in the Architecture of Freedom Intelligence (AFI), which identifies navigability—the structural availability of paths—as the primary organizing principle of all complex systems. The Law of Freedom, F = P/D, states that navigational capacity equals differentiation capacity (Perception, P) divided by structural resistance (Distortion, D). Under this framework, intelligence is not pattern memorization but distortion navigation: all systems move according to dx/dt = −P(x)·∇D(x), following gradients of resistance scaled by perceptual capacity. We demonstrate that this gradient law is structurally identical to Fick's diffusion, Berg–Brown chemotaxis, Ohm's law, and gradient descent—revealing a deep structural unity that the data paradigm treats as coincidental analogy. Nature does not train on labeled datasets: ants, neurons, immune cells, and ecological populations navigate through calibrated heuristics on Perception and Distortion fields, not through backpropagation over historical trajectories. This observation motivates a fundamental reconceptualization of what training should accomplish. We propose Freedom Intelligence Training (FIT): a learning paradigm oriented toward learning P and D fields directly, rather than fitting statistical correlations over behavioral snapshots. FIT rests on five predictions: (i) models trained on P–D fields require exponentially less data than pattern-extraction models; (ii) generalization improves because P–D fields encode causal structure; (iii) out-of-distribution performance improves because navigability laws transfer across domains; (iv) interpretability is natural since every prediction decomposes into ΔP and ΔD contributions; (v) the exploration–exploitation transition is quantifiable as the coefficient of variation of the Freedom field crossing 1.0. We provide ten falsification criteria and position FIT within the emerging landscape of world models, physics-informed learning, and causal inference. This is a theoretical proposal; a complete experimental roadmap is provided.

Abstract: Current implementation of pneumonia diagnosis remains challenging to achieve better performance and improve to get better result. Convolutional neural networks (CNNs) have demonstrated the successful automation of pneumonia diagnosis through the analysis of chest X-ray images, which can be combined with other methods to improve prediction and classification accuracy rates. The aim of this research is to propose an innovative framework for pediatric pneumonia diagnosis that unites three fine-tuned pre-trained CNN models through feature fusion at the EfficientNetB0, RestNet50, and MobileNetV2 to achieve better performance. The mixed-model architecture framework provides an ideal solution for time-sensitive clinical applications operating in resource-constrained environments. The proposed framework model demonstrates successful performance in maintaining excellent sensitivity and specificity measures because clinical use requires minimal false-negative and false-positive results. Furthermore, the proposed framework model outperformed individual models and compared favorably to previous studies related to pneumonia classification, achieving an accuracy level of 96.14%, a precision of 94.10%, a recall of 96.92%, and an F1-score of 94.97%.

Abstract: Forest fires in the Amazon rainforest pose a critical environmental challenge, with impacts on biodiversity, atmospheric composition, and climate regulation. Fire activity has intensified in recent decades due to climate variability and increasing anthropogenic pressure, raising concerns about a potential shift of the Amazon from a carbon sink to a carbon source. This study analyzes the spatial and temporal variability of fire activity across the Amazon basin, with a focus on the Brazilian region, over the period 2001–2022. The analysis is based on satellite-derived active fire data from NASA’s Fire Information for Resource Management System (FIRMS), obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). Fire Radiative Power (FRP) is used as a proxy for fire intensity and combustion processes. The observed increase in fire activity post-2012 is primarily attributed to the deployment of the VIIRS sensor, which offers superior sensitivity for detecting small-scale and low-intensity fires Pronounced peaks of fire activity are observed in 2004, 2005, 2007, and after 2019. Statistical analyses reveal strong interannual variability and cyclical behavior in FRP, linked to variations in drought conditions, precipitation, land-use change, and environmental policy. Overall, the study highlights the value of multi-sensor satellite observations for long-term fire monitoring on the Amazon.

Abstract: In volatile environments, work teams operate as complex adaptive systems that reconfigure internal processes in response to internal and external tensions. Team adaptability—a systemic outcome—is influenced by paradoxical leadership (PL), but the motivational pathways translating PL into adaptive behavior remain underexplored. Grounded in Conservation of Resources theory, this multi‑wave, supervisor–subordinate dyadic study of 114 high‑tech teams adopts a systems perspective and treats goal orientations as collective resource‑allocation rules. PL most strongly fosters systemic adaptability by cultivating a team performance‑approach orientation—an agentic, short‑term resource‑mobilization strategy that drives visible competence demonstration. Although team learning orientation predicts adaptability when tested alone, its mediating effect is suppressed once performance‑approach orientation is included, consistent with competitive resource‑allocation dynamics in specialist teams. PL also reduces performance‑avoidance orientation, but this reduction does not yield a significant indirect effect on adaptability, indicating that removing dysfunction is not equivalent to activating adaptive capacity. By comparing three competing motivational pathways, the study identifies a dominant leadership leverage point for configuring resource flows to produce emergent adaptation and offers implications for designing systemic interventions and models to enhance team resilience.

Abstract: Mixed-halide perovskite solar cells with the composition Cs0.1(MA0.17FA0.83)0.9Pb(I0.83Br0.17)3 were fabricated obtaining solar cells as glass/ITO/SnO2/triple cation perovskite/HTL/Au, subsequently used as photoanodes for efficient solar-driven water splitting by applying commercial catalytic nickel foils onto the Au back-contact pads of devices. To enable operation under alkaline media the de-vices were encapsulated using commercial PET–EVA multilayer films, providing a ro-bust barrier while leaving the Ni foils exposed as the electrochemically active interface. Two operating configurations were investigated and compared: (i) an outside configu-ration, where the perovskite solar cell powered an external electrochemical cell, and (ii) an immersed configuration, in which the encapsulated device was directly integrated into the electrolyte. In particular, the oxygen evolution reaction onset shifted from ~1.32 V vs RHE, when the Ni electrode was not powered by the perovskite absorber, to ~0.34 V vs RHE when the perovskite device powered the nickel foil for both immersed and outside configurations. The IS device achieved a maximum Applied Bias Photon-to-Current Ef-ficiency of ~20% under AM 1.5G illumination (100 mW cm⁻²), among the highest reported for perovskite-based photoanodes.

Abstract: Background/Objectives: Staphylococcus aureus is one of the leading causes of bacterial infection–related mortality worldwide, with outcomes complicated by antimicrobial resistance; asymptomatic colonization (~30% of the population) increases the risk of subsequent infection, often with the colonizing strain. While high-income countries provide surveillance data, comprehensive data from LMICs are lacking, and the COVID-19 pandemic has significantly influenced the incidence and epidemiology of S. aureus infections, highlighting a critical data gap in Slovakia. Methods: We conducted data analysis using the KNIME Analytics Platform, an open-source, visual workflow environment that facilitates integration, preprocessing, and advanced analysis of complex biomedical datasets. This study analyzed 5 years of data from routine laboratory diagnostics extracted from the laboratory information system (LIS). Results: Our data reveals that the incidence of multidrug-resistant S. aureus, including MRSA, increased during 2020–2022—particularly in surgical departments—and remained elevated into the post-pandemic period, while MSSA incidence was consistently higher overall and predominantly driven by colonization rather than infection. Conclusions: This study provides essential insights into the use of big data analytics platforms. Identified missing gaps, such as information about the difference between colonization vs. infection, and their implementation in the future, together with whole genome sequencing, set a foundation for epidemiological research purposes in Slovakia.

Abstract: The Dzhanibekov effect—also known as the tennis racket theorem in its classical formulation—remains one of the most visually striking and widely circulated demonstrations of rotational instability in torque-free motion. A rigid body spinning about its intermediate principal axis undergoes abrupt, repeated flips that appear paradoxical to non-specialists and counterintuitive even to many trained physicists when encountered as a real-world phenomenon rather than as a textbook theorem. Conventional mechanics accounts for this behavior through the instability of the intermediate axis in Euler’s equations; however, this explanation is typically framed as a binary statement (“stable” versus “unstable”) and rarely develops a deeper dynamical interpretation of three experimentally salient features: (i) the emergence of highly organized, quasi-periodic flips rather than unstructured chaos; (ii) the strong dependence of the observed flip dynamics on preparation, perturbations, and real-world imperfections; and (iii) the apparent “memory” of the system, which repeatedly returns to similar macroscopic configurations despite inevitable dissipation and microstructural coupling.In this paper, we propose a rigorous reinterpretation of the Dzhanibekov effect within the framework of Viscous Time Theory (VTT), viewing the flip not merely as a consequence of intermediate-axis instability, but as a coherence-regime transition in an anisotropic informational geometry. We introduce an informational manifold for rigid-body rotation, in which rotational states evolve along constrained trajectories shaped by anisotropic reconfiguration costs associated with the principal inertia structure and by an effective informational viscosity arising from internal mode coupling and finite-time redistribution. In this picture, the flip is not an instantaneous kinematic accident but a finite-time transition between metastable corridors of rotational coherence, with the observed quasi-periodicity emerging as a geometric consequence of navigation along preferred informational pathways.This formulation yields a set of quantitative, testable predictions absent from the standard narrative, including hysteresis under cyclic control of initial conditions, direction-dependent flip thresholds and transition times, metastable latency regimes, and scaling relations linking flip timing to an informational viscosity parameter. To assess these predictions, we perform a comprehensive numerical validation combining high-resolution integration of the Euler equations, ensemble statistics over large sets of perturbations, spectral and structural diagnostics, multi-precision convergence testing, and comparative model analysis.Quantitative analysis demonstrates that informational viscosity produces a bounded suppression of instability growth (approximately 2–15%) without altering phase-space topology, integrability, or scaling structure. No chaotic attractors, bifurcation cascades, or nonphysical divergences emerge. The Dzhanibekov instability remains fundamentally geometric, with VTT operating as a coherent rate-level regulator rather than a replacement mechanism. This bounded rate modification becomes logarithmically amplified in flip-time statistics, providing a clear and measurable experimental signature.By reframing one of the most iconic “video-paradox” phenomena of classical mechanics as an informational hysteresis and regime-transition process, this work provides a mathematically grounded bridge between visible macroscopic dynamics and a general theory of anisotropic, viscous informational evolution. The Dzhanibekov effect thus emerges not only as a pedagogical curiosity, but as a quantitative macroscopic laboratory for probing informational geometry, coherence regimes, and finite-time reconfiguration in real physical systems.

Abstract: The rapid advancement of artificial intelligence (AI) presents unprecedented challenges for labor market forecasting, requiring fundamental methodological innovations that move beyond traditional extrapolation techniques. This policy paper proposes comprehensive enhancements to the U.S. Bureau of Labor Statistics (BLS) employment projection systems to better capture and forecast AI's impact on employment structures, job roles, and workforce skill requirements. Drawing on recent empirical research and the bureau's existing methodological frameworks, we present an integrated architectural framework that combines task-based exposure modeling, real-time data analytics, causal inference methods, and enhanced gross flows estimation. Our recommendations address critical gaps in current BLS methodologies identified through systematic literature review and analysis of emerging AI adoption patterns, including the distinction between automation and augmentation effects, the nonlinear dynamics of AI adoption, and differential impacts across worker demographics. We propose a dynamic Occupational AI Exposure Score (OAIES) framework that leverages large language models and occupational task data, alongside enhanced data collection strategies and modernized estimation techniques. The architectural framework, illustrated through five interconnected diagrams, demonstrates how these methodological innovations integrate into a coherent system for measuring labor market transformation. These enhancements would enable more accurate projections of job displacement, skill evolution, and employment transformation across industries and geographic regions, supporting evidence-based policymaking for workforce development in an AI-driven economy. The paper concludes with a phased implementation strategy and validation protocol to ensure methodological rigor and operational feasibility.

Abstract: This study develops a hybrid analytical framework that bridges data-driven K2 structural learning with expert-informed Bayesian Networks to decrypt the intricate interdependencies among policy instruments, resource endowments, and socio-economic variables across China’s hydropower, wind, and solar power. The results demonstrate a fundamental paradigm shift from resource-bound growth to institutional-steered expansion, notably in the solar sector where the Renewable Portfolio Standard (RPS) has superseded natural radiation as the primary determinant for capacity scaling. Forward sensitivity analysis and backward diagnostic attribution reveal that achieving high-growth milestones requires a synergistic convergence of tech-cost reductions and mandatory consumption quotas, whereas the absence of RPS leads to a catastrophic 64% degradation in systemic causal connectivity. These findings underscore the necessity of transitioning from price-side stimuli to structural consumption-side mandates to ensure a resilient and certain energy transition under stringent carbon constraints.

Abstract: We report a direct empirical discovery from the analysis of 149 galaxies in the SPARC database. Through a purely data-driven computational approach, we find that galactic rotation curves naturally organize themselves into four statistically distinct dynamical families, with hierarchical substructure revealing seven finer-grained families. Two families exhibit exceptional regularity, with 100% success in basic kinematic modeling. This classification emerges objectively from the data structure itself, without any theoretical assumptions about dark matter or galaxy formation. Extensive validation including PCA analysis (shape parameters dominate over scale), cross-validation (85.2% agreement), bootstrap uncertainty (mean probability 0.654), and comparison with previous morphological classifications shows only 16.7% agreement, confirming that this is a fundamentally new classification scheme based purely on kinematics. Physical properties reveal systematic differences across families: Family 3 (Rising) has the highest mass (log M = 9.75), largest radius (14.5 kpc), and highest baryonic fraction (31.2), while Family 0 (Flat) has the lowest mass and smallest radius. We present these families as a new phenomenological framework for understanding galactic dynamics, independent of morphological considerations.

Abstract: Balancing accuracy and accessibility in solar energy flux estimation models remains a key challenge in atmospheric radiative transfer research. Since spectral models require computationally intensive spectral calculations, a widely adopted simplification strategy is to parameterize atmospheric spectral transmittances using various wavelength-averaging formulations. This work introduces a broadband parametric model derived from a spectral model that accurately estimates the three components of solar irradiance, direct normal, diffuse, and global under clear-sky conditions. The procedure used to develop the model is structured in two stages. Initially, discrete broadband transmittances are obtained by applying an independent integration scheme to the spectral transmittances provided by the source spectral model. The second stage involves fitting these results to obtain continuous broadband atmospheric transmittances, expressed as analytical functions depending solely on atmospheric state parameters and remaining independent of wavelength. The model development procedure is relatively classical; however, the calculation of the diffuse component introduces a new approach for estimating the fraction of aerosol scattering directedtoward the ground. The model was tested against data collected fromeight radiometric stations distributed across six continents andbenchmarked against two well-established reference models. Overall, theresults indicate a high level of accuracy and demonstrate the practical applicability of the model.