Abstract: Background/Objectives: Software has become core infrastructure in biomedical science; however, tools and workflows remain fragmented across subfields, limiting reproducibility and slowing translation from data generation to actionable decisions. This narrative review synthesizes representative software ecosystems across three major pillars—bioinformatics, molecular modeling and simulations, and epidemiology/public health—and evaluates their translational readiness using a shared cross-domain framework focused on reproducibility, validation, interoperability, usability, and decision relevance. Methods: A narrative review of articles indexed in PubMed/NCBI, Web of Science, and Scopus between 2000 and 2025 was conducted. Domain-specific terms related to bioinformatics, molecular modeling, docking, molecular dynamics, epidemiology, public health, and workflow management were combined with software- and algorithm-focused keywords. Studies describing, validating, or applying documented tools with biomedical relevance were included. Results: Across domains, mature data standards and reference resources (e.g., FASTQ, BAM/CRAM, VCF, mzML), widely adopted platforms (e.g., BLAST/BLAST+, Bioconductor, AutoDock Vina, GROMACS, Epi Info, QGIS), and increasing use of workflow engines were identified. Software pipelines routinely transform molecular and surveillance data into interpretable features supporting hypothesis generation. Conclusions: Integrated, standards-based, and validated software pipelines can shorten the path from measurement to decision in biomedicine and public health. Future progress depends on reproducibility practices, benchmarking, user-centered design, portable implementations, and responsible deployment of machine learning.

Abstract: The rapid integration of Artificial Intelligence (AI) into education has created both transformative opportunities and complex challenges for teaching and learning. This study provides a comprehensive systematic narrative review of the existing literature on AI in educational contexts, focusing on its potential to enhance personalized learning, support instructional efficiency, and facilitate data-driven decision-making. AI-driven tools, including adaptive learning platforms, intelligent tutoring systems, and automated assessment technologies, were found to improve student engagement, academic outcomes, and collaborative learning when implemented thoughtfully. However, the study also highlights persistent challenges such as limited teacher preparedness, infrastructure constraints, and inequitable access to technology, which may hinder effective AI adoption. Ethical considerations—including data privacy, algorithmic transparency, cultural alignment, and academic integrity—further underscore the need for responsible and human-centered integration of AI in schools. Findings suggest that AI’s educational value depends not only on technological sophistication but also on its alignment with pedagogical objectives, ethical principles, and institutional readiness. To maximize benefits, the study recommends investments in professional development, infrastructure, equitable access, and clear ethical guidelines, alongside strategies that balance AI use with traditional teaching approaches. Overall, this research emphasizes that AI should complement, rather than replace, human educators, ensuring that technological innovation enhances learning while safeguarding student rights and fostering critical thinking.

Abstract: Tourism Micro, Small, and Medium Enterprises (MSMEs) in underdeveloped regions play a crucial role in driving local economic development and sustaining the tourism ecosystem. Yet, they face limitations in innovation capacity and organizational performance. This study aims to develop and test a green innovation model to improve MSME organizational performance and strengthen the tourism ecosystem in East Sumba Regency, Indonesia. This study employed a quantitative approach, collecting data through questionnaires from tourism MSMEs, which were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that green innovation, represented by product value, technology, networking, marketing, and market demand, has a positive and significant impact on organizational performance, which, in turn, acts as a key mediator in improving ecosystem performance, as reflected in productivity and resilience. These findings confirm that the impact of green innovation on the tourism ecosystem is indirect and dependent on strengthening the operational and financial performance of MSMEs. The novelty of this study lies in integrating the empirical PLS-SEM model with an implementation approach, including the development of training modules and the digitalization of learning, in the context of 3T regions (Frontier, Outermost, and Underdeveloped). Limitations in this study use data from a single time period; further research is recommended to use multi-period data to capture the dynamics of change better.

Abstract: Background: The rapid adoption of generative artificial intelligence (AI) tools in higher education has prompted growing interest in students’ digital and AI literacy, ethical awareness, and perceptions of institutional readiness. Recent reviews of the evidence indicate that while student use of AI tools is increasing, levels of understanding, confidence, and access to guidance remain uneven across higher education contexts (Dos, 2025; Zhai et al., 2024). Methods: A cross-sectional quantitative survey was conducted among higher education students (N = 85) using an anonymous online questionnaire. The instrument assessed students’ self-reported AI literacy and self-efficacy, frequency of AI tool use, and perceived readiness of students and institutions to use AI in higher education. Descriptive statistics and internal consistency analyses were performed. Results: Students reported moderate overall AI literacy and self-efficacy (M = 3.55 on a 5-point scale), with strong internal consistency across items (Cronbach’s α = .84; McDonald’s ω = .88). Confidence in judging appropriate versus inappropriate AI use was higher than confidence in accessing support or improving AI outputs through prompting. AI tool use was widespread but heterogeneous, with 55.3% of respondents reporting daily or weekly use. A substantial proportion of students selected “Cannot decide / No experience yet” (30.6% for the readiness comparison item) when evaluating institutional readiness, indicating notable uncertainty regarding institutional AI preparedness. Conclusions: The findings suggest that student engagement with AI in higher education is characterised by moderate confidence, uneven practical support, and limited clarity regarding institutional readiness, consistent with prior research (Dodds et al., 2024; Dos, 2025; Zhai et al., 2024). The results highlight the importance of transparent communication, accessible guidance, and inclusive AI literacy development to support responsible AI use from the student perspective.

Abstract: Quantum Encryption in Phase Space (QEPS) is a physical-layer encryption framework that harnesses the quantum-mechanical properties of coherent states to secure optical communications against both classical and quantum computational threats. By applying randomized phase shifts, displacements, or their dynamic combinations—implemented as unitary transformations in phase space—QEPS disrupts the phase reference essential for coherent detection, establishing a phase synchronization barrier. This review synthesizes the theoretical foundations, security mechanisms, and experimental progress of the QEPS framework, encompassing its three principal variants: the round-trip Quantum Public Key Envelope (QPKE) protocol—a public-key-like scheme built upon phase randomization (QEPS-p), the symmetric phase-only QEPS-p, and the displacement-based QEPS-d. Experimental validations demonstrate that authorized users achieve bit-error rates (BER) below the forward-error-correction threshold, whereas eavesdroppers are confined to BER near 50%, equivalent to random guessing—all while utilizing standard coherent optical transceivers at data rates up to 200 Gb/s over 80 km of fiber. We further examine QEPS’s robustness to channel impairments, its seamless compatibility with existing digital signal processing (DSP) pipelines, and its distinctive position within the post-quantum cryptography landscape. Finally, we outline key challenges and future research directions toward deploying QEPS as a practical, quantum-resistant security layer for next-generation optical networks.

Abstract: Background/Objectives: Approximately 53.4 million U.S. adults aged 50 or older have low bone mass, yet male bone loss remains under-researched. This study evaluated the effects of one year of prune supplementation on bone health in osteopenic men. Methods: Fifty-nine men (55–80 years) were randomly assigned to one of three groups: 0 g prune (control), 50 g prune, or 100 g prune daily, along with 450 mg elemental calcium and 800 IU vitamin D₃. Dual-energy X-ray absorptiometry (DXA) scans and blood samples were collected at baseline, 3, 6, and 12 months. Results: No significant changes were observed in total bone mineral density (BMD) over one year. C-reactive protein (CRP) was significantly higher in the control group compared to the 50 g group at all time points. Osteo-protegerin (OPG) decreased significantly in all groups, however, the decrease was significantly greater in the control compared to both prune groups. Sclerostin (SOST) significantly increased over time in all groups, albeit to a greater degree in the control group. Tartrate-resistant acid phosphatase-5b (TRAP5b) increased in the 50 g and control group but remained stable in the 100 g group. Conclusions: Overall, prune supplementation did not improve BMD beyond that of the calcium and vitamin D₃ supplementation however, there was a modest effect on biomarkers of inflammation and bone metabolism.

Abstract: The conservation status of the Colombian Caribbean dune system was assessed considering the influence of natural and anthropogenic factors. The study took place in five locations with a gradient of human disturbance. In each site, twenty-seven plots were established along three transects perpendicular to the coast. Environmental variables such as dune height, slope, sediment physical-chemical attributes, and anthropogenic impact were assessed in each site, while species composition, frequency, and plant cover were determined for each plot. The results show a correlation between natural and anthropogenic factors and the composition and structure of plant communities growing on the beach and coastal dunes. Human disturbances (urbanized areas, construction, burning, debris, trampling, logging, tourism, groins, sewage, roads, garbage, and sediment extraction) were particularly relevant. Plant cover and species diversity were highest in the sites with the lowest human impact, while the contrary occurred in sites where human impact was highest. Furthermore, community structure varied among sites: trees and vines were more frequent in the preserved locations, while shrubs and parasitic plants were more abundant in the disturbed sites. Management alternatives should consider the environmental factors (natural and anthropogenic) affecting vegetation to improve the conservation of plant diversity on coastal dunes along the Colombian Caribbean coast.

Abstract: Objective: Following the publication from Haylen BT et al¹ describing the MUSPACC (midline uterosacral plication anterior colporrhaphy combo) procedure, we changed our clinical practice and started performing MUSPACC in patients with cystocele and vaginal vault prolapse (VVS). The aims of this study were to evaluate peri- and postoperative complications, vaginal and urinary symptoms including patient satisfaction 3 months postoperatively. Materials and Methods: A retrospective study of 58 women, who underwent MUSPACC during a 5-year period. Patients completed three prolapse questions from the International Consultation on Incontinence-Vaginal Symptoms (ICIQ-VS) and the International Con-sultation on Incontinence Questionnaire- Urinary Incontinence Short Form preoperatively and 3 months postoperatively. Records were reviewed for demography, peri- and postop-erative complications. Results: At follow-up, patients had a significant improvement in vaginal symptoms with a preoperative mean ICIQ-VS score of 15.2 declining to 1.16. A total of 42,1% of patients with preoperative urinary incontinence (UI) became completely dry after MUSPACC pro-cedures alone. Three patients (15%) experienced de novo UI. Perioperative complications occurred in one patient. Postoperative complication rate was 20,7% (12/58) including one patient, who experienced a postoperative fistula between right ureter and vagina. No patients required further operation. Totally 96,4% of patients were satisfied postoperatively. Conclusion: MUSPACC procedure is a good surgical option for treatment of VVP and cys-tocele with a positive impact on vaginal and urinary symptoms, high patient satisfaction and with a low rate of serious complications. Cystoscopy should be performed periopera-tively for immediate detection and management of possible lesions of the urinary tract.

Abstract: The article analyzes the role of selected digital services in maintaining the functioning of the Ukrainian state and economy in wartime conditions and assesses their adaptive po-tential for European Union countries in the context of sustainable and resilient develop-ment. The starting point was the concept of institutional and economic resilience, which has grown in importance in the context of global crises, contributing directly to the goals of sustainable development and long-term socio-economic stability. A qualitative and quantitative approach was used, including secondary data analysis, comparative analy-sis, and triangulation of sources. The results of the study showed that platforms such as Diia, Diia.Business, eRobota, and Air Alert played a key role in ensuring the continuity of public services, support for businesses, and crisis communication. The integration of these tools into the public management system enabled Ukraine to maintain basic state func-tions despite the ongoing armed conflict. A comparative analysis with Estonia, Poland, and Denmark confirmed that the Ukrainian model can be a source of inspiration for EU digitalization strategies, particularly in the areas of mobile administrative services and integration with crisis management systems. The article's conclusions emphasize the importance of digitization as a strategic resource that strengthens the resilience and sus-tainability of public institutions and economies in emergency situations.

Abstract:

This study aimed to develop a comprehensive typology of Sudanese sorghum-farming households within their food security status to inform targeted agricultural policy and rural development strategies. Using survey data from 392 households across 11 Sudanese states, the research captures the structural, socio-economic, and geographical diversity of farming systems and scrutinizes the relationship between socioeconomic characteristics of farmer households and related probability of constituting a specific farmer type. To assert this, Principal Component Analysis (PCA), hierarchical clustering, and Multinomial logistic regression analysis were applied. Through PCA and hierarchical clustering, three types of farmers were identified: The first type (Vulnerable Farmers), characterized by low education levels, small landholdings, high food insecurity, and reliance on subsistence farming; The second type (Well-off Remote farmers), operating larger landholdings meant for commercial purposes, yet facing challenges related to geographic isolation and limited market access; The third type (Educated Farmers with access to urban areas), consisting of households with higher education, diversified income sources, and proximity to markets, though still experiencing persistent food insecurity. Multinomial logistic regression analysis confirmed that household size, age, education, land size, market distance, and income structure are significant predictors of respective types of farmers. Thus, the study stands as a tool to enlighten intended/future policies, in providing input support and credit for vulnerable farmers, infrastructure and market access for remote commercial farmers, and land tenure security with innovative-geared incentives for farmers interacting with urban areas to foster inclusive, adaptive agricultural policies, and sustainable development across Sudan’s diverse farming communities.

Abstract: WRKY transcription factors (TFs) are pivotal in plant stress responses, yet their roles in Camellia oleifera, an economically important oil crop, remain poorly understood. We identified 192 WRKY genes in the tetraploid C. oleifera genome, and classified them into three groups (I, II and III) based on conserved domains. Chromosomal distribution revealed uneven localization of the WRKY genes, with the highest density (25 WRKY genes) on the Chromosome 10. RNA-seq analysis on anthracnose-resistant (CL150) and susceptible (CL102) cultivars inoculated with Colletotrichum gloeosporioides identified 1,822 differentially expressed genes (DEGs) and 109 DEGs dependent to CL150, including 11 core DEGs shared between the cultivars. Notably, one WRKY gene (YC.08G0001620-1A, Type I) exhibited significant upregulation in CL150, suggesting its role in disease resistance. Functional enrichment linked the DEGs to oxidative stress and metabolic pathways. This study provides a comprehensive WRKY family analysis in C. oleifera and highlights YC.08G0001620-1A as a promising candidate for molecular breeding to enhance anthracnose resistance in this economically important oil crop.

Abstract: Lung cancer is one of the most commonly diagnosed cancers worldwide and remains the leading cause of cancer-related death in both men and women. In 2022, approximately 2.5 million new cases of lung cancer and 1.8 million deaths due to the disease were estimated. Historically, lung cancer has been more frequent in men, although the difference between sexes has been decreasing, with tobacco use remaining the main etiological factor. Survival rates vary considerably depending on the stage at diagnosis and other factors, and overall prognosis remains poor, with a relatively low five-year survival rate compared to other types of cancer. In this work, the objective is to present current approaches to lung cancer diagnosis through the study of multiple genetic alterations and biomarkers, mainly detected by next-generation sequencing (NGS), which has significantly transformed cancer diagnostics by enabling highthroughput and cost-effective genomic analysis. In the context of lung cancer, NGS plays a crucial role in improving molecular characterization, guiding targeted therapies, and supporting personalized medicine strategies. Specifically, its relevance lies in the ability to provide a comprehensive genomic profile of the tumor, identify driver mutations, predict treatment response, detect co-occurring alterations, and assist in therapeutic stratification. A real-world case study was conducted including 101 patients diagnosed with lung cancer between 2023 and 2025 in a reference laboratory, whose tumors were analyzed using NGS. The most frequently altered genes identified were KRAS, EGFR, and ALK, together with other less common but clinically actionable alterations, as well as the evaluation of programmed death-ligand 1 (PD-L1) expression by immunohistochemistry. In summary, next-generation sequencing represents a fundamental tool in the diagnostic workflow of lung cancer, enabling comprehensive molecular profiling that supports personalized treatment selection and contributes to improved clinical management of patients.

Abstract: This study was inspired by Alcantara-Bode’s equivalent to the Riemann Hypothesis published in 1993, the equivalent formulation consisting in the injectivity of an integral operator connected to Riemann Zeta function. Surprisingly, the research on this line has not continued, an explanation would be the lack of criteria for the injectivity of integral operators. This paper aims to fill this gap by proposing a functional-numerical analysis solution exploiting the operator positivity properties on dense sets. The main theorem says that a linear, bounded operator strict positive definite on a dense set of a separable Hilbert space, has its null space containing only the null element, equivalently, it is injective. Having in mind to obtain a generic and useful criterion, we gradually changed the hypothesis of the strict positivity of the operator on a dense set to the involvement at the end, of the associated Hermitian operator that is semi positive on the whole space requesting additional properties related to the positivity of operator approximations on finite dimension subspaces. Then, in order to apply the criterion for Hermitian Hilbert-Schmidt operators, we choose an adequate dense set allowing to obtain operator sparse matrix representations. The criterion applied to the associated Hermitian of the Alcantara-Bode integral operator, showed that the equivalent holds, so the Riemann Hypothesis is true.

Abstract:

This study presents a multi-index performance system that is systematically used to assess the binder synergy and fly ash reactivity of eco-sustainable cementitious composite (ESCC) using the Strength Activity Index (SAI) as a reference in line with ASTM C618. The partial replacements of fly ash with high and low calcium fly ash (HCFA and LCFA) were added to the fly ash to sand (FA/S) ratios of 0, 10, 20, and 30% with a constant mix parameter, such as a 50% ratio of water to slag and a 20% ratio of activator to slag. Initial Flow Index (IFI) and Flow Retention Index (FRI) were used to measure fresh-state performance, and compressive-, tensile-, and flexural-based indices, i.e., SAI, Tensile Strength Index (TSI), and Flexural Strength Index (FSI), were used to measure mechanical performance. The results indicate that flowability and workability retention decrease with an increase in FA/S ratio, with LCFA-based mixtures having better flow retention than HCFA systems. The optimum mechanical performance at a replacement level of 20% FA/S produced the maximum SAI values of about 112% HCFA and 110% LCFA with a consistent increase in TSI and FSI values at 28 days. When the replacement levels were increased (30% FA/S), all strength indices decreased with the effect of dilution and decreased the packing efficiency of the binder. Comparisons of SAI with the respective TSI and FSI values through correlation analysis showed that the quantitative relationship between compressive, tensile, and flexural behavior was definite and showed that compressive strength alone is not enough to extrapolate mechanical performance. Collectively, the proposed framework provides a reasonable performance-based basis for the manner in which fly ash could be utilized in the most effective way in eco-sustainable cementitious compositions.

Abstract: Reliable estimation of kinetic parameters in molecular dynamics (MD) requires distinguishing physical phenomena from numerical artifacts. Standard MD workflows often mask integration errors through empirical damping, potentially obscuring rare configurational transitions. We introduce a calibration framework employing intentionally conservative numerical parameters—including reduced timesteps (0.10 fs) and attenuated intermolecular forces—to establish a numerical fidelity baseline. This approach isolates integration artifacts from force-field complexities, providing a reference against which production MD methods can be benchmarked. By demonstrating stable integration under maximally challenging conditions, we provide a methodology for validating the numerical foundations of kinetic inference in drug discovery applications.

Abstract: We develop an informational extension of spacetime thermodynamics in which local entropy production is coupled to spacetime curvature within an effective covariant framework. Spacetime is modeled as a continuum limit of finite-capacity information registers, giving rise to a coarse-grained entropy field whose gradients define an informational flux. Within a nonminimally coupled scalar–tensor formulation, the resulting field equations imply that the local divergence of this flux is sourced by the Ricci scalar, establishing a direct relation between curvature and entropy production. The corresponding integral form links cumulative entropy generation to the integrated spacetime curvature over a causal region. In stationary limits, the framework reproduces the Bekenstein–Hawking entropy of horizons, while in homogeneous expanding cosmologies it yields monotonic entropy growth consistent with the observed arrow of time. The construction remains compatible with unitarity at the microscopic level and with holographic entropy bounds in the stationary limit. Numerical solutions in flat FLRW backgrounds are used as consistency checks of the coupled evolution equations and confirm the expected curvature–entropy behavior across cosmological epochs. Overall, the results provide a thermodynamically consistent interpretation of curvature as a geometric source of irreversible information flow, without modifying the underlying gravitational field equations.

Abstract:

District heating systems are central to Europe’s decarbonisation efforts and its 2050 climate-neutrality target. However, given the deep embedding of district heating in the socio-economic system and built environment, meeting policy targets at the local level gives rise to a range of technical, infrastructural and socio-economic challenges. This is due to the high complexity and multidimensionality of the process, as well as the scarcity of local resources (e.g. land, surface waters, waste heat, etc.). In Bucharest, Romania, the largest district heating system in the European Union, the process of decarbonisation represents a particularly complex challenge. The system is characterised by high technical wear, heavy dependence on natural gas, significant heat losses and complex governance structures. This paper presents a strategic planning exercise for aligning the Bucharest system with the Energy Efficiency Directive 2023/1791. Drawing on system data, investment modelling and local resource mapping from the LIFE22-CET-SET_HEAT project, it evaluates scenarios for 2028 and 2035 that shift generation from natural gas to renewable, waste heat and high-efficiency sources. Options include large-scale heat pumps, waste-to-energy, geothermal and solar heat. Heat demand profiles and electricity price dynamics are used to evaluate economic feasibility and operational flexibility. The findings show that technical decarbonisation is possible, but financial viability hinges on phased investments, regulatory reforms and access to EU funding. The study concludes with recommendations for staged implementation, coordinated governance and socio-economic measures to safeguard affordability and reliability.

Abstract:

Forest ecosystems are vital to global carbon cycling as sinks or sources, while fast-growing, adaptable pines such as P. kesiya and P. oocarpa are central to national carbon sequestration efforts. This study was aimed at determining biomass accumulation variations and carbon stock dynamics between these two species at the age of 16 years in the Viphya Plantations, a prominent timber producing area in northern Malawi. Following the systematic sampling, forest inventory data was collected from 20 circular plots of 0.05 ha each. Above and below ground biomass was estimated using generic allometric models for pine species. Findings indicate that there were significant (P<0.001) differences in biomass accumulation and carbon sequestration between P. oocarpa and P. kesiya plantations. P. oocarpa accumulated more biomass (298.86±12.09 Mgha^-1) than P. kesiya (160.13±23.79 Mgha^-1). Furthermore, P. oocarpa plantation had a higher annual carbon sequestration (32.22±1.30 tCO₂e/ha/yr) as compared to P. kesiya plantation (17.26±2.56 tCO₂e/ha/yr). In addition, the uncertainty was less than 1% and fit within the IPCC’s recommended range (<15%). Therefore, the study has demonstrated that species selection should match management objectives: P. oocarpa maximizes short-to-medium term carbon sequestration and productivity, while P. kesiya supports long-term soil carbon stability. Hence, integrating both optimizes carbon benefits.

Abstract:

Background/Objectives: Rural and underserved adults face barriers to hypertension (HTN) self-management, and in-person lifestyle education programs in academic medical settings may have limited reach This pilot study evaluated a publicly available HTN self-management app (iOS/Android) with respect to feasibility, perceived usefulness, user satisfaction, and user-entered metrics relevant to HTN and lifestyle management. Methods: We conducted an internet-based, single-arm pilot of a mobile app available in commercial app stores. Adults aged ≥19 years who downloaded the free app and reported HTN self-enrolled via in-app registration and electronic consent; no direct recruitment or compensation was provided. Outcomes included an in-app questionnaire (HTN history, perceived BP status, concern, and a key self-management behavior) and app engagement/health-entry data (registration counts; use of tracking features; distributions of user-entered metrics). Results: From June 2020–July 2025, 819 users completed the in-app questionnaire; five were excluded as spam (N=814). Responses clustered in 2021 (76.8%), and completion time was brief (median 91 s; IQR 65–131). Most respondents reported hypertension for >2 years (57.3%; 21.5% unsure). Perceived BP was “normal” (42.1%), “borderline” (24.8%), or “high” (15.2%), with 15.0% unsure. For a key self-management behavior, only 21.8% reported measuring their blood pressure “usually/always,” while 24.8% reported never measuring their blood pressure. More than half were at least somewhat concerned about their BP (56.6%). Conclusions: In a largely rural, southeastern context, this publicly available HTN app demonstrated feasible low-touch uptake and captured user-entered self-management data, though sustained tracking occurred in a subset of users. Findings support further pragmatic testing focused on engagement, equity, and integration into nurse-led care workflows.

Abstract: We develop Real–Now–Front (RNF) cosmology, a generative framework in which spacetime arises dynamically as an advancing physical present aligns a pre-geometric chronon medium. Chronons are alignment degrees of freedom, not quanta of time; their coherent ordering induces Lorentzian geometry, causal structure, and operational rods and clocks. The dynamics are governed by the Temporal Coherence Principle (TCP), a local alignment and relaxation rule that reconstructs matter patterns and selects a preferred coherence density, so that spacetime symmetries emerge as stable operational properties rather than being postulated. Because each RNF advance encounters a metric-free layer, TCP enforces geometric rescaling to restore coherence, yielding kinematic cosmic expansion without vacuum energy and a local, self-tuning Hubble flow. Under-coherent regions expand, over-coherent regions shrink and collapse, and near-equilibrium regions evolve GR- and FRW-like, with vacuum-dominated regions generically producing late-time acceleration. Chronon microphysics further imposes a universal curvature bound through the Chronon Exclusivity Principle (CEP), leading to finite-density, nonsingular cores with R_core ∝ M^1/3. Small cores (Micro Chronon Condensates) provide a natural cold dark matter candidate, while larger cores reproduce general-relativistic black-hole exteriors with CEP-regulated interiors. RNF cosmology also predicts a mild two-metric structure, yielding small but testable distance–redshift deviations while qualitatively reproducing the large-scale phenomenology of ΛCDM.