Abstract: This work synthesizes a complete paradigm developed across multiple interconnected publications, and we propose a paradigm where space itself is the fundamental, elastic medium. This framework rehabilitates an absolute reference frame while fully preserving the mathematical formalism of Special Relativity as an effective observational theory. The model replaces the postulate of a featureless vacuum with three physical principles: space elasticity, wave-matter identity, and energy-conserving In/Out wave circulation. From these we derive a self-consistent field theory where particles emerge as localized standing waves and photons as self-propelled dipoles. The paradigm provides mechanistic explanations for core principles: velocity and kinetic energy are geometric deformation states of matter waves, and the equivalence of inertial and gravitational mass follows from their common origin in total deformation energy. Crucially, it yields testable predictions that distinguish it from standard physics, including an asymmetric outcome for the symmetric Twin Paradox scenario and measurable asymmetries in high-energy ion collisions. This work offers a realist, wave-based foundation for reconciling quantum non-locality, relativistic effects, and gravitational interaction.

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Climate change poses increasing risks to global food security, with maize production in vulnerable regions such as Nakuru County, Kenya, and Northwest China expected to be significantly affected. This study assessed the impacts of future climate conditions on maize growth and yield in the 2030s (2021–2040) and 2050s (2041–2060) under RCP 4.5 and RCP 8.5, relative to a 1986–2005 baseline. The CERES-Maize model was used to simulate the effects of projected changes in temperature, precipitation, and solar radiation, and to evaluate the effectiveness of key adaptation strategies. Results showed that climate change is likely to shorten maize growing durations by up to 34 days in Nakuru County and 38 days in Northwest China, leading to yield reductions of 2.7–26.5% and 4.6–22.4%, respectively, with stronger impacts in the 2050s and under RCP 8.5. Simulations further demonstrated that adaptation measures—including adjusting planting dates, applying appropriate irrigation, and adopting improved cultivars—could increase maize yields by 20.7–38.6% in Nakuru and 17.6–28.6% in Northwest China, depending on the scenario. These findings indicate that integrating multiple adaptation strategies can substantially reduce climate-induced yield losses, emphasizing the need for investment in irrigation infrastructure, climate services, and cultivar improvement to safeguard future maize production.

Abstract: Despite substantial growth in eco-innovation (EI) research, most studies rely on cross-sectional data, limiting understanding of the temporal dynamics of EI and its determinants under varying macroeconomic conditions. This study addresses this gap by analysing panel data from Spanish manufacturing firms across three phases of the business cycle: pre-crisis expansion (2004–2007), the global financial crisis (2008–2013), and recovery (2014–2016). We investigate the drivers of two distinct types of eco-innovation: efficiency EI (energy and material savings) and environmental EI (reducing environmental harm), focusing on the role of regulation, institutional interventions, and firm-level innovation capacities. Using a random-effects panel probit model that accounts for unobserved firm heterogeneity, we examine how these drivers operate across different macroeconomic contexts. Our findings reveal that regulation consistently fosters EI, while the influence of subsidies, R&D capacity, and collaborative networks is more context-dependent, particularly during economic downturns. The results highlight the cumulative, path-dependent, and cyclical nature of eco-innovation, providing novel insights into the conditions that enable firms to sustain green innovation over time.

Abstract: We propose a geometrically motivated framework in which the large-scale evolution of the Universe is described by a coherent multidimensional wavefunction possessing a preferred direction of propagation. Within this formulation, the scalar envelope of the wavefunction defines a critical hypersurface whose temporal evolution provides an effective geometric description of cosmic expansion. The resulting picture naturally incorporates an arrow of time, large-scale homogeneity, and a nonsingular expansion history, without invoking an inflationary phase, a cosmological constant, or an initial singularity. The critical hypersurface takes the form of a three-dimensional sphere whose radius plays the role of a cosmological scale factor. Its evolution leads to a time-dependent expansion rate with a positive but gradually decreasing acceleration. The associated density evolution follows a well-defined scaling law that is consistent with the standard stress–energy continuity equation and corresponds to an effective equation-of-state parameter w = -1/3. As a consequence, the total mass–energy contained within the expanding hypersurface increases with time in a manner that remains fully compatible with the continuity relation. Analytical estimates derived from the model yield values for the present expansion rate and mean density that are in close agreement with current observational constraints. Within this geometric interpretation, the gravitational constant emerges as an invariant global potential associated with the critical hypersurface, linking the conserved properties of the wavefunction to observable gravitational coupling. The framework therefore provides a self-consistent, effective description in which cosmic expansion and gravitational dynamics arise from the geometry of a universal wavefunction, suggesting a deep connection between quantum structure, spacetime geometry, and cosmological evolution.

Abstract: Astronaut training has undergone significant transformation since the early days of human spaceflight, evolving in response to technological advances, changing mission objectives, and the increasing complexity of international cooperation. This paper provides a historical overview of astronaut training, tracing its development from the early Space Race era to the present day. It examines how initial training programs, largely focused on military pilots and short-duration missions, have expanded to encompass a broader range of skills, disciplines, and professional backgrounds. The paper compares astronaut training approaches across six major spacefaring entities: NASA (United States), Roscosmos (Russia), ESA (Europe), CNSA (China), JAXA (Japan), and CSA (Canada), highlighting both commonalities and differences shaped by national priorities, organizational culture, and mission requirements. In addition, the paper discusses the emergence of commercial human spaceflight and its impact on training philosophies, regulatory frameworks, and safety considerations. By outlining historical trends and current practices, this paper provides a comprehensive overview of astronaut training in the new era of spaceflight and identifies key factors influencing its continued evolution.

Abstract: This work presents a time-domain analog-to-digital conversion method in which the amplitude of a sensor signal is encoded through its crossing instants with a periodic ramp. The proposed architecture departs from conventional ADC and PWM demodulation approaches by shifting quantization entirely to the time domain, enabling waveform reconstruction using only a ramp generator, an analog comparator, and a timer capture module. A theoretical framework is developed to formalize the voltage-to-time mapping, derive expressions for resolution and error, and identify conditions that ensure monotonicity and single-crossing behavior. Simulation results demonstrate high-fidelity reconstruction for both periodic and non-periodic signals, including real photoplethysmographic (PPG) waveforms, with errors approaching the theoretical quantization limit. A hardware implementation on a PSoC 5LP microcontroller confirms the practicality of the method under realistic operating conditions. Despite ramp nonlinearity, comparator delay, and sensor noise, the system achieves effective resolutions above 12 bits using only native mixed-signal peripherals and no conventional ADC. These results show that accurate waveform reconstruction can be obtained from purely temporal information, positioning time-encoded sensing as a viable alternative to traditional amplitude-based conversion. The minimal analog front end, low power consumption, and scalability of timer-based processing highlight the potential of the proposed approach for embedded instrumentation, distributed sensor nodes, and biomedical monitoring applications.

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Multi-component polymer hydrogels present complex physiochemical interactions that make accurate compositional analysis challenging. This study evaluates three analytical techniques: Nuclear Magnetic Resonance (NMR), Advanced Polymer Chromatography (APC), and Thermogravimetric Analysis (TGA) to quantify polyvinyl alcohol (PVA) and polyethylene glycol (PEG) content in hybrid freeze-thaw derived PVA/PEG/PVP hydrogels. Hydrogels were synthesized using an adapted freeze–thaw method across a wide range of PVA:PEG ratios, with PVP included at 1 wt% to assess potential intermolecular effects. NMR and APC reliably quantified polymer content with low average errors of 2.77% and 2.01%, respectively, and were unaffected by phase separation or hydrogen bonding within the composite matrix. TGA enabled accurate quantification at PVA contents ≤62.5%, where PEG and PVA maintained distinct thermal decomposition behaviors. At higher PVA concentrations, increased hydrogen bonding and crystalline restructuring, confirmed by FTIR through shifts near 1140 cm⁻¹ and significant changes in the –OH region, altered thermal profiles and reduced TGA accuracy. Together, these findings establish APC as a high-throughput alternative to NMR for multi-component polymer analysis and outline critical thermal and structural thresholds that influence TGA-based quantification. This work provides a framework for characterizing complex polymer networks in biomedical hydrogel systems.

Abstract: Systems favoring cross-pollination such as male sterility and female flowering type are of great importance in the development of new hybrid cultivars and their seed production. The advantages of male sterility are expressed in production of cheaper and competitive seeds. The presence of this characteristic in watermelon is not common, and in some cases, it is accompanied by negative manifestations. A collection of 150 watermelon genotypes was tested at the Maritsa Vegetable Crops Research Institute, Bulgaria, over the past nine years to search for a genetic source of male sterility. The results revealed that two mutations were found. The first mutation was in a plant of the Asar variety, which formed completely degenerated structures in the place of male and female flowers that were completely sterile. The other mutation affected male flowers, female flowers and leaf shape. Male flowers produced a small amount of pollen. Female flowers were formed but they were sterile and aborted at an early stage. The genotype can be propagated by pollination of the normal plants, which in the next generation segregate into mutant - 25%, and normal - 75%. The gene source is phenotyped according to the main characteristics of the fruits and the vegetation period. The mutation found cannot be directly used in a breeding program, but it is of interest for studying this important trait. The success of detecting flowers that are sterile depends on the number of watermelon plants, which for the conditions of the experiment amounted to a minimum of 4492 plants at a probability level of P3 – 0.95.

Abstract: In this paper, a wireless network architecture is considered that combines double Intelligent Reflecting Surfaces (IRSs), Energy Harvesting (EH), and Non-Orthogonal Multiple Access (NOMA) with cooperative relaying (C-NOMA), to leverage the performance of Non-Line-of-Sight (NLoS) communication and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behaviour and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in terms of spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication, compared to conventional systems, thereby facilitating green communication systems.

Abstract: The clinical management of Pancreatic Neuroendocrine Neoplasms (Pan-NENs) is complicated by the disease's intrinsic variability, which creates significant hurdles for accurate risk profiling and the standardization of treatment protocols. Recently, Artificial Intelligence (AI) has offered a promising avenue to address these challenges. By integrating and processing high-dimensional multimodal datasets (encompassing clinical history, radiomics, and pathology), these computational tools can refine survival forecasts and support the development of personalized medicine. However, the transition from experimental success to routine clinical use is currently obstructed by reliance on limited, retrospective cohorts that lack external validation, alongside unresolved concerns regarding algorithmic transparency and ethical governance. This review evaluates the current landscape of AI-driven prognostic modeling for Pan-NENs and critically examines the pathway towards their reliable integration into clinical practice.

Abstract: The South Africa national programme aims to enhance energy in informal settlements by maximising access to energy, alleviating poverty, and promoting urban inclusion. Nevertheless, a high rate of unlawful connections and old distribution systems has been identified in municipalities already experiencing financial poverty. This article assesses the suitability of electrifying informal settlements, mainly by funding capital programmes with grants. These programmes have beneficial effects on municipal revenue, non-technical electricity losses (NTLs), network destruction, and sustainability in the long run. Based on recent studies, city reports, and case studies in large cities, this paper concludes that electrification without commensurate investment in revenue protection systems, reinforced infrastructure, and institutional changes drives losses in revenues and leads to additional maintenance burdens. This article ends with policy guidelines, which aim to unify the process of electrification with sustainable municipal revenue recovery.

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The increasing demand for sustainable energy production necessitates the development of innovative technologies for converting municipal waste into valuable energy offering a viable alternative to fossil fuels. This study presents a flexible, portable, and expandable waste-to-energy concept that integrates gasification and pyrolysis processes production of combustible gases and liquid fuels. Particular emphasis is placed on the use of transparent and interpretable modeling approaches to support system optimization and future scalability. The proposed methodology is demonstrated on two experimental systems currently operated at CEET Explorer, VSB – Technical University of Ostrava, Czech Republic: (i) a primary gasification facility equipped with a plasma torch, reactor, hydrogen separator and tank, fuel cells, and renewable grid connections; and (ii) a secondary pyrolysis unit designed to maximize pyrolysis oil production. Both systems are modeled and simulated using in-house software developed in Python, employing stoichiometric balances, symbolic regression, and polynomial regression to represent chemical reactions and energy flows. The findings demonstrate that transparent models—such as stoichiometric modeling combined with interpretable machine learning—can accurately reproduce the operational behavior of waste-to-energy processes. Gasification is optimized for hydrogen generation and electricity production via fuel cells, whereas pyrolysis favors liquid fuel yield with syngas as a by-product. Molar mass relations are applied to ensure consistent conversion between mass and volume across gasification, pyrolysis, and combustion pathways, maintaining the conservation of mass. Overall, the integration of stoichiometric balance models with symbolic and polynomial regression provides a reliable and interpretable framework for simulating real waste-to-energy systems. The current results, based on bio-wood waste from the Czech Republic, validate the proposed methodology, which is made openly available to promote transparency, reproducibility, and further advancement of sustainable waste-to-energy technologies.

Abstract: Diatoms are key primary producers and sensitive indicators in polar freshwater ecosystems, responding rapidly to environmental change. This study investigates diatom species richness and the influence of environmental variables in fourteen coastal lakes on King George and Horseshoe Islands in the maritime Antarctic. Water and surface sediment samples collected in 2017, 2019, and 2020 were analyzed using light and scanning electron microscopy, revealing 86 diatom species and genera across all lakes except Lake 5. King George Island exhibited higher species richness, with frequent occurrences of Planothidium lanceolatum, Achnanthidium dolomiticum, Fragilaria capucina and Nitzschia homburgiensis. On Horseshoe Island, common taxa included Achnanthes, Achnanthidium, Fragilaria, Nitzschia, Navicula, and Gomphonema. Among the previously measured water chemistry variables, HCO3- (ρ = 0.78, p = 0.005) and K+ (ρ = 0.69, p = 0.019) showed the strongest positive correlations with diatom species richness. Major ions and nutrients exhibited moderate relationships with DO, salinity, and pH. In contrast, temperature and trace metals displayed weak or negligible correlations, suggesting indirect influences on diatom diversity. These findings demonstrate that diatom communities in the Maritime Antarctic lakes are highly diverse and are strongly shaped by variations in water chemistry, underscoring the ecological sensitivity of these freshwater ecosystems.

Abstract: Despite significant global commitment to smart buildings and Digital Twin technologies within architecture research and practice, existing systems face fundamental challenges: they widely suffer from data silos, hindering comprehensive data integration; they are constrained by cognitive limitations, preventing deep learning from predicting complex behaviors and spatial intentions; their design goals are fundamentally device-centric, rather than human-centric; and they operate in a state of environmental isolation, lacking dynamic coordination with the external environment. To address these bottlenecks, this paper proposes the BLUE Building Paradigm, a novel and pioneering framework for next-generation spatial intelligence. BLUE represents four core pillars: Big-data (B), Learning (L), User (U), and Environment (E). The core contribution of the BLUE Building is the construction of a Spatio-Temporal Cognitive Operating System, which, through a unified Spatial Semantic Graph and Adaptive Reinforcement Learning, achieves a deep understanding of spatial states and user intentions, and forms a dynamic, continuously optimized closed-loop synergy with the external environment. This marks an epoch-making transition in spatial intelligence from passive automation to proactive cognition and continuous self-adaptation. This paper details the operational mechanism of the BLUE Paradigm, its key technical implementations, and cross-scale interaction strategies. Furthermore, it introduces the BLUE Building Rating and Evaluation Mechanism, including its potential for expansion, translating the paradigm's cognitive capabilities into quantifiable industry standards to drive adoption. The BLUE Building Paradigm not only sets a new benchmark for building energy efficiency and occupant well-being but also lays a solid theoretical foundation for the resilient, sustainable, and integrated development of future urban systems and the human experience.

Abstract:

In this study, three point mutations of EGFR relevant to lung cancer therapy are detected. Mutated EGFR is the target of a therapy for non-small cell lung cancer (NSCLC) using tyrosine kinase inhibitors (TKIs) as treatment drugs. Background/Objectives: Point mutations in exon 21 (L858R and L861Q) of the EGFR gene are TKI-sensitive; however, mutations in exon 20 (T790M) are TKI-resistant. Therefore, a fast detection method that classifies a NSCLC patient to be drug sensitive or drug resistant is highly clinically relevant. Methods: Probes were designed to detect three point mutations in genomic samples based on DNA hybridization on a solid surface. A method has been developed to detect single nucleotide polymorphism (SNP) for these mutation detections in the 16-channel nanobioarray chip. The wash by gold-nanoparticles (AuNP) was used to assist the differentiation detection Results: The gold nanoparticle-assisted wash method has enhanced differentiation between WT and mutated sequences relevant to the EGFR sensitivity to tyrosine kinase inhibitors. Conclusions: The WT and mutated sequences (T790M, L858R and L861Q) in genomic samples were successfully differentiated from each other.

Abstract: This study introduces an all-fiber optic sensing network based on fiber Bragg grating (FBG) technology for structural health monitoring (SHM) of launch vehicle payload fairings un-der extreme thermo-mechanical conditions. A wavelength–space dual-multiplexing ar-chitecture enables full-field strain and temperature monitoring with minimal sensor de-ployment. Structural deformations are reconstructed from local measurements using the inverse finite element method (iFEM), achieving sub-millimeter accuracy. High-temperature experiments verified that FBG sensors maintain a strain accuracy of 0.8 με at 500 °C, significantly outperforming conventional sensors. Under 15 MPa mechanical loading and 420 °C thermal shock, the fairing structure exhibited no damage propagation. The sensing system captured real-time strain distributions and deformation profiles, con-firming its suitability for aerospace SHM. The combined use of iFEM and FBG enables high-fidelity, large-scale deformation reconstruction, offering a reliable solution for reusa-ble aerospace structures operating in harsh environments.

Abstract: Medium-sized cities are increasingly affected by processes of urban fragmentation and residential segregation, despite having traditionally been perceived as more socially cohesive and territorially balanced than large metropolitan areas. These cities often act as functional connectors between metropolitan hubs and rural regions, yet they are particularly vulnerable to unplanned suburban growth, housing market polarization and uneven access to urban opportunities. This study develops and applies a multidimensional Urban Territorial Index (UTI) to assess socio-spatial inequality in Ciudad Real, a medium-sized city in central Spain, and its functional urban area. The UTI integrates six indicators across three analytical dimensions—socioeconomic, sociodemographic and housing—using a weighted composite approach informed by principal component analysis and implemented through GIS-based spatial analysis. The index is calculated at census-section and neighborhood scales and externally validated against a local Human Development Index, showing a strong correlation (r = 0.87; p < 0.001). The results reveal a pronounced core–periphery polarization. Central and southern neighborhoods associated with strategic infrastructures—such as the university, high-speed rail station and hospital—concentrate higher income levels, educational attainment and land values, while peripheral municipalities and disadvantaged neighborhoods exhibit higher unemployment, lower rents and greater concentrations of migrant populations. The analysis also identifies suburban municipalities with intense housing construction but demographic stagnation, leading to population–housing mismatches, underutilized developments and service provision deficits. Methodologically, the UTI proves to be a robust and replicable tool for capturing multidimensional urban vulnerability in medium-sized cities, where metropolitan-scale indices often fail to detect fine-grained socio-spatial disparities. Substantively, the findings demonstrate that infrastructure-led development and suburban expansion can reinforce fragmentation and segregation in non-metropolitan contexts when not accompanied by integrative planning strategies. The study contributes to ongoing debates on spatial justice, urban governance and sustainable development, offering policy-relevant insights for medium-sized cities across Southern Europe and comparable urban regions.

Abstract: Over the last decade, bark beetle outbreaks have significantly impacted forests in Central Europe, causing extensive loss of forest cover. We evaluated the impact of partial deforestation in three mountain forest catchments in the Jeseníky Mountains, comparing them with the unaffected Červík catchment (Beskydy Mountains) and the severely affected Pekelský stream catchment (Czech-Moravian Highlands). Atmospheric deposition in the catchments was similar, with total element input driven primarily by precipitation volumes rather than ion concentrations. We did not observe the hypothesized increase in DOC and nitrogen export, although nitrate outflow was slightly higher than atmospheric input in two cases. Significant export of calcium, magnesium, and bicarbonates was driven mainly by the geology of the individual catchments. The limited impact of bark beetle outbreaks on DOC dynamics can be attributed to the relatively low proportion of clear-cut areas and the rapid development of ground vegetation on impacted sites.

Abstract: Traditional mean–variance portfolio optimization is ill-suited to cryptocurrency markets, where extreme volatility, fat-tailed distributions, and unstable correlations undermine variance as a risk measure. To overcome these limitations, this paper develops a unified entropy-based framework for portfolio diversification grounded in the Maximum Entropy Principle (MaxEnt). Within this formulation, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) emerge as complementary specifications derived analytically via the method of Lagrange multipliers, ensuring mathematical tractability and interpretability. Empirical validation is conducted on a portfolio of four leading cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—using weekly return data from January to March 2025. Results reveal that Shannon entropy converges to near-uniform diversification, Tsallis entropy (q = 2) penalizes concentration more strongly and enhances robustness against tail risk, while WSE integrates asset-specific informational priorities, aligning allocations with investor preferences or market characteristics. Comparative analysis confirms that all three models yield allocations more resilient and structurally balanced than variance-driven portfolios, mitigating estimation risk and concentration effects. This study provides a coherent mathematical formulation of entropy-based portfolio optimization by embedding Shannon, Tsallis, and Weighted Shannon entropies within a common Maximum Entropy (MaxEnt) optimization framework. Beyond its immediate empirical scope, this work also opens several avenues for future research. First, entropy-based portfolio construction can be extended to dynamic multi-period settings with transaction costs and liquidity frictions, which are particularly relevant in cryptocurrency markets. Second, the framework may be generalized to incorporate alternative entropy measures such as Rényi or Kaniadakis entropy, enabling more refined sensitivity to tail risks and nonlinear dependencies. The proposed framework provides a flexible foundation for future extensions toward dynamic, multi-period portfolio optimization under uncertainty.

Abstract: New nuclear power plant (NPP) designs, particularly advanced reactors and small modular reactors (SMRs) are expected to be highly automated, changing job demands and shifting the roles and responsibilities of operators. The expanded capabilities of machines and their more prominent role in plant operation means that operators need new information to support effective human-automation teaming and the maintenance of situation awareness. To understand the impact of new automation and artificial intelligence (AI) technology in NPP control rooms, a systematic literature review (SLR) on function allocation (FA) methods was conducted. This SLR focused on four areas. (1) Identifying the prevalence of quantitative, qualitative, and mixed methodologies. (2) Developments in levels of automation frameworks. (3) Revisions to Fitts List. (4) Ena-bling factors for improved access to data-driven approaches. The review was limited to work occurring after 1983, when the U.S. Nuclear Regulatory Commission published research on FA [1]. The results of the review demonstrate that many of the post-1983 methods are qualitative and descriptive. The review also identified several themes for managing human out of the loop issues. The discussion closes with proposed future work leveraging large language models and simulator-based approaches to enhance existing FA methods.