Abstract: Metal homeostasis, which coordinates the influx and efflux of essential elements such as iron (Fe) and manganese (Mn) in chloroplasts, is essential for optimum photosynthesis, especially in metal accumulating plants. Brassica juncea (Indian mustard) is a metal-tolerant species with a strong metal accumulation capacity, making it a suitable model for studying transition metal homeostasis. In this study, we identified two efflux transporters, BjYSL6.1 and BjYSL6.4, that localize in the endomembrane system of Schizosaccharomyces pombe and interact with the chloroplast Mn influx transporter BjNRAMP4.1 at the plasma membrane and within the chloroplasts. Bimolecular fluorescence complementation and split-ubiquitin yeast two-hybrid assays confirmed specific protein-protein interactions among these transporters, as well as with the membrane-bound thioredoxin BjHCF164, a known regulator of photosynthetic electron transport. Gene expression studies revealed that BjNRAMP4.1 and BjYSL6 isoforms are inversely regulated under Fe and Mn stress conditions, with BjNRAMP4.1 being strongly induced under deficiency, whereas BjYSL6.1 and BjYSL6.4 are downregulated. These findings suggest that a coordinated network involving BjNRAMP4.1, BjYSL6s, and BjHCF164 modulates metal influx and efflux at the chloroplast and plasma membrane interfaces, thereby maintaining metal homeostasis, which is critical for photosynthetic efficiency in B. juncea.

Abstract: Background: The Gibbs Energy Redistribution Theory (GERT) program established a thermodynamic ontology for cosmology (Paper I) and later identified the post-relativistic dissolution boundary of the relativistic ruler in the Hyperdilute Regime (Paper II). The complementary open question is the onset of relativistic metric legibility in the early Universe. Objective: To determine, within GERT, the emergence boundary of the relativistic metric ruler and define the lower limit of validity of the effective relativistic regime. Methods: We define the metric-emergence parameter Ξ(α) ≡ λγ(α)/dph(α), where λγ is the photon mean free path and dph is the GERT particle horizon. The boundary is set by Ξ = 1. We compute αem using two recombination treatments (Saha equilibrium and Peebles kinetics) and test robustness against the unknown Primordial Cauldron boundary αPC. Results: We obtain αem = −3.0±0.1, with uncertainty dominated by recombination kinetics (Saha vs. Peebles). Varying αPC over 25 orders of magnitude changes αem by less than 5×10−4, showing strong insensitivity to primordial microphysics. Together with Paper II (αcrit = 12.88 ± 0.12), the relativistic GERT domain spans 15.9 ±0.2 decades in α = log10(a). Conclusions: The relativistic ruler is an emergent operational regime, not an ontologically unlimited one. GERT nowprovides a complete domain map with pre-relativistic, relativistic, and post-relativistic sectors. The onset and dissolution boundaries are thermodynamically controlled, giving a symmetric validity structure for Layer 3.

Abstract: We ask for finite field versions of following three: (1) Grothendieck Inequality, (2) Johnson-Lindenstrauss Flattening Lemma, (3) Bourgain-Tzafriri Restricted Invertibility Theorem.

Abstract: Background: Emergency obstetric situations require rapid clinical decision-making, technical competence, and emotional preparedness to ensure safe and compassionate care for both mother and newborn. However, nursing students often have limited opportunities to experience such high-risk, low-frequency events during clinical placements. Simulation-based education has emerged as an effective strategy to prepare future nurses for caring in emergency contexts, allowing them to develop both technical and non-technical skills in a safe learning environment. This study aimed to evaluate the impact of a high-fidelity obstetric emergency simulation program on nursing students’ knowledge, perceived safety, and learning experience. Methods: A mixed-methods design was employed, combining a quasi-experimental pretest–posttest assessment without a control group and qualitative analysis of open-ended reflections. Eighty-two third-year nursing students participated in two simulation sessions addressing obstetric emergencies such as breech birth, shoulder dystocia, out-of-hospital delivery, eclampsia, postpartum hemorrhage, and maternal cardiac arrest. Data were collected using validated instruments measuring knowledge, perceived safety, and satisfaction and self-confidence in learning, and were analyzed using Wilcoxon signed-rank tests and thematic analysis. Results: Significant improvements were observed in students’ knowledge of complex obstetric maneuvers and in their perceived safety when managing emergency situations (p < .001, r > .40). Participants reported high levels of satisfaction and confidence in learning. Qualitative findings highlighted increased emotional preparedness, improved clinical reasoning, and recognition of the importance of teamwork and reflective debriefing in emergency care contexts. Conclusions: High-fidelity simulation is an effective educational strategy for preparing nursing students to provide safe and confident care in obstetric emergencies. Integrating simulation into nursing curricula can strengthen both technical competence and the emotional readiness required for caring in urgent and high-pressure clinical situations.

Abstract:

Background: Cardiac arrest is the third leading cause of natural death in Europe and thus presents a growing burden on both our society and healthcare system. There has been very little research done on cardiac arrests of non-cardiac origin despite their increasing incidence, as they represent a heterogenous group of patients in which the type and outcome of treatment vary depending on the underlying cause of the cardiac arrest. Aim: The aim of our study is to research how the Slovenian healthcare system has worked and currently works in the field of cardiac arrests of non-cardiac origin. Methods: Our study was descriptive and retrospective. We compared 2 time periods, 2010/2011 and 2022/2023. Our sample included all patients admitted to Centre for Intensive Internal Medicine (CIIM) during these periods after either out-of-hospital or in-hospital cardiac arrest of non-cardiac origin. Results: The incidence of all cardiac arrests of non-cardiac origin was higher in 2022/2023 (Hi-squared test, p=0.021), while the incidence of those that occured in-hospital was lower in 2022/2023 (Hi-squared test, p=0.007). The number of male patients was higher in the second period (Hi-squared test, p=0.013). The age of the patients did not differ significantly between the two periods (Student's t-test, p>0.05). ICU stay was longer in the second period (Mann Whitney U test, p=0.027). The number of tests performed was higher and treatment was more aggressive in the second period than in the first period. Patient survival was higher in the second period in the in-hospital cardiac arrest of non-cardiac origin group (Student's t-test, p=0.048). Conclusion: The incidence of cardiac arrest of non-cardiac origin in Slovenia has been increasing through the years. Better hospital treatment results in better overall survival and a lower incidence of in-hospital cardiac arrests. More patients with out-of-hospital cardiac arrests are nowadays being resuscitated by lay bystanders in the field, so patients' survival to hospital admission is higher. The proportion of male patients is increasing, age is not changing significantly. Despite better diagnosis processes, new treatments and improved knowledge, the survival and neurological outcome of patients have not improved significantly.

Abstract: Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used around the world for their pain-relieving and fever-reducing properties. However, excessive intake of NSAIDs can lead to harmful effects on multiple body systems, including the cardiovascular, gastrointestinal, hepatic, renal, and nervous systems. The anti-inflammatory activity of 34 derivatives of 1-aryl-6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline was investigated in vivo. A relationship between the activity of the compounds and the nature of the substituents, as well as their positional and mutual arrangement in the C ring (1-Ar-), was established. In silico modelling of 1-aryl-6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline derivatives interaction with COX-2 (PDB ID: 1PXX) active site indicated that the nitro-derivatives exhibited the highest stability owing to their superior capacity for electrostatic and hydrogen bond formation compare to brominated compounds. The presented data on the effect of substituents –NH₂, –OH, and –OCH₃ in ring C (1-Ar-) of 1-aryl-6,7-dimethoxy-1,2,3,4-tetrahydroisoquinolines on anti-inflammatory activity further stimulate the search for new highly effective derivatives within this series.

Abstract: Deploying foundation models in embodied edge systems is fundamentally a systems problem, not just a problem of model compression. Real-time control must operate within strict size, weight, and power constraints, where memory traffic, compute latency, timing variability, and safety margins interact directly. The Deployment Gauntlet organizes these constraints into eight coupled barriers that determine whether embodied foundation models can run reliably in practice. Across representative edge workloads, autoregressive Vision-Language-Action policies are constrained primarily by memory bandwidth, whereas diffusion-based controllers are limited more by compute latency and sustained execution cost. Reliable deployment therefore depends on system-level co-design across memory, scheduling, communication, and model architecture, including decompositions that separate fast control from slower semantic reasoning.

Abstract: Engineer-to-Order (ETO) manufacturers face persistent cost and complexity challenges driven by product variety, including duplicate components, redundant variants, and inconsistent procurement setups. Although enterprise resource planning (ERP) and product lifecycle management (PLM) systems contain detailed Bills of Materials (BOMs) and procurement records, they typically lack portfolio-wide support for systematic cross-product commonality analysis without substantial manual effort. Prior approaches are either conceptual (e.g., indices and modularity frameworks) or ad hoc in practice, often relying on one-off spreadsheet analyses. This paper introduces the concept of Product Commonality Analysis Tools (PCATs) and develops and evaluates a lightweight PCAT in an action-research collaboration with a European ETO laser manufacturer. The PCAT operates on exported enterprise data to provide interactive portfolio-level views of component reuse and cross-product consistency. Its usefulness is evaluated through scenario-based think-aloud usability sessions and a functional comparison against Excel workarounds, standard ERP/PLM reporting, and vendor customizations. The results indicate that a lightweight PCAT can be integrated into existing ERP/PLM workflows with minimal disruption and can reduce the effort required to prepare reusable portfolio views for engineering and procurement reviews.

Abstract: Polyfluoroalkyl substances (PFAS) are synthetic compounds shown to be associated with metabolic disturbances in the experimental literature. Evidence of the relationship between PFAS and MetS from human epidemiological studies remains inconclusive and warrants further study. This study leverages a pooled index to examine associations between a mixture of PFAS and metabolic syndrome in a sample of adults in the United States. Using data from the National Health and Nutrition Examination Survey 2005-2018 (n= 8095), we examined the relationship between serum concentrations of perfluorohexanesulfonic acid, perfluorononanoic acid, perfluorodecanoic acid, perfluoroundecanoic acid and 2-(N-Methyl-perfluorooctane sulfonamido acetic acid, and MetS. We evaluate individual associations with logistic regression and joint associations in a pooled index (PI) model. One standard deviation increase in the PI was associated with 18% decrease in odds of MetS (OR: 0.82, 95% CI: 0.76, 0.89). In logistic regression models, higher PFAS concentrations were also associated with decreased odds of MetS in perfluorodecanoic acid (PFDA) (OR: 0.43, 95% CI: 0.28, 0.64) and perfluoroundecanoic acid (PFUA) (OR: 0.19, 95% CI: 0.11, 0.36). This study found an inverse association between serum PFAS concentration and MetS, in both pooled and individual models; however, given the cross-sectional design, these findings should be interpreted cautiously.

Abstract: We study the long-time behavior of nonlinear stochastic evolution equations in a separable Hilbert space driven by a Q-Wiener process. The linear part of the equation is generated by a strongly continuous semigroup with an exponential dichotomy, which provides fixed rates of decay and growth. The nonlinear drift and diffusion terms are globally Lipschitz and become small as time tends to infinity. Our main result shows that under these conditions, the mean-square Lyapunov exponents of the nonlinear system coincide with those of the linear part. In other words, nonlinear stochastic perturbations that decay in time do not change the main growth or decay rates of solutions in the mean-square sense. This result provides simple and verifiable criteria ensuring that the long-time Lyapunov behavior of the nonlinear stochastic equation is fully determined by the linear semigroup, even in the presence of time-dependent stochastic perturbations.

Abstract: This study addresses the challenges of financial risk early warning by proposing a modeling approach based on spatiotemporal Transformers. The research first examines the multidimensional characteristics of financial risk, emphasizing its temporal dynamics and cross-regional interactions. It notes that many existing methods struggle to jointly capture temporal dependencies and inter-regional risk transmission patterns. To overcome these limitations, a unified spatiotemporal modeling framework is developed. The framework integrates temporal encoding, spatial adjacency information, and multi-head attention mechanisms to model long-range dependencies and regional spillover effects. In the model architecture, an embedding layer is employed to learn representations from multi-source financial indicators. A self-attention mechanism facilitates global feature interaction, while a graph convolution component further enhances the modeling of spatial relationships across markets. The final risk representation is generated through a feed-forward network with normalization layers, providing a structured basis for financial risk assessment and early warning analysis. Experimental evaluations include comparative studies and sensitivity analyses under varying missing data ratios, time window settings, and environmental conditions. The results indicate that the proposed method consistently outperforms several baseline models in terms of accuracy, precision, recall, and F1-score. Overall, the approach demonstrates strong robustness and practical applicability in complex financial settings, offering an effective tool for financial risk monitoring and decision support.

Abstract: Low-voltage distribution networks (LVDNs) serve as the final delivery layer of the electricity system, directly influencing reliability, public safety, customer service quality, and the integration of distributed energy resources. Despite their importance, LVDNs have historically received less monitoring than transmission and medium-voltage systems due to their scale, cost, and deployment complexity. Non-contact magnetic sensing has emerged as a promising alternative to invasive measurement methods for these networks. Among magnetic sensor types, giant magnetoresistive (GMR) devices are appealing because they offer high sensitivity, compactness, low power consumption, and compatibility with embedded electronics. This review assesses the current state of GMR-based monitoring for overhead and low-voltage applications, focusing on non-contact current measurement, fault detection, and fault classification. It first examines the operating characteristics of LVDNs and the unique challenge of detecting low- and high-impedance faults. Next, it outlines the physical principles behind GMR sensing, compares GMR with Hall, AMR, TMR, current transformer, and Rogowski-coil technologies, and discusses the use of multi-axis sensor heads to address cross-coupled magnetic fields in three-phase setups. Special focus is given to calibration, alignment, temperature effects, electromagnetic interference, packaging, wireless deployment, and data-driven classification. The review concludes that GMR sensors are well-suited for scalable, non-contact monitoring, but widespread adoption in the field will require better low-voltage fault datasets, standardized calibration procedures, long-term environmental testing, and closer integration with digital-twin and smart-meter infrastructures.

Abstract: In this work, the design and experimental validation of passive UHF RFID tag antennas are presented with the objective of evaluating the impact of chip placement and miniaturization approaches on tag performance. Four initial antenna layouts were developed by varying the position of the RFID integrated circuit within a coupling loop. Simulations and measurements confirmed that Antenna 1 achieved the best impedance matching, with a minimum reflection coefficient of −40 dB at 866 MHz and a power sensitivity of −16.3 dBm. Based on this reference design, a miniaturized version (Antenna 5) was obtained by integrating meander lines and capacitive end-loading, reducing the physical size while maintaining resonance at 866 MHz. Both structures were fabricated and evaluated using a Voyantic Tagformance measurement system, with read-range measurements performed under freespace conditions and in proximity to dielectric and metallic materials. The results demonstrated a maximum read range of 8.6 m for Antenna 1 in free space, while Antenna 5 preserved a read range of 6.3 m. In the presence of copper, Antenna 1 maintained a read range of 3 m, whereas Antenna 5 achieved approximately 0.5 m, confirming the robustness of the proposed designs in representative industrial environments.

Abstract: Beef production is widely recognized as a significant contributor to global greenhouse gas emissions, making robust and transparent environmental assessments essential for advancing sustainability within supply chains. This study applies a comprehensive cradle‑to‑grave Life Cycle Assessment (LCA) to evaluate the environmental performance of beef destined for export, following ISO 14040, ISO 14044 and ISO 14067 standards and the Product Category Rules for meat of mammals. Sixteen impact categories were quantified for 1 kg of vacuum‑packed beef using detailed primary data from a pasture‑based production system and a representative processing facility. The total climate change impact was 3.27×10¹ kg CO₂eq, with enteric methane and feed production jointly responsible for over 70% of overall impacts. Slaughtering and distribution were associated mainly with fossil energy use and ozone depletion, while soil carbon sequestration partially compensated biogenic emissions. The results were consistent with international benchmarks, highlighting the environmental advantages of pasture‑based systems, low fertilizer use, and stable land management. Key hotspots were identified in animal growth, feed efficiency, and manure management, with logistics also contributing notably. Overall, the study provides a high‑resolution environmental baseline that can support Environmental Product Declarations and guide targeted mitigation strategies across beef supply chains.

Abstract: Modern e-commerce platforms must handle sudden and unpredictable traffic surges caused by flash sales, festive shopping events, and viral online activity. Traditional web architectures typically adopt one of two extremes: a tightly coupled monolithic design that provides low latency but becomes fragile under heavy load, or a loosely coupled microservices architecture that improves scalability and resilience but introduces communication overhead during normal operation. This trade-off forces system designers to choose between performance efficiency and scalability robustness. This paper introduces ATLAS (Adaptive Traffic-aware Loose–tight Architecture System), a next-generation adaptive web architecture that dynamically adjusts its coupling strategy based on real-time system conditions. ATLAS employs machine learning models to analyse operational telemetry, predict traffic surges, detect anomalies, and forecast potential system failures. Using these predictions, the architecture can automatically transform its runtime structure, switching between tightly coupled monolithic execution and loosely coupled microservices deployment as traffic conditions evolve. To improve reliability, ATLAS incorporates a self-healing recovery pipeline that autonomously detects service failures, isolates faulty components, and restores normal operation without human intervention. Through case studies of large-scale platforms such as Google Search, Amazon, and Flipkart, we illustrate how existing systems can evolve toward the ATLAS paradigm, enabling self-adaptive and resilient web infrastructures for the next generation of large-scale online services.

Abstract: Environmental noise is increasingly recognized as a major environmental development challenge, with road traffic identified as the dominant source of acoustic pollution across Europe. Noise barriers are among the most widely implemented mitigation strategies. However, their spatial distribution and adequacy remain poorly documented, limiting their effectiveness for sustainable territorial planning. This study develops the first georeferenced database of highway noise barriers in Andalusia (Spain) and applies a reproducible, transdisciplinary geospatial workflow integrating field surveys, remote-sensing tools, and Geographic Information Systems (GIS). A total of 110 barriers were mapped, classified by material, geometry, and surrounding land use, and analyzed in relation to dwellings, schools, and hospitals. Results show that 1.6% of the Andalusian highway network is currently protected by barriers, with strong territorial disparities: over 50% of all structures are concentrated along coastal metropolitan corridors, while extensive inland areas remain unprotected. Misalignments were also detected between barrier placement and officially reported high-exposure segments, indicating limited correspondence between infrastructural deployment and acoustic priorities. Beyond generating a comprehensive regional dataset, the methodology provides a scalable basis for national and European initiatives seeking to harmonize the mapping and assessment of noise-mitigation infrastructures. By offering an open-access, transferable framework, this work supports policy professionals, environmental managers, and planners in evaluating mitigation gaps and informing more equitable and sustainable transportation and land-use strategies.

Abstract: Background: Atrial fibrillation (AF) frequently coexists with heart failure (HF) and worsens clinical outcomes. However, predictors of AF in HF with preserved (HFpEF) and mildly reduced ejection fraction (HFmrEF) remain poorly defined. This study aimed to identify clinical, laboratory, and echocardiographic predictors of AF in these HF phe-notypes. Methods: This retrospective single-center observational study included 700 consecutive patients with HF hospitalized between January 2018 and December 2023. The median age was 74 years (IQR 66–80). Women predominated in the cohort (55.3% vs. 44.7%, p < 0.001). Based on echocardiographically assessed left ventricular ejection fraction, patients were stratified into groups with preserved (≥50%), mildly reduced (41–49%) and reduced (≤40%) ejection fraction. Predictors of AF were evaluated using univariate and multivariate lo-gistic regression analyses, and model discrimination was assessed using ROC analysis. Results: Strongest predictors of AF in our patients with HFpEF and HFmrEF were left atrial size (OR 1.114 per mm increase; 95% CI 1.054–1.177; p < 0.001), moderate and severe tricuspid regurgitation (OR 4.092; 95% CI 1.977–8.466; p < 0.001 and OR 6.957; 95% CI 2.482–19.499; p < 0.001), male gender (OR 1.680; 95% CI 1.076–2.621; p = 0.022) and advanced age (OR 1.070 per year; 95% CI 1.032–1.109; p < 0.001). Conclusions: In patients with HFpEF and HFmrEF, AF is strongly associated with atrial remodeling, with left atrial enlargement as the key structural predictor. Identification of high-risk patients using clinical and echocardiographic parameters may facilitate earlier AF detection and improved risk stratification.

Abstract: Deep learning-based tumor segmentation has achieved strong performance on benchmark datasets, yet models often degrade when deployed in new hospitals. This decline is largely driven by domain shift, including differences in scanners, acquisition protocols, reconstruction settings, patient populations, and annotation styles. In high-stakes clinical workflows, such instability limits real adoption because a model that performs well in one center may fail silently in another. This paper presents a preprint-ready methodological framework for domain shift-robust segmentation in multi-hospital MRI and CT tumor imaging. The proposed design combines four complementary ingredients: strong segmentation backbones from the U-Net family, domain-generalization through intensity, style, and frequency-based augmentation, self-supervised pretraining on unlabeled multi-site data, and optional label-free test-time adaptation for target hospitals. The manuscript emphasizes a deployment-oriented evaluation protocol that prioritizes worst-site reliability, boundary safety, calibration, and failure analysis rather than average Dice alone. We describe an experimental plan with leave-one-hospital-out validation, targeted ablations, uncertainty analysis, and stress tests under artifact corruption. The expected pattern is that self-supervised pretraining and frequency-aware augmentation reduce the gap between in-domain and out-of-domain performance, improve worst-site Dice, and lower extreme boundary errors measured by Hausdorff distance. The central argument is that robustness should be treated as a first-class objective in medical image segmentation and that multi-center validation, transparent reporting, and clinically meaningful error analysis are necessary before deployment.

Abstract: Anthocyanins and betalains are hydrophilic plant pigments with numerous physiological and ecological functions. The biosynthesis routes of anthocyanins and betalains differ with anthocyanins being synthesized from phenylalanine via the general phenylpropanoid pathway, whereas betalains are derived from tyrosine. Although the precursors phenylalanine and tyrosine are present in all plants, there is no known plant where both these pigments are co-accumulated. Most plants synthesize anthocyanins, while certain families in the order Caryophyllales produce betalains. There is apparent mutual exclusion of these two plant pigments. Over the past five decades, evidence accumulated supporting this theory of mutual exclusion of the two pigments. However, recently published reports claim the presence of anthocyanins in well-known betalain-pigmented plants. Here, we explore the causes of such claims and provide recommendations for future studies on the topic.

Abstract: By using Abel's transformation we study the correlation of generalized divisor functions d_k(n) and obtain the correct main term order of the asymptotic estimate for the correlations.