Abstract: We develop a symmetry-based reconstruction of the vacuum impedance and the fine-structure constant. Hyperbolic geometry and discrete sectorization of the electromagnetic field plane are the only input assumptions. The construction identifies a unique integer-square hyperbolic selector that fixes the electric–magnetic partition without adjustable parameters. This yield the geometric part of the vacuum impedance when combined with the quantum scale $h/e^{2}$. The same discrete structure provides a normalization for the fine-structure constant through a universal sector angle $\pi/24$, connecting topological quantization phenomena in metals and alloys, including Berry phases, Zak phases, and quantized Hall responses. The resulting framework places electromagnetic constants within a unified geometric–topological setting and suggests experimentally accessible consequences in systems with discrete rotational or modular symmetry.

Abstract: This study examines how artificial intelligence (AI) contributes to contemporary processes of authenticity evaluation by functioning as a multimodal diagnostic cue in consumer decision-making. Drawing on survey data collected from 468 visitors at Terra Madre Salone del Gusto in Turin, Italy, the study tests a structural model comprising five latent constructs: Authenticity Trust, Perceived AI Usefulness and Diagnosticity, Multimodal Value, User Engagement, and Behavioural Intentions. The findings indicate that heritage-based and institutional authenticity cues remain foundational in consumers’ evaluations, but are increasingly interpreted and conditionally reinforced through interaction with AI-mediated information perceived as credible and diagnostically informative. Multimodal inputs—particularly the integration of textual, visual, and auditory narratives—are associated with richer authenticity perceptions and higher levels of user engagement. Experiential enjoyment during interaction with the AI system is positively related to intentions to adopt AI-supported evaluation tools, while behavioural intentions also encompass a willingness to pay a premium for products confirmed as authentic. Although the use of a convenience sample limits generalisability, the results highlight the broader potential of multimodal AI systems to reduce evaluative uncertainty and support trust formation in complex cultural and consumer environments. Conceptually, the study advances the notion of augmented authenticity, defined as a hybrid evaluative process in which tradition-based trust mechanisms are dynamically interpreted and reinforced through perceived AI diagnosticity and multimodal coherence. By situating AI within culturally embedded processes of meaning-making rather than purely instrumental evaluation, the findings contribute to interdisciplinary debates on technology-mediated trust, consumer judgement, and the societal implications of AI-assisted decision-making.

Abstract: The most severe premalignant lesion of glandular epithelium of the cervix is ade-nocarcinoma in situ (AIS). In most cases it is associated with persistent Human papillo-mavirus (HPV) infection and most often occurs in women in the fourth decade of life. In most high-income countries, primary screening has shifted to HPV testing, while cytology is used for patient triage. Even with current robust screening protocols, their sensitivity for glandular lesions remains limited. Diagnosis of AIS obtained by biopsy, brushing or curettage is confirmed by excisional methods and pathohistological verification. Therapy depends on the patient’s lifestyle and reproductive age. In our case, we present nulliparous patient with persistent ASC-US, HPV infection with alpha-7 types (without HPV 16 and 18 types), and AIS which was diagnosed after conization, follow up and two biopsies with curettage of cervical canal. Our case report highlights limitations in detection of glandular lesions and need for caution in patients with persistent and seemingly low-grade cytological abnormalities, notably in young patients with high-risk HPV types.

Abstract: This paper presents an Artificial Intelligence (AI) -driven framework designed to bridge the gap between raw user feedback and strategic decision-making. Moving beyond traditional sentiment analysis, which often overlooks the specific "why" behind visitor dissatisfaction, this research utilizes a sophisticated dual approach. By integrating the contextual precision of Bidirectional Encoder Representations from Transformers (BERT) with the generative reasoning of Large Language Models (LLMs) like Gemini, the system extracts fine-grained, aspect-based insights and actionable recommendations. The frame-work’s effectiveness is demonstrated through a case study of the Archaeological Site of Mystras. Ultimately, this work offers a scalable solution for tourism professionals and policymakers to listen more deeply to the authentic voice of the traveler.

Abstract: The concept of maintenance has undergone a significant evolution, adapting to the changing demands of industry over time. Initially limited to corrective actions during the Industrial Revolution—often performed without specialized personnel or dedicat-ed departments—modern maintenance now incorporates advanced design considera-tions such as reliability, maintainability, safety, sustainability, and performance. This research presents a novel methodology aimed at integrating maintainability into the early stages of equipment and system design. Centered on continuous improvement, the approach prioritizes design variables that facilitate efficient maintenance throughout the asset’s lifecycle. Grounded in the UNE 151001 standard and employing the Quality Function Deployment (QFD) technique, the proposed methodology intro-duces the “House of Maintainability”—a structured tool that supports maintainabil-ity-oriented design and allows for diagnostic assessments of existing systems. By cap-turing stakeholder requirements and maintenance experience across various systems and contexts, the tool systematically translates these inputs into design criteria, ensur-ing compliance with maintainability standards. The methodology is validated through a real-world case study, confirming its practical applicability and effectiveness in en-hancing industrial design processes with a focus on maintainability.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Abstract: The Apennine wolf (Canis lupus italicus) increasingly inhabits human-modified land-scapes, where exposure to anthropogenic environments may influence pathogen cir-culation and antimicrobial resistance (AMR). Despite this relevance, no shotgun met-agenomic data are available for this subspecies during rehabilitation. A juvenile male wolf admitted to a Wildlife Rehabilitation Center (WRC) after traumatic injury and treated with multiple antibiotics was sampled at admission (T0) and after 11 months of rehabilitation (T1). Shotgun metagenomic sequencing (Illumina NovaSeq) was used to characterize fecal microbial communities, potential pathogens, and antimicrobial resistance genes (ARGs) using Kraken2 and CARD-RGI. Bacterial diversity increased from T0 to T1. Microbial composition shifted from Enterobacterales-dominated pro-files to more diverse communities. Reads associated with animal and human patho-gens were detected at both time points, together with human-associated taxa and viral reads at T1. ARGs were abundant (444 at T0; 417 at T1), mainly involving efflux pumps and β-lactamases. Genes related to Highest Priority Critically Important Anti-microbials—including mcr variants, van clusters, and oxazolidinone resistance deter-minants—were identified. Shotgun metagenomics revealed marked microbiome changes and high ARG diversity in an Apennine wolf during rehabilitation. These findings highlight wolves as potential sentinels of environmental AMR and emphasize the importance of biosecurity measures in WRCs.

Abstract: Optimizing resource utilization and task execution within scalable cloud computing infrastructures remains a paramount challenge for service providers. This paper proposes and empirically evaluates a novel framework for intelligent resource orchestration, leveraging advanced learning algorithms to dynamically enhance performance. Our methodology integrates reinforcement learning principles to adaptively manage heterogeneous cloud resources, aiming to minimize task completion times and maximize system throughput. Through rigorous simulation experiments, this study demonstrates a significant improvement in resource allocation efficiency compared to conventional scheduling paradigms. The findings offer a strategic blueprint for developing autonomous and cost-effective cloud management systems, paving the way for next-generation adaptive cloud services.

Abstract: The study presents a novel process to design lightweight, high-performance cooling manifolds for power electronics using generative design (GD). The process begins with a baseline design that defines the constraints of the manifold with regard to the target cooling geometry and flow path. A GD flow optimization is then performed to minimize pressure drop and improve flow uniformity. Once a final fluid volume is obtained, a GD structural optimization is conducted to minimize weight and material usage. The final design demonstrated a 74.4% increase in heat transfer coefficient, an 87.3% improvement in uniformity, and a 63.3% reduction in weight.

Abstract: The objective of this work is to propose a simulation strategy for production planning that is compatible with the dynamism of natural gas processing, especially under open-market arrangement, in which several scheduling simulations must be performed within short time horizons. In such contexts, traditional first-principles-based ap-proaches, although accurate, require prohibitive computational times, motivating the need for an alternative simulation strategy. This work thus proposes a data-driven model built with the aid of machine learning and applied in a case study with historical data from the largest gas processing site in Brazil: Cabiúnas Petrobras asset. Main plant flowrates were selected: 18 targets and 44 input candidates – 1282 observations from three and a half years of operation. Principal Component Analysis was used for order reduction, keeping the 22 main principal components. A forward neural network (2 hidden layers and 225 neurons per layer) was built from training/test sets randomly selected and optimized hyperparameters – learning rate (0.001533) and batch size (8). Training converged in roughly 200 epochs (Adam optimizer), with early stop triggered by validation set. A mean absolute error of 0.0017 (test set) and R2=0.72 were found, a promising result considering plant complexity and data simplicity. Results showed particularly good fit for lighter products (sales gas, natural gas liquid), also indicating an opportunity for further work by including inputs related to liquid fractionation.

Abstract: The spontaneous emergence of macroscopic dissipative structures in systems driven by generalized chemical potentials is well-established in non-equilibrium thermodynamics. Some examples are, hurricanes, Bénard cells, reaction-diffusion patterns, and atmospheric/oceanic currents. Less recognized, however, are microscopic dissipative structures that form when the driving potential excites internal molecular degrees of freedom (electronic states and nuclear coordinates), typically via high-energy photons. The thermodynamic dissipation theory for the origin of life posits that the core biomolecules of all three domains of life originated as self-organized molecular dissipative structures—chromophores or pigments—that proliferated across the Archean ocean surface to absorb and dissipate the intense “soft” UV-C (205–280 nm) and UV-B (280–315 nm) solar flux into heat. Thermodynamic coupling to ancillary antenna and surface-anchoring molecules subsequently increased photon dissipation and enabled more complex dissipative processes, including modern photosynthesis, to dissipate lower-energy but higher-flux UV-A and visible light. Further thermodynamic coupling to abiotic geophysical cycles (e.g., diurnal, water cycles, winds, and ocean currents) ultimately produced today’s biosphere, efficiently dissipating the full incident solar spectrum well into the infrared. This paper details three examples of molecular dissipative structuring (nucleotides, fatty acids, pigments) and argues that dissipative structuring, rather than natural selection, is the fundamental creative force in biology at all levels of hierarchy.

Abstract: This paper presents a distributed AI training system that pools GPU high-bandwidth memory, host DRAM, and SSD into a coordinated parameter-serving hierarchy to support multiterabyte, sparsity-dominated deep models without sharing raw features across machines. The design shards and caches only the working parameters in GPU memory via multi-GPU hash tables, communicates intra-node over NVLink, and performs inter-node synchronization using RDMA-backed collective updates to preserve convergence under data parallelism. A four-stage pipeline overlaps network transfers, SSD I/O, CPU partitioning, and GPU compute while file-level compaction mitigates I/O amplification, yielding high throughput without inflating latency at scale. On industrial click-through-rate workloads with multi-terabyte embeddings, the system outperforms a large in-memory CPU cluster while maintaining production-grade accuracy, improving both training speed and price-performance for distributed AI. Overall, the architecture offers a pragmatic blueprint for scaling distributed learning through memory-hierarchy co-design and communication-aware parameter serving rather than brute-force cluster expansion.

Abstract: Amaranth (R2) is used as a color additive in cosmetics. In Korea, R2 is permitted only as a cosmetic colorant and is prohibited in products intended for infants and children under 13 years of age; in Europe, it is regulated solely as a cosmetic colorant rather than a hair dye ingredient. Despite its regulatory relevance, dermal absorption data for R2 are lacking. In this study, percutaneous absorption of R2 was evaluated using the Franz diffusion method with excised rat dorsal skin. Quantitative analysis of R2 was developed and validated using high-performance liquid chromatography (HPLC) in accordance with Korean Ministry of Food and Drug Safety guidelines, demonstrating acceptable linearity (r² = 0.9996–0.9999), accuracy (95.5–104.4%), and precision (0.3–5.8%). Two formulations (skin lotion and cream), each containing 1% R2, were applied at 113 mg/cm² for 24 h. Dermal absorption was assessed by analyzing receptor fluid, skin wash, stratum corneum, epidermis, and dermis. Total dermal absorption of R2 was 3.4 ± 2.7% for the lotion and 0% for the cream, corresponding to in vitro skin permeabilities of 34.5 ± 27.0 μg/cm² and 0 μg/cm², respectively. Total recovery ranged from 80.3 ± 8.2% to 91.4 ± 19.4%. These results provide essential data for cosmetic risk assessment of R2.

Abstract:

This study examines how different programming paradigms are associated with learning experiences and cognitive challenges as encountered by non-computer science novice learners. Using a case-study approach situated within specific instructional contexts, we integrate survey data from undergraduate students with large-scale public question-and-answer data from Stack Overflow to explore paradigm-related difficulty patterns. Four instructional contexts—C, Java, Python, and Prolog—were examined as pedagogical instantiations of imperative, object-oriented, functional-style, and logic-based paradigms using text clustering, word embedding models, and interaction-informed complexity metrics. The analysis identifies distinct patterns of learning challenges across paradigmatic contexts, including difficulties related to low-level memory management in C-based instruction, abstraction and design reasoning in object-oriented contexts, inference-driven reasoning in Prolog-based instruction, and recursion-related challenges in functional-style programming tasks. Survey responses exhibit tendencies that are broadly consistent with patterns observed in public Q&A data, supporting the use of large-scale community-generated content as a complementary source for learner-centered educational analysis. Based on these findings, the study discusses paradigm-aware instructional implications for programming education tailored to non-major learners within comparable educational settings. The results provide empirical support for differentiated instructional approaches and offer evidence-informed insights relevant to curriculum-oriented teaching and future research on adaptive learning systems.

Abstract: As the reliability and validity of forensic evidence, particularly in feature comparison disciplines, confront on-going scrutiny, forensic practitioners must ensure their processes, whether for investigative, intelligence or evidential purposes are robust, scientifically grounded, and validated. In forensic facial identification, morphological analysis is internationally recognized as the preferred method for facial image comparison, and is applied during the analysis and comparison steps of the Analysis, Comparison, Evaluation, Verification (ACE-V) process, commonly applied in feature comparison. While several international proficiency tests have assessed forensic facial examiners’ accuracy in comparing mated and non-mated pairs (black box tests), fewer opportunities have focused on evaluating inter-laboratory procedures and methods. To address this gap, members of a small border and immigration focused expert working group participated in an inter-laboratory collaborative exercise designed to analyse and harmonize best practices across member laboratories. There are limited published validation studies of facial image comparison methods. This paper presents the results of a collaborative exercise that compares the methodologies of three different agencies, highlighting key similarities and differences in examiner process and decision making, and provides a foundation for the development of similar future initiatives.

Abstract:

Knowledge about the germination potential of Mandacaru seeds is fundamental for maintaining breeding programs and germplasm banks. Thus, we aimed to study the germination of stored and freshly harvested mandacaru seeds in order to investigate seed viability as a function of storage imposition, in addition to characterizing seed anatomy and conducting biochemical evaluation. Germination tests were conducted in a completely randomized design in a 2×6 factorial scheme, with two storage conditions and six temperatures (15, 20, 25, 30, 35, and 40°C), with 4 replications of 25 seeds each. Anatomical evaluation tests and biochemical tests had 5 and 10 replications for each storage condition, respectively. It is concluded that the range of 25-35°C is ideal for germination of C. jamacaru seeds, and temperatures below 20°C and above 35°C are detrimental to germination. X-ray computed microtomography was efficient for characterizing seed anatomy and differentiating their tissues, allowing accurate and clear evaluation of their internal structures, and proper storage was efficient in minimizing the deleterious effects of H₂O₂ and MDA accumulation.

Abstract: Trypanosoma vivax is a hemoparasite of major veterinary importance, causing trypanoso-miasis in domestic and wild ruminants. While cattle are widely recognized as susceptible hosts, water buffaloes are increasingly reported to develop acute, subacute, and chronic infections with severe health and production impacts. This review critically evaluates the current knowledge on T. vivax in buffaloes, focusing on pathogenesis, epidemiology, clin-ical manifestations, diagnostic approaches, therapeutic challenges, and control strategies. Data from Africa and South America are synthesized, with particular emphasis on out-breaks in the Amazon Biome, especially Marajó Island (Brazil), where buffalo farming represents a key economic activity. Advances in molecular diagnosis, such as PCR-based methods, are compared with traditional parasitological and serological tools, and their applicability in field conditions is discussed. Current chemotherapeutic options, emerging reports of drug resistance, and perspectives for vaccine development are examined. In ad-dition, integrated control strategies considering mechanical vectors, iatrogenic transmis-sion, and biosecurity practices are highlighted. This review identifies critical gaps in re-search and provides practical recommendations for surveillance and disease manage-ment in buffalo herds. The information presented here aims to support veterinarians, re-searchers, and policymakers in designing sustainable strategies to mitigate the impact of T. vivax in tropical livestock systems.

Abstract: Background: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disorder globally. Mazdutide has shown clinical benefits in weight management and metabolic regulation, indicating its potential as a therapeutic agent for NAFLD. This study aimed to investigate the efficacy and mechanism of action of Mazdutide against early-stage NAFLD. Methods: A NAFLD mouse model was induced by a 12-week high-fat diet, followed by a 4-week treatment with subcutaneous Mazdutide (100, 200, or 400 μg/kg). In vitro, a cellular NAFLD model was established by treating hepatocytes with 1 mM free fatty acids for 24 h, followed by co-treatment with Mazdutide (10, 20, or 50 nM) or the endoplasmic reticulum (ER) stress inhibitor 4-phenylbutyric acid (4-PBA). Serum and hepatic lipid profiles, liver injury markers, and pro-inflammatory cytokines were quantified. Liver histopathology was assessed by hematoxylin and eosin and Oil Red O staining. Protein expression related to ER stress, inflammation, and lipid metabolism was analyzed by immunohistochemistry and Western blot. Results: Mazdutide treatment significantly ameliorated systemic and hepatic lipid metabolism disorders, reduced liver injury markers and hepatic steatosis, and mitigated inflammation and oxidative stress in NAFLD mice and hepatocytes. Mechanistically, Mazdutide alleviated ER stress by modulating the PERK-eIF2α-ATF4-CHOP pathway, suppressed the NF-κB-mediated inflammatory response, and downregulated key lipogenic regulators, including SREBP-1, C/EBPβ, and PPARγ. Conclusion: Our findings demonstrate that Mazdutide alleviates hepatic ER stress in NAFLD, leading to suppressed inflammatory responses and improved lipid metabolism, which ultimately attenuates disease progression.

Abstract: Gene expression is inherently stochastic, and promoter switching–induced transcriptional bursting generates substantial cell-to-cell variability in mRNA abundance. Such variability is commonly characterized by the mean and variance; however, these low-order statistics fail to capture the geometric features of mRNA copy number distributions and may obscure mechanistic differences in promoter dynamics. In this work, we analyze a two-state stochastic gene transcription model and derive explicit analytical expressions for higher-order moments of mRNA abundance. We show that skewness and kurtosis provide mechanistically informative signatures of transcriptional bursting, explicitly depending on promoter switching kinetics and burst size. In particular, positive skewness increases with slower promoter switching and larger burst sizes, even when the mean expression level is fixed, while elevated kurtosis distinguishes burst-dominated, low-expression regimes from Gaussian-like high-expression regimes. Our results demonstrate that distinct promoter dynamics can produce identical mean expression levels and variances while exhibiting markedly different skewness and kurtosis. Incorporating higher-order statistics, therefore, extends conventional mean–variance analyses and enables improved discrimination between competing stochastic gene expression mechanisms in single-cell data.

Abstract: The global health crisis driven by Antimicrobial Resistance (AMR) necessitates an urgent pivot toward novel therapeutic agents, with traditional medicinal plants serving as a critical resource. The Asteraceae genus Verbesina, particularly utilized in Mexican ethnobotany, has garnered scientific attention due to its potent bioactive profile against infection and inflammation. This review provides a comprehensive and critical synthesis of the pharmacological landscape of Verbesina species, focusing specifically on the dual role of its major secondary metabolites, the Sesquiterpene Lactones (SLs), as both cytotoxic and antimicrobial agents. We systematically compile and analyze reported in vitro data, including IC50 values from cancer and non-cancerous cell lines, and MIC values against clinically relevant drug-resistant strains like S. aureus and E. coli. A core focus is placed on establishing the therapeutic index (SI = IC50/MIC) for lead compounds, providing a crucial indicator of drug feasibility. Furthermore, we review the proposed molecular mechanisms of SL action, such as the crucial role of the α-methylene-γ-lactone moiety in alkylating cellular targets, which underpins both their antiproliferative and bactericidal effects. By critically bridging ethnopharmacology with modern mechanistic data, this review validates the translational potential of Verbesina metabolites and highlights clear directions for bioassay-guided isolation and optimization as next-generation anti-resistance scaffolds.