Abstract: Semantic interoperability remains a critical challenge in Spatial Data Infrastructures (SDIs), particularly when aligning authoritative taxonomies with collaborative folksonomies. Recent advances in Large Language Models (LLMs) offer new avenues for automated semantic interpretation, yet these 'sub-symbolic' approaches often lack the logical rigor required for structured geospatial data. This paper evaluates the capability of LLMs – specifically distinguishing between traditional architectures and emerging Large Reasoning Models (LRMs) – to perform semantic alignment between the Brazilian national cartographic standard (EDGV) and OpenStreetMap (OSM). Using a formal ontology as a prompting scaffold, we tested seven model versions (including ChatGPT 5.0, DeepSeek R1, and Gemini 2.5) on their ability to identify semantic equivalents and generate valid ontological mappings. Results indicate that while traditional LLMs struggle with hierarchical structures, reasoning-oriented models demonstrate significantly improved capacity for complex inference, correctly identifying many-to-one (n:1) relationships across linguistic barriers. However, all models exhibited limitations in generating syntactically valid OWL code, revealing a gap between semantic comprehension and formal structuring. We conclude that a neuro-symbolic approach, using ontologies to ground AI reasoning, provides a viable pathway for semi-automated interoperability, although future work must address the lack of explicit spatial reasoning in current architectures.

Abstract: Antarctic ice core data reveal a consistent pattern across glacial cycles: atmospheric CO₂ does not immediately track temperature decline as interglacial conditions give way to glaciation. The most dramatic example occurs during the Last Interglacial (Eemian, MIS 5e), where CO₂ remained essentially constant at 275–280 ppm for approximately 13,000 years while temperature fell 7°C. This paper examines whether similar behavior can be detected during cooling from earlier interglacials. Using harmonic fits to temperature and CO₂ data spanning 350,000 years, phase plots are constructed of CO₂ versus temperature that isolate the warming and cooling branches of each glacial cycle. The analysis reveals that the Eemian is the clearest but not unique example: MIS 9 shows comparable behavior, while the MIS 7 complex presents an instructive exception that may reflect extreme orbital forcing conditions. The asymmetry between rapid CO₂ release during warming and slow CO₂ absorption during cooling suggests rate-limited processes govern the return of atmospheric carbon to oceanic and terrestrial reservoirs. These observations are inconsistent with CO₂ acting as the primary driver of temperature change on glacial-interglacial timescales.

Abstract: Porcine epidemic diarrhea virus (PEDV), particularly the emerging GII genotype, poses a severe threat to the global swine industry. Current vaccines based on classical strains often provide limited cross-protection against these heterogeneous variants. In this study, a novel PEDV GIIc strain, designated as PEDV-HeN2024, was successfully isolated and identified through cell culture, immunofluorescence assay (IFA), genetic sequencing, and phylogenetic analysis. Animal challenge studies demonstrated that this isolation exhibited high pathogenicity, causing severe diarrhea in both 3-5-day-old piglets. Furthermore, an inactivated vaccine was developed by emulsifying the purified virus with ISA 201 VG adjuvant (1:1, v/v). In contrast to the commercial vaccine, the PEDV-HeN2024 inactivated vaccine induced significantly higher titers of neutralizing antibodies and virus-specific total immunoglobulins in immunized animals. Crucially, these antibodies elicited broad cross-neutralizing activity against homologous GIIc, as well as heterologous GIIa and GIIb strains in vitro and in vivo. Our findings indicate that the inactivated vaccine candidate developed from the emerging PEDV-GIIc variant is a promising broad-spectrum vaccine for controlling the prevalent PEDV strains.

Abstract: Traditional log alerting systems suffer from high false positive rates and delayed anomaly diagnosis. This paper proposes an intelligent log analysis framework integrating self-supervised temporal modeling with vectorized semantic retrieval. The system constructs a log collection pipeline using the ELK Stack, employs BERT-derived models for semantic encoding of log fragments, and utilizes a Temporal Contrastive Learning module to capture cross-temporal anomaly patterns. By integrating Cluster-based Outlier Detection and an Attention-based visualization mechanism, it enables interpretable diagnosis of complex system behaviors. Experiments conducted on a production dataset of 120 million logs achieved a 14.7% improvement in F1 score, reduced detection latency by 48%, and attained an average alert accuracy of 92.3%. This framework significantly enhances the intelligent operations and maintenance capabilities of full-stack systems in AIOps environments.

Abstract: Astigmatism, a common refractive error characterized by an irregular corneal or lenticular curvature, represents a significant pediatric public health concern with profound implications for visual development and long-term ocular health. This review synthesizes contemporary evidence on the complex, multifactorial etiology of astigmatism, emphasizing its critical and synergistic relationship with myopia progression. We delineate the substantial genetic component, with heritability estimates of 40-60%, involving polygenic inheritance patterns and specific SNPs in loci such as PDGFRA and CTNNA2. The pathophysiology is further explored through physiological triggers, including extraocular muscle imbalance, dynamic eyelid pressure, and corneal biomechanical weakening. Modern environmental accelerants, notably prolonged near work and digital device usage, are examined for their role in disrupting emmetropization. The core thesis of this manuscript advocates for a paradigm shift towards early infantile screening and personalized, multi-modal intervention strategies. We critically evaluate conventional therapies (spectacles, toric contact lenses, orthokeratology), emerging pharmacological agents (low-dose atropine), and evidence-based lifestyle modifications (increased outdoor exposure, nutritional optimization). Furthermore, we explore the integration of advanced diagnostics (anterior-segment OCT, Scheimpflug tomography, genetic risk profiling) and complementary approaches like Nutritional optimization and traditional medicine systems, such as Ayurvedic Netra Tarpana and yogic eye exercises, within a holistic management framework. The convergence of genetic insights, advanced biometry, and personalized medicine heralds a new era in preventing astigmatism-related amblyopia and mitigating its role in axial elongation, ultimately preserving lifelong visual function.

Abstract: Transcriptomic analyses of public datasets (TCGA and GTEx) revealed that both CD74 and Cathepsin L (CTSL) are significantly overexpressed in diffuse large B-cell lymphoma (DLBCL) compared to normal tissues, and their expression levels are highly correlated (Spearman R = 0.64, p = 3×1046). Kaplan–Meier analysis showed that elevated expression of both genes is associated with reduced disease-free survival (DFS), defining a high-risk CD74+/CTSL+ DLBCL subgroup. This is the first study demonstrating coordinated overexpression of CD74 and CTSL, and proposing their dual targeting via antibody–drug conjugates (ADCs) to improve outcomes in relapsed or refractory DLBCL. Cysteine cathepsins, a family of proteases, are upregulated in many cancers, facilitating tumor invasion and metastasis. Cathepsins are overexpressed and play key roles in DLBCL progression. GB111-NH₂, a potent broad-spectrum cathepsin inhibitor, significantly reduced cathepsin activity in lymphoma cell lines and patient samples. GB111-NH₂ treatment increased apoptosis and caspase-3 activation in DLBCL patient cells and chronic lymphocytic leukemia (CLL) mononuclear cells. Here, we developed a modified cathepsin inhibitor, M-GB, containing a maleimide linker for site-specific antibody conjugation. While M-GB alone has poor cell permeability, when conjugated to an antibody, it forms an ADC (M-GB-ADC) that selectively induces lymphoma cell death. M-GB-ADC demonstrated high specificity for CD74-expressing lymphoma cells while exhibiting minimal toxicity to non-target cells. Our findings highlight the potential of M-GB–ADC as a targeted therapy for overcoming rituximab resistance and treatment failure in DLBCL. This strategy enhances therapeutic efficacy and provides a treatment option by directing a cathepsin inhibitor payload specifically to malignant B cells.

Abstract: Background/Objectives: In recent years, increasing attention has been directed toward the human immune system and strategies to enhance its function. Whole-body cryother-apy (WBC), a short-term therapeutic application of extreme cold of about minus 90 de-grees, seems to show a positive influence on the immune system, physical pain, body composition and other human regulatory systems. Methods: In this one- armed prospec-tive monocentric observational study 20 adult participants underwent 18 sessions of cry-otherapy over 9 weeks (–90°C, 3 – 6 minutes each), followed by a 9-week post-intervention phase. Results: In many parameters of bioimpedance analysis, blood parameters and subjective perception of stress, statistically significant improvement could be found, especially directly after intervention phase. Some improvements persist till end of study time frame. Conclusions: The results of this pilot study underscore the impact of cryotherapy on pathways related to immune function and metabolic regulation. The results will pave the way for further randomized controlled trials that study and confirm the efficacy of WBC.

Abstract: Artificial intelligence (AI) platforms in East Asia often elicit privacy concern yet sustain user participation. This study interprets the pattern as bounded compliance—a satisficing equilibrium in which engagement persists once minimum transparency and reliability thresholds are perceived in platform governance. A symmetric adult survey in Fujian, China (N = 185) and Busan–Gyeongnam, Korea (N = 187) examines how accountability visibility and privacy concern jointly shape platform trust and use. Heat-map diagnostics and logit marginal effects show consistently high willingness (≥0.70) across conditions, with stronger accountability sensitivity in Korea and stronger continuity assurance in China. Under high concern, willingness converges to a “good-enough” zone where participation endures despite discomfort. The findings highlight governance thresholds as practical levers for trustworthy AI: enhancing feedback visibility (e.g., case tracking, resolution proofs) and maintaining institutional continuity (e.g., O&M capacity, incident-response coverage) can sustain public confidence in AI-enabled public-service platforms.

Abstract: Two-phase ejectors are a promising alternative for improving the performance of direct expansion vapor compression refrigeration systems, especially in transcritical applica-tions. Extensive literature has been produced on modeling, simulations and experiments related to two-phase ejectors. In particular, 0D models have proven to offer a trade-off be-tween simplicity and precision. In these models, there remains significant uncertainty re-garding the estimation of the two-phase speed of sound and the choking conditions at the primary nozzle throat. These choking conditions have a considerable impact on the throat geometry. This study proposes a novel approach that relies solely on conservation equa-tions (mass and energy) to determine the thermodynamic conditions at the throat for de-sign purposes. The results of the proposed approach and 13 other approaches reported in the literature were compared with published experiments data regarding the throat diam-eter and pressure. The proposed approach showed robust when validated against three experimental cases, predicting the throat diameter and pressure of a primary convergent–divergent nozzle with deviations of -8 % and +15 %, while the other approaches exhibited larger deviations of -12 % to +574% and -73 % to +21 %, respectively. Moreover, the pro-posed approach reliably generates a convergent-divergent nozzle configuration across a wide range of operating conditions, including variations in primary and secondary pres-sures as well as variations in primary nozzle efficiency using R1234yf and CO2 as work-ing fluids.

Abstract: This review explores the link between engineering and creativity, challenging the perception gap between structured training and creative fields. It reframes human creativity insights from prominent scholars to inform the development of AI systems capable of creative problem-solving. The paper translates abstract and philosophical models into structured, computationally tractable frameworks to bridge human creativity research and machine learning applications. The review focuses on four core frameworks to guide AI design: Wallas’s Four-Stage Process, Rhodes’ Four Ps Model, Simonton’s Creativity-as-Influence Model, and Runco’s prevailing framework. It traces the historical progression of creativity research from early efforts by Guilford and Torrance to later dynamic frameworks by Amabile and Csikszentmihalyi. The document discusses how these models, which evolved from abstract theorizing to structured, multidimensional constructs, provide a foundation for examining and applying creativity within technical domains. It also addresses the growing integration of AI, distinguishing between human creativity and artificial creativity produced by machines. The forward-looking perspective suggests an augmentative role for AI within hybrid human-AI workflows. Ultimately, the review aims to provide a blueprint for developing AI systems that move beyond rote problem-solving to exhibit adaptive, context-sensitive, and generative capabilities, capitalizing on the synergy between creativity science and AI.

Abstract: This paper introduces the Operational Coherence Framework (OCOF) v1.3, a formal architecture specifying the structural prerequisites for semantic interpretation in intelligent systems. The framework defines interpretive intelligence not through scale or behavioral sophistication, but through five independent operational axioms: Boundary Integrity, Precision Structuring, Semantic Valuation, Policy Alignment, and Global State Continuity. Each axiom imposes a distinct informational constraint, and their joint satisfaction delineates the operational envelope within which internal states can support meaningful structure. Rather than adopting emergent or capacity-based accounts of meaning, OCOF characterizes meaning as a condition of structural readiness—a phase transition that occurs only when boundary stability, signal reliability, valuation structure, action coherence, and temporal continuity collectively reach their coherence thresholds. The framework situates mechanisms from the Free Energy Principle, Predictive Processing, Integrated Information Theory, and Control Theory within this unified constraint architecture, showing that these models operate as specialized components presupposing the structural conditions defined by OCOF. A central contribution of this work is the operational definition of Meaning-Readiness, the point at which a system’s boundary integrity and precision structure allow the reliable attribution of semantic relevance beyond syntactic or associative processing. We demonstrate the logical independence and non-circularity of the five axioms, establishing OCOF as a self-contained and falsifiable theoretical kernel. As a result, OCOF v1.3 provides a substrate-neutral foundation for evaluating interpretive capacity in biological, artificial, and hybrid systems, offering a principled basis for cognitive modeling and AGI alignment.

Abstract: The growing complexity of BIM (Building Information Model) models leads to perfor-mance issues, extended file loading times, and difficulties in cross-industry coordina-tion. One of the main factors reducing performance are so-called "heavy" library com-ponents (families in Revit), characterized by excessive geometric complexity, a large number of instances, or improper optimization. Currently, the identification of such components is based mainly on the experience of designers and manual inspection of models, which is time-consuming and prone to errors. This article presents a new tool, HeavyFamilies, which automates the detection and analysis of heavy library compo-nents in BIM models. The tool uses a multi-criteria analysis method, evaluating com-ponents based on five key parameters: number of instances, geometry complexity, number of walls and edges, and estimated file size. Each parameter is weighed ac-cording to its impact on model performance. The developed solution has been imple-mented as a pyRevit plugin for Autodesk Revit, offering a graphical interface with a tabular summary of results, a CSV export function, and visualization of detected components directly in the model. Validation of the tool on real BIM projects has demonstrated its effectiveness in identifying components with a weight index exceed-ing the threshold of 200, allowing designers to prioritize optimization efforts. The HeavyFamilies tool is a practical contribution to the field of BIM model optimization, enabling a systematic approach to managing model performance in complex construc-tion projects and supporting the development of smart cities.

Abstract: Automation of the plant-based meat extrusion process requires a scheme to provide quantitative estimates of output fibrosity, which must be carried out online and in real-time. A novel machine learning regression model for this purpose, is proposed in this article. A deep neural network that was originally trained for image classification, was extended to provide quantitative fibrosity estimates. Relevant layers of the network were retrained using real-world laboratory data. Plant-based meat or textured vegetable protein products with varying fibrous microstructures were obtained using different ingredient formulations and process conditions on a pilot-scale twin screw extruder with in-barrel moisture range of 29.2-40.9% (wet basis). Images of extruded plant-based meat products were collected to serve as sample inputs. An experiment was devised, where image samples were randomly presented to two expert human subjects, who provided as feedback, fibrosity scores lying within the interval [1, 10]. Statistical metrics were adopted to evaluate the performance of the trained network. It was found that the network performed significantly better when trained separately with feedback scores of each individual subject, than with the combined scores, indicating that it was able to capture nuances of a subject’s perception. Another study was directed at the explainability of the network’s estimations. Using standard software, a set of synthetic images of varying shapes and sizes were created as inputs to the network. Interpretations of its output scores indicate that the network’s estimates were based on features relevant to porosity and fibrosity, while not influenced by extraneous ones.

Abstract: Knowledge of the maximum gust expected over a period of years is essential for off-shore structures design. Because long records of gust speed are not normally availa-ble, maximum gusts have traditionally been estimated by multiplying the maximum expected hourly or 10-minute wind speed by a gust factor. That calculation ignores the possibility that the highest gust might not occur in the hour with the highest mean wind speed. A similar problem arises in the estimation of the maximum expected in-dividual wave height. By analogy with the accepted method of calculating maximum wave heights, we demonstrate how maximum gusts can be calculated from time series of average wind speed and wind gust distributions. We used measurements from the IJmuiden meteorological mast offshore The Netherlands to find wind gust distribu-tions. The IJmuiden data is particularly useful for studying gusts because four years of measurements were made at a sampling frequency of 4 Hz. Those distributions were used to predict extreme values of gusts in a storm using methods similar to those used in wave height calculations. The resulting extreme values closely matched ex-treme values calculated directly from the measured maximum gusts in each storm. The methods described here can calculate extreme gust speeds more accurately than the methods currently in use.

Abstract: Post-disaster reconstruction remains largely excluded from circular-economy ap-proaches. This gap is particularly evident in earthquake-affected inner territories, where reconstruction faces severe logistical constraints—especially rubble manage-ment—and where debris is often composed of materials closely tied to local building cultures and community identities. In these contexts, rebuilding still follows linear, emergency-driven models that treat rubble primarily as waste. This study introduces Rubble as a Material Bank (RMB), a digital–material framework that reconceptualises earthquake rubble as a traceable and programmable resource for circular reconstruc-tion. RMB defines a rubble-to-component chain integrating material characterisation, data-driven management, robotic fabrication, and reversible architectural design. Se-lected downstream segments are experimentally validated through the TRAP project, developed within the European TARGET-X program. The experimentation focuses on extrusion-based fabrication of dry-assembled wall components using rubble-derived aggregates. Results show that digitally governed workflows can enable material reuse while revealing technical and regulatory constraints on large-scale implementation.

Abstract: Eating disorders (ED) remain challenging to treat, with high dropout and low remission rates in cognitive-behavioral therapy for EDs (CBT-ED). Psilocybin treatment (PT) demonstrates therapeutic potential to enhance CBT-ED by exerting several neurobiological, psychological, and experiential effects (e.g., antidepressant, neuroplasticity, emotional openness) that are hypothesized to increase psychotherapeutic engagement, reduce dropout, and improve clinical outcomes. This article provides the first consolidation of existing theoretical evidence for PT/CBT-ED, proposes considerations for a con-current intervention protocol, and presents clinical and research considerations to empirically test its feasibility, safety, and efficacy. This line of inquiry is expected to advance the development of approaches that improve ED treatment outcomes and, more broadly, advance the study of psychedelics as tools to enhance evidence-based psychotherapy models.

Abstract: Amino acid derivatives, such as β-keto esters and pyrrolones, were used as nucleophiles in organocatalyzed Michael additions to nitroalkene acceptors, while fatty acid derivatives acted as both nucleophiles (β-keto esters) and electrophile (nitroalkene acceptor). Bifunctional noncovalent organocatalysts were employed as asymmetric organocatalysts. Twenty compounds – including fatty acid and amino acid derivatives, as well as fatty acid–amino acid conjugates – were prepared with enantioselectivities of up to 98% ee. All novel products were fully characterized. This research demonstrates the ease of assembling readily available fatty acid and amino acid building blocks under ambient conditions.

Abstract: Zika virus (ZIKV), a mosquito-borne flavivirus, is associated with congenital malformations and neuroinflammatory disorders, highlighting the need to identify host factors that shape infection outcomes. Macrophages, key targets and reservoirs of ZIKV, orchestrate both antiviral and inflammatory responses. Vitamin D (VitD) has emerged as a potent immunomodulator that enhances macrophage antimicrobial activity and regulates inflammation. To investigate how VitD shapes macrophage responses to ZIKV, we reanalyzed publicly available RNA-seq and miRNA-seq datasets from monocyte-derived mac-rophages (MDMs) of four donors, differentiated with or without VitD and subsequently infected with ZIKV. Differential expression analysis identified long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs, integrated into competing endogenous RNA (ceRNA) networks. In VitD-conditioned and ZIKV-infected MDMs, 65 lncRNAs and 23 miRNAs were significantly modulated. Notably, lncRNAs such as HSD11B1-AS1, Lnc-FOSL2, SPIRE-AS1, and PCAT7 were predicted to regulate immune and metabolic genes, including G0S2, FOSL2, PRELID3A, and FBP1. Among the miRNAs, let-7a and miR-494 were downregulated, while miR-146a, miR-708, and miR-378 were upregulated, all of which have been previously implicated in antiviral immunity. Functional enrichment analysis revealed pathways linked to metabolism, stress responses, and cell migration. ceRNA network analysis suggested that SOX2-OT and SLC9A3-AS1 may act as molecular sponges, modulating regulatory axes relevant to immune control and viral response. Despite limitations in sample size and experimental validation, this study provides an exploratory map of ncRNA–mRNA networks shaped by VitD during ZIKV infection, highlighting candidate molecules and pathways for further studies on host–virus interactions and VitD-mediated immune regulation.

Abstract: Background/Objectives: Diagnosing Rotator Cuff Tears (RCTs) via Magnetic Resonance Imaging (MRI) is clinically challenging due to complex 3D anatomy and significant in-terobserver variability. Traditional slice-centric Convolutional Neural Networks (CNNs) often fail to capture the necessary volumetric context for accurate grading. This study aims to develop and validate the Patient-Aware Vision Transformer (Pa-ViT), an explainable deep learning framework designed for the automated, patient-level classification of RCTs (Normal, Partial-Thickness, and Full-Thickness). Methods: A large-scale retrospective dataset comprising 2,447 T2-weighted coronal shoulder MRI examinations was utilized. The proposed Pa-ViT framework employs a Vision Transformer (ViT-Base) backbone within a Weakly-Supervised Multiple Instance Learning (MIL) paradigm to aggregate slice-level semantic features into a unified patient diagnosis. The model was trained using a weighted cross-entropy loss to address class imbalance and was benchmarked against widely used CNN architectures and traditional machine learning classifiers. Results: The Pa-ViT model achieved a high overall accuracy of 91% and a macro-averaged F1-score of 0.91, significantly outperforming the standard VGG-16 baseline (87%). Notably, the model demonstrated superior discriminative power for the challenging Partial-Thickness Tear class (ROC AUC: 0.903). Furthermore, Attention Rollout visualizations confirmed the model’s reliance on genuine anatomical features, such as the supraspinatus footprint, rather than artifacts. Conclusions: By effectively modeling long-range dependencies, the Pa-ViT framework provides a robust alternative to traditional CNNs. It offers a clinically viable, explainable decision support tool that enhances diagnostic sensitivity, particularly for subtle partial-thickness tears.

Abstract: Lipid nanoparticles (LNPs) are a critical structural element of modern mRNA therapeutics, including COVID‑19 modRNA vaccines. Each formulation is a multicomponent system in which the LNP serves not as a passive carrier but as an active, biointeractive entity whose ionizable lipids engage directly with cellular membranes. Current evidence from cellular, transcriptomic, and proteomic analyses indicates that LNPs, with or without active mRNA cargo, alter transcriptomic programs and protein expression. This suggests that, even during uptake and interaction with the membrane (transfection), the membrane serves as an initial site for inflammatory, detoxifying, and stress responses. Simultaneously, pathways involved in fat metabolism and detoxification are affected, such as the peroxisome proliferator-activated receptor γ (PPARγ) and cytochrome P450 (CYP) enzyme systems. We believe that the phosphatidylinositol (PI) cycle is the initial point for these disorders. This cycle regulates both organelle trafficking and membrane restructuring following endocytic processes, including macropinocytosis. When this cycle is disrupted, membrane restructuring and organelle dysfunction occur, triggering downstream signaling cascades such as nuclear factor kappa-B (NF- κB), mitogen-activated protein kinases (MAPKs), Janus kinase–signal transducer (JAK-STAT) pathways, and mechanistic target of rapamycin (mTOR) complexes. Transfection with LNPs may induce a systemic condition we call lipid-nanoparticle-driven membrane dysfunction (L‑DMD), where transfection results in broader dysregulation of cellular communication, stress response, and energy balance. This hypothesis-driven review offers a mechanistic foundation for understanding the diffuse, often enduring, biological effects observed after exposure to messenger RNA LNP formulations. It highlights a needed perspective at the intracellular level and within systems biology.