Search Results (1,116)

This study aims to identify and synthesize the major global trends that shaped 2025 by applying the DeflyCompass methodology to a curated corpus of strategic foresight reports. The study synthesizes insights from 23 strategic reports published by leading international organizations, including the World Economic Forum, Accenture, Euromonitor, and major technology firms. Methodologically, DeflyCompass operationalizes a structured hybrid human–AI pipeline comprising the deployment of multi-agent AI systems, automated knowledge graph construction, semantic clustering, and hybrid human–AI validation processes, reducing an initial set of 816 preliminary signals to a validated catalog of 50 high-priority trends across six PESTEL domains: Political, Economic, Social, Technological, Environmental, and Legal/Governance. Key findings indicate that artificial intelligence functions as a systemic enabling technology across all domains, climate and sustainability imperatives permeate multiple domains, geopolitical fragmentation introduces systemic tension, and trust deficits emerge as a critical vulnerability. The study contributes a replicable and scalable framework for global-level strategic foresight that operationalizes human–AI integration within a rigorous expert-driven validation process, complementing existing hybrid analytical approaches in the literature. Implications extend to decision-making in technology governance, sustainability strategy, social adaptation, and scenario planning, highlighting the necessity of integrating AI augmentation with human expertise for effective future-oriented planning. Full article

Environmental pollutants, lifestyle factors, and intrinsic metabolism can amplify reactive oxygen and nitrogen species (ROS/RNS) generation beyond antioxidant capacity. The resulting oxidative stress damages macromolecules, perturbs redox signaling, and may accelerate biological aging. This review synthesizes evidence published mainly in 2020–2025 on how major pollutant classes (air pollutants, metals, pesticides, nanoparticles, and micro-/nanoplastics) induce ROS through shared nodes mitochondrial electron transport disruption, NADPH oxidase activation, and redox cycling/Fenton chemistry and how these signals propagate to epigenetic remodeling (DNA methylation, histone modifications, and non-coding RNAs). To move beyond descriptive cataloging, we grade the strength of evidence by study context (cell culture, animal models, human observational studies, and clinically oriented biomarker research), highlight convergent findings and unresolved controversies, and specify key methodological limits. We then compare oxidative-stress biomarker platforms by analytical specificity, pre-analytical susceptibility, and translational readiness, distinguishing validated markers from exploratory redox-epigenetic and multi-omics signatures. Finally, we discuss how exposomics and AI-assisted multi-omics integration may support biomarker discovery while emphasizing current constraints (confounding, batch effects, and limited prospective validation) that must be addressed for clinical translation. Full article

Background/Objectives: Autophagy plays a role in systemic lupus erythematosus (SLE) pathogenesis. Nevertheless, the specific genetic determinants underpinning this process remain poorly characterized. Summary data-based Mendelian randomization (SMR) analysis was therefore utilized to pinpoint autophagy-related genes associated with SLE risk. Methods: We analyzed 700 autophagy-related genes, integrating methylation quantitative trait loci (mQTL), expression QTL (eQTL) from blood and relevant tissue, and protein QTL (pQTL) data with genome-wide association studies (GWAS) data on SLE from the IEU dataset (discovery). GWAS data from FinnGen and the GWAS Catalog were used as replication datasets. Colocalization analysis identified shared genetic variants. Blood samples from 10 healthy control and 20 SLE patients were collected and analyzed for the expression of candidate genes. Results: Our SMR analysis identified suggestive associations between NLRP6 expression (OR = 0.528, 95%CI = 0.291–0.96) and p27Kip1 protein abundance (OR = 0.269, 95%CI = 0.08–0.904) with SLE susceptibility in the discovery cohort, supported by colocalization evidence. Additionally, we found that the methylation of the NLRP6 promoter (cg06432119) was significantly increased, while NLRP6 expression and p27Kip1 level were significantly decreased in SLE patients compared to controls. Furthermore, NLRP6 mRNA expression was significantly negatively correlated with the SLE severity (SLEDAI-2000). Conclusions: These findings not only prioritized candidate genes via SMR analysis but also provided evidence of epigenetic dysregulation of NLRP6 and its correlation with disease activity in SLE, thereby offering novel insights into the underlying mechanisms. Full article

Analyzing music similarity in large catalogs is challenging because people perceive music differently and important details are found in audio, text, and metadata. This article introduces a multimodal framework that uses an ontology to make music similarity and recommendation more explainable. The framework brings together learned features from audio, lyrics, and other text with structured metadata in a shared similarity space, and then improves ranking with a music ontology that captures relationships between songs, artists, genres, and moods. The design works with any encoder that creates fixed-size features. This study uses strong neural audio and text encoders, mainly based on transformers. This approach allows the system to handle different input types while staying reliable across datasets. This study tests the framework on several open music and audio datasets using content-based retrieval tasks and standard ranking measures. In addition to Configurations C1–C4, this study includes an external content-based reference baseline based on conventional MIR audio descriptors. This baseline represents a signal-level retrieval approach that models complementary aspects of the audio signal, such as timbre, harmony, and spectral characteristics, and is evaluated under the same retrieval protocol as the main framework. It is included to provide an external comparison point outside the proposed C1–C4 design. Compared to audio-only and non-ontological variants within the same framework, the proposed multimodal and ontology-guided configurations achieve better precision, recall, and mean average precision, and also cover more rare content. Visualizations and case studies show that combining different data types and using ontology-based reranking can improve performance and make results easier to interpret. This work lays the groundwork for explainable, cognitively informed music recommendation systems and points to future work in modeling user behavior over time and adapting to different cultures. Full article

This study aims to present a parametric analysis in the optimization problem for steel-concrete composite cellular beams with steel deck slabs. A comparative analysis was carried out considering two scenarios, namely, (i) in the first scenario, the slab span and applied loads were varied, adopting slab configurations from a manufacturer’s catalog for spans of 10 m to 20 m with a step of 2.5 m; (ii) in the second scenario, the same span and loading conditions were considered; however, slab optimization was performed by introducing reinforcement in order to evaluate the resulting impacts on the structural design. In both analyzed scenarios, the objective function was defined as the composite system’s CO₂ emissions. The design constraints were defined based on literature recommendations, and to solve the optimization problem, the Particle Swarm Optimization (PSO) algorithm was also adopted. The results demonstrate that the PSO algorithm was effective in identifying optimal solutions and that the introduction of slab reinforcement, combined with optimal design, led to CO₂ emission reductions of up to 25% at the highest load levels analyzed. Full article

Industrial electrical houses are engineered systems that transform and control electrical power to supply industrial loads. Preparing technical proposals for these rooms requires consistent engineering choices across multiple artifacts while drawing from heterogeneous client documents, historical projects, and supplier catalogs. This paper reports an industrial prototype that integrates generative AI, system modeling, and mathematical decision methods to support that workflow. We represent requested outputs as ordered sequences of functions and link those functions to candidate equipment blocks through functional and physical graphs that enable traceable retrieval and reuse. Using this representation, we compute a minimal internal-cost baseline by solving a mixed-integer assignment model with sizing constraints, and we rank technically feasible alternatives using fuzzy DEMATEL to derive criterion weights and TOPSIS to obtain an overall ordering under multiple criteria. The workflow is illustrated with an example and the prototype tool used in a company operating in Chile, Peru, Ecuador, and Bolivia, where document ingestion and equipment-list extraction are integrated with human validation. The results illustrate how structured representations, optimization, and multi-criteria ranking can support auditable configurations for engineering review and commercial selection. Full article

The structures predicted by AlphaFold can provide unprecedented opportunities for docking screens; however, experimentally validated examples of using the apo-form are limited. This study reports novel chemotype inhibitors targeting the leucine-rich repeat kinase 2 (LRRK2) kinase domain through a docking screen using one of the ensemble structures starting from the template deposited by AlphaFold2. The MODELLER software generated the ensemble. The conformer that showed the best early enrichment of true positives with the mixture of known ligands and their property-matched decoys was selected. The docking screen against approximately 1.3 million small molecules and enzyme-based assays with the LRRK2 kinase domain followed. We selected 17 molecules, excluding those similar to all known kinase inhibitors. Combined with analogs-by-catalog, ten new small molecules with Ki values below 15 μM were discovered, including one sub-μM inhibitor. To test selectivity, enzyme assays with a mutant and six additional kinases, including known off-targets of existing LRRK2 inhibitors, were performed using three inhibitors. The data suggest that the novelty in chemical structure may be insufficient for providing selectivity. Our approach is generally applicable to cases where information on known binders is available but experimental structure is not. Full article

Supergiants are luminous post-main-sequence massive stars whose effective temperatures ($T_{\mathrm{eff}}$) are key inputs for stellar evolution and feedback studies. We present a photometry-based procedure to derive $T_{\mathrm{eff}}$ for a sample of galactic supergiants of spectral types B and A by fitting the spectral energy distributions (SEDs) in the UV-to-mid-IR range to ATLAS9 model spectra converted into synthetic photometry using the corresponding passband transmission profiles while simultaneously solving for the line-of-sight extinction. The SEDs were constructed from published data taken in different photometric systems (Johnson or Kron–Cousins $UBVRI$, Strömgren $uvby$, $JHK$ magnitudes from various sources, and AllWISE) and supplemented with UV TD-1 fluxes for brighter stars. The interstellar extinction law is based on Cardelli, Clayton & Mathis approximation assuming a total-to-selective ratio $R_V=A_V/E(B-V)=3.1$. The best-fitting parameters are obtained by minimizing a covariance-weighted ${\chi }^2$ statistic in logarithmic flux space over a grid of $A_V$ values and a discrete model grid. We test the method on 20 targets and find generally good agreement with published literature temperature estimates. The main limitations are non-simultaneous photometry for possibly variable objects and the residual coupling between temperature and reddening in broadband SED fitting. This study is intended as a methodological demonstration on a pilot sample rather than a definitive parameter catalog. Full article

The exponential growth of unstructured medical data, particularly clinical notes and diagnostic reports, presents mounting challenges for healthcare knowledge extraction and utilization. This study introduces the Digital Medical Catalog (DMC), a framework that automates the conversion of clinical narratives into an auditable, semantically structured knowledge base. The framework combines BioClinicalBERT embeddings, c-TF-IDF statistical grounding, and semantic clustering, enabling high-fidelity classification (Macro F1 = 0.877 ± 0.012), traceable topic labeling, and temporal trend analysis. By demonstrating that semantic representation methods, reinforced with statistical grounding, are essential for large-scale medical text processing, this work establishes a foundation for privacy-preserving data governance and real-time intelligence within modern healthcare infrastructures. Full article

Martinique sponge fauna was largely undocumented until 2017, when the first inventory of Porifera colonizing coral reefs, mangroves and caves around the island was published. We performed an integrative classification of sponges in the foraging area of hawksbill turtle (Eretmochelys imbricata) in Martinique. Sponge specimens were retrieved as direct or indirect diet items consumed by hawksbill turtles after video observations, and the feeding behaviors of these predators were tracked. Morphology was supplemented with molecular identification (DNA barcoding) based on a multi-locus approach using COI, 28S and ITS genetic markers. Seventeen different species were identified, belonging to seven orders: Poecilosclerida, Dictyoceratida, Verongiida, Agelasida, Haplosclerida, Clionaida, and Tetractinellida. Haplosclerida exhibited the greatest diversity and species abundance, followed by Verongiida. The 28S marker provided the highest confidence in species identification. We provided new barcode records for Hyattella cavernosa and Amphimedon caribica. Among the cataloged sponges, only four of them had been previously reported as food items of E. imbricata (Xestospongia muta, Iotrochota birotulata, Spirastrella coccinea and Cinachyrella kuekenthali). The rest represent newly documented items that are potentially preyed upon by this turtle predator. The characterization of sponges as being part of the feeding habitat of hawksbill turtles underpins management and protection plans for this critically endangered species, and the benthic community on which they feed, by providing criteria for generating networks of Marine Protected Areas (MPAs) in the Caribbean regions. Full article

This study proposes an integrated software model for automatic document classification and metadata generation based on the Dublin Core standard to address the issue of rapid and consistent management of archival documents in a digital environment. This approach combines the stages of receiving incoming documents, converting them to text using optical character recognition (OCR), image preprocessing (binarization, deskew, noise reduction), and text cleaning and vectorization (TF–IDF) into a single pipeline. In the document classification stage, the Bidirectional Encoder Representations from Transformers (BERT) model with a context-sensitive transformer architecture is used, along with classical machine learning models (Logistic Regression, Naive Bayes, Support Vector Machine) and an ensemble approach (LightGBM), to increase the accuracy by modeling the document content at a deep semantic level. Experiments were conducted on the RVL-CDIP dataset, and the OCR efficiency was evaluated using the Character Error Rate (CER) indicator, and the classification results were evaluated using the accuracy, precision, recall and F1-score metrics. The results confirmed the high stability and generalization ability of the BERT (accuracy, 95.1%; F1, 95.0%) and LightGBM (accuracy, 93.2%; F1, 93.2%) models. In the final stage, OCR, NER, and classification outputs are automatically organized into Dublin Core metadata elements (Title, Creator, Date, Description, Subject, Type, Format, Language) and exported in JSON/XML formats. This automation significantly reduces manual cataloging effort and improves indexing and retrieval efficiency in digital archival systems. Full article

The mandatory introduction of Romania’s national e-invoicing system, ANAF e-Factura, in January 2024 has reshaped B2B document exchange in the retail sector, but suppliers still operate in parallel with two proprietary electronic data interchange (EDI) platforms, EDINET and DocProcess, which increases integration complexity. This paper presents the architecture, implementation, and evaluation of a custom Laravel-based B2B platform developed to automate commercial workflows across these three channels. The system supports XML purchase order ingestion and normalization, product identifier resolution, unified order persistence, platform-specific invoice XML generation, and ANAF SPV submission via SmartBill and Oblio REST APIs. A comparative analysis of real production XML documents showed full field-level overlap across 21 invoice data dimensions, with the main differences between systems related to entity identification schemes rather than business information content. During 2025, the platform processed 1247 EDI purchase orders and achieved an 87.30% fully automated processing rate, reaching 94.60% by year-end through progressive product catalog enrichment. The results indicate that ANAF e-Factura is technically capable of covering the core invoice exchange function currently duplicated by proprietary EDI platforms, while their coexistence continues to impose additional integration effort and slows SME digital transformation, particularly for small and medium-sized suppliers. Full article

Synthetic derivatives of testosterone known as 17α-alkylated anabolic–androgenic steroids have been developed to retain anabolic effects while enabling oral administration. Here, we present newly identified hydrated solid forms of three agents: oxandrolone hemihydrate (C₁₉H₃₀O₃·0.5H₂O), fluoxymesterone hydrate (C₂₀H₂₉FO₃·H₂O), and methandienone hemihydrate (C₂₀H₂₈O₂·0.5H₂O). Their crystal structures were determined using single-crystal X-ray diffraction, supplemented by powder X-ray diffraction and thermal analyses. Computational methods were employed to investigate molecular interactions and crystal packing. Lattice energy evaluations revealed that the hydrated forms are energetically less stable than their anhydrous counterparts, with significantly less negative values (e.g., −113.4 kJ/mol for oxandrolone hemihydrate vs. −164.4 kJ/mol for the anhydrous form). Energy decomposition analysis indicates that while water molecules participate mostly in electrostatic-driven hydrogen bonding, they disrupt the dispersive packing efficiency found in the anhydrous phases. Specifically, intermolecular interaction energies show that host–host hydrogen bonds (up to −62.2 kJ/mol in oxandrolone) dominate over weaker host–water couplings (−8.9 to −34.9 kJ/mol). The newly reported crystal structures contribute to the expanding catalog of solid-state forms for 17α-alkylated steroids and provide important details regarding their metastable nature and the dehydration-driven phase transformations observed under climatic stress conditions. Full article

The tourism industry faces increasing pressure for agile, personalized services, yet travel agencies struggle with fragmented knowledge scattered across isolated systems and legacy formats. While Large Language Models (LLMs) are widely applied in customer-facing roles, their potential to enhance internal operational efficiency remains largely underexplored. This study presents the design and evaluation of an intelligent assistant specifically for travel agency operations, built upon a Retrieval-Augmented Generation (RAG) architecture using Gemini 2.0 Flash. The system integrates heterogeneous data sources, including structured product catalogs and unstructured documentation processed via Optical Character Recognition (OCR), into a unified interface comprising work assistance, interactive training, and evaluation modules. Results demonstrate information retrieval times not greater than 45 s, ensuring its daily usability, while maintaining 95% accuracy. Furthermore, the system democratizes tacit senior expertise and accelerates new employee onboarding. This research validates RAG architectures as a powerful solution to knowledge fragmentation, shifting the strategic AI focus from customer automation to employee empowerment and operational optimization. Full article

We present an enhanced earthquake catalog for Central Evia and the Northern Gulf of Evia, in Central Greece, between June 2018 and November 2023. The area is characterized by a low background seismicity rate, with occasional clustered events and seismic swarms, including those of February–April 2022 near Drosia and of October 2022 near Styra. The seismic catalog was enhanced by integrating additional data acquired through the application of the EQ-Transformer deep-learning model. A total of ~1400 events were analyzed, with ~1200 of them being successfully relocated with the double-difference method. The available focal mechanisms indicate predominantly normal, oblique-normal, and pure strike-slip faulting. The relocated seismicity was examined in conjunction with known mapped faults to investigate the activated structures at depth, providing insight into their degree of activity. In Drosia, the seismicity, at a depth of ~14 km, can be related to an E–W dextral strike-slip fault, with subtle surficial expression. In Psachna, the epicenters are oriented in an NE–SW direction, not matching the strike of the mainshock’s normal focal mechanism, but roughly coinciding with NE–SW-oriented topographic spurs and the local drainage pattern. In Markates and Prokopi, the seismicity is sparse, but the focal mechanisms are consistent with SW–NE dextral strike-slip faulting, aligned with the trend of the Nileas depression and the Prokopi–Pelion fault zone. Finally, in Mouriki, the seismic cluster is characterized by WNW-ESE normal faulting, most likely related to the SSW-dipping Messapio fault. Full article