Introduction: This article is the product of exploratory research carried out at the Universidad del Atlántico (Barranquilla, Colombia) in 2026, which consists of a bibliometric study that addresses corporate image and customer perception of the use of renewable energy, based on the premise that such use has become a key element of business sustainability strategies. Problem: Despite the growth in studies on sustainability and clean energy, the literature that comprehensively analyzes the relationship between corporate image and customer perception regarding the use of renewable energy is scattered and poorly systematized. Objective: To analyze the evolution and main trends in scientific production on corporate image and customer perception associated with the use of renewable energy. Methodology: The bibliometric study was developed following the PRISMA guidelines, based on 206 articles indexed in Scopus and WoS between 2015 and 2026. The analysis was supported by tools such as Bibliometrix (R), VOSviewer, and Microsoft Excel. Results: The results show significant growth in publications in recent years, a high geographical concentration of scientific output, and limited collaboration among authors. Thematic clusters related to sustainability, corporate social responsibility, and consumer behavior are also identified. Conclusion: The literature shows an expanding but still fragmented field of research, highlighting the need for more comprehensive studies that delve deeper into the impact of renewable energy use on corporate image and customer perception. Limitations: The study is limited to documents indexed in Scopus and a bibliometric approach; therefore, it does not include publications from other databases.
Introduction: This article stems from the doctoral thesis “Characterization and Classification of Seismo-Volcanic Signals Using Artificial Intelligence Techniques and Data Analytics” at the University of Cauca (2022–2026). Problem: Real-time seismic monitoring requires a rapid and accurate classification process for various types of events to assess volcanic activity and thus activate possible early warnings. However, this field faces a fundamental operational bottleneck: in a newly instrumented volcano, monitoring networks record different types of earthquakes with a lower volume of data than required by standard deep learning classifiers, which need hundreds or thousands of labeled samples to achieve adequate performance. Objective: To overcome the data scarcity problem, we developed and evaluated a Multimodal Prototypical Network (MPN) designed to maintain good classification performance even when labeled data are extremely limited. Methodology: Our approach merges CLIP ViT-B/32 visual embeddings, extracted directly from seismic spectrograms, with a 19-dimensional tabular descriptor vector based on the physical characteristics of the signals under study. This multimodal input is processed using a 5-fold ensemble strategy driven by focal loss. The model was rigorously evaluated using the Chiles-Cerro Negro Volcanic Complex (CCNVC) dataset, categorizing three distinct signal classes: Volcanic-Tectonic (VT), Long-Period (LP), and Very-Long-Period (VLP) events. Results: Under full supervision (6,366 labeled samples), the MPN achieved exceptional performance with a 98.73% balanced accuracy and a 99.89% ROC AUC. Crucially, in a few-shot scenario with only 33 labeled waveforms (14 VT, 14 LP, and 5 VLP), the model retained a highly robust 85.27% balanced accuracy and a 95.36% ROC AUC. Furthermore, physical consistency analysis confirmed that the network’s decisions are driven by spectral morphology (amplitude–geometry correlation, r = 0.248) rather than simple amplitude geometry, aligning with the expected physics of volcanic source mechanisms. Conclusion: These findings establish intermodal transfer from large-scale vision-and-language pretraining as a highly viable and physically consistent strategy for volcanic monitoring environments with a scarcity of labeled data. Originality: Instead of relying solely on raw waveform statistics or standard deep learning, this work uniquely combines foundation models (CLIP) with strict tabular constraints based on physical features to solve the specific few-shot labeling problem inherent in operational volcanology. Limitations: While performance with limited labeled data is noteworthy, the absolute lower bound for labeling requirements (e.g., fewer than 5 VLP samples) was not tested. Furthermore, the model’s physical consistency is tied to the specific origin mechanisms of VT, LP, and VLP events in the CCNVC; applying this exact multimodal framework to signals with greater overlap (such as hybrid events or tremors) might require recalibrating the tabular vector based on the physics of those signals.
Introduction: This article is the result of research on the Tourist Accommodation Recommendation Model Using Machine Learning with Information Extraction Through the Web Scraping Technique conducted at the Universidad Distrital Francisco José de Caldas in 2026. Problem: In tourism cold-start scenarios, the lack of structured data and user history reduces the quality of accommodation recommendations. Web Scraping is required [2], but it introduces inconsistencies and structural variability that hinder accurate recommendations. Objective: To design and evaluate a hybrid geo-semantic recommender system that integrates content-based similarity between accommodations with geographic proximity under an unsupervised framework using web-scraped data.Methodology: Data from 13,204 Colombian accommodations were scraped from Booking.com using Playwright, preprocessed, and represented as vectors. Then, cosine similarity and Haversine distance were combined into a weighted hybrid scoring function, evaluated through similarity standard deviation, intra-list similarity, and geographic relevance. Results: The base model achieved an average cosine similarity of 0.868 and a mean geographic distance of 0.16 km, with 100% of recommendations within the defined geographic constraint radius. In an exploratory comparison against Booking.com, the proposed model returned semantically and spatially consistent alternatives independent of commercial positioning criteria. Conclusions: The geo-semantic model generates coherent and geographically relevant recommendations from web-scraped data in cold-start contexts where user interaction data are unavailable. Originality: This work proposes a reproducible geo-semantic recommender model for tourist accommodation in cold-start scenarios, integrating semantically represented web-scraped accommodation data with geographic proximity to generate recommendations. Limitations: The model does not incorporate user preference data.
Introduction: This article is the result of doctoral research conducted in 2025 on quantum software architecture, addressing the transformative potential of quantum computing and the challenges associated with integrating quantum capabilities into software systems. Problem: The lack of standardized architectural models for hybrid classical–quantum systems limits interoperability, reuse, and systematic software design. Objective: This study proposes a reference architecture to support the design and evolution of hybrid classical–quantum software systems. Methodology: The architecture was derived through a harmonization process. Results: The proposed framework defines ten architectural elements organized across three dimensions: Conceptual, Organizational, and Quality. Conclusion: The architecture provides a structured foundation for designing hybrid classical–quantum solutions. Originality: The work consolidates dispersed knowledge into a unified framework for hybrid classical–quantum systems. Limitations: The model is based on the currently limited body of literature and industrial experience, requiring future validation.
Introduction: This article is a product of research focused on the adoption of secure development practices in educational settings, developed at Universidad Veracruzana in 2024. Security is crucial in web development to guarantee data protection against unauthorized access; frameworks like ASP.NET Core offer built-in mechanisms, yet the lack of structured models can lead to system exploitation. Problem: Despite the availability of technical documentation, knowledge gaps persist regarding the effective contexts for these mechanisms, the specific threats they mitigate, and consolidated practical guidelines. Objective: This study identifies and analyzes ASP.NET Core security mechanisms, application scenarios, associated threats, and best practices. Methodology: A Gray Literature Review (GLR) was conducted following Garousi’s methodology, analyzing 38 sources from 2022 to 2024 to capture industry practices. Results: Eight mechanisms and thirteen threats were identified. The review highlights that most vulnerabilities arise from incorrect configurations and improper handling of sensitive data. Conclusion: These findings provide a foundation for developers to implement secure coding, emphasizing that effectiveness depends on correct configuration rather than just the tools. Originality: This research bridges the gap between documentation and practitioner experience by linking mechanisms to real-world attack vectors and practices. Limitations: The scope is limited to ASP.NET Core and gray literature, potentially excluding proprietary industry practices.
