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Abstract: Space weather is usually evaluated through large-scale geomagnetic disturbances, particularly coronal mass ejections (CMEs) and storm indices such as Kp and Dst. However, disruptive events can also arise when these parameters remain quiet, suggesting additional mechanisms. This paper introduces the Global Electric Circuit (GEC) as a framework to explain such cases, showing how changes in ionospheric conductivity, total electron content (TEC), and radiation flux can influence terrestrial infrastructures. The first contribution is to highlight the GEC as a driver of space weather impacts, extending existing models beyond CME and geomagnetic indices. The second is to develop a cross-sectoral risk perspective that traces how GEC-related disturbances affect both energy and digital infrastructures, creating cascading vulnerabilities. The approach is evaluated using the 2025 Spain blackout, when widespread disruptions occurred despite the absence of major CME activity. Observational data show anomalies in ionospheric and atmospheric conditions consistent with GEC-driven processes. These disturbances coincided with fluctuations in photovoltaic output, grid instability, and communication interruptions. The paper also proposes methodological guidelines, recommending multi-scale analysis windows (4 hours, 16 hours, 3–7 days) and the integration of multi-source datasets. These include upstream satellite observations at the L1 point, GNSS-derived TEC and ionosonde data, atmospheric reanalysis and pressure fields, ground magnetometer networks, and infrastructure-level energy and digital data. The findings demonstrate that incorporating GEC into space weather studies and explicitly linking energy and digital sectors provides a stronger basis for both scientific research and practical resilience planning.
Keywords: smart city, space weather, Global Electric Circuit (GEC), energy sector, digital sector, Spain blackout, resilience
Published in ReVIS: 22.01.2026; Views: 126; Downloads: 2
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Abstract: Organizations are increasingly confronted with regulatory requirements that encompass both personal data protection and cybersecurity. While the GDPR establishes a clear framework for processing personal data, the NIS 2 Directive introduces additional obligations aimed at strengthening cybersecurity resilience. Addressing these combined demands represents a complex legal, organizational, and technical challenge. One way forward is the development of integrated audit frameworks that support systematic compliance assessment across both domains. Building on prior work in which the original privacy by design (PbD) model was developed and empirically tested, this paper proposes an extended model that incorporates NIS 2 requirements. The extended framework aspires to provide a robust and comprehensive instrument for identifying compliance gaps and supporting organizations in adapting more effectively to an increasingly demanding regulatory landscape.
Keywords: privacy by design, conceptual model, cybersecurity, NIS 2
Published in ReVIS: 22.01.2026; Views: 100; Downloads: 1
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Abstract: This paper presents the design and implementation of a digital feedback loop for optimizing material consumption in a manufacturing environment. The study focuses on small and medium-sized enterprises (SMEs) that often lack access to costly Manufacturing Execution Systems (MES). We demonstrate how commonly available tools such as Microsoft Excel, Power Query, and open-source solutions can be combined. We created a functional feedback mechanism linking ERP data, CNC machine outputs, and production logs. The proposed solution was developed and tested in a woodworking company producing custom furniture components. By integrating heterogeneous data sources, we established a real-time overview of material usage and waste, reducing manual work and increasing process transparency. The study highlights the role of simple Extract Transform Load (ETL) tools in supporting smart manufacturing, data-driven decision-making, and continuous process improvement.
Keywords: digital feedback loop, smart manufacturing, power query, ERP integration, data transformation, material optimization, ETL process
Published in ReVIS: 22.01.2026; Views: 106; Downloads: 2
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Abstract: Customer retention is a major problem in the telecommunications industry. This study develops and evaluates models to identify possible churners. Machine learning techniques (“Decision Trees”, “Random Forests”, “Logistic Regression” and “Neural Networks (multilayer perceptron MLP)”) were applied through Python and R to analyze the “Telco Customer Churn” Kaggle dataset, based on customer assests and service usage. The data pre-processing compiled missing data and then standardized it. Evaluation used nested 10-fold cross-validation with an inner loop for hyperparameter tuning and mutual-information top-K feature pruning, with pre-processing confined to training folds. In Python, RF and LR achieve F1(~0.629), with Logistic Regression accuracy ~0.75. In R, Logistic Regression performed best (F1 ≈ 0.60 ± 0.03, Accuracy ≈ 0.80 ± 0.01). Metrics derived from pooled confusion matrices averaged over folds equal outer-fold means, confirming generalization across folds and between Python and R. Research offers empirical evidence for transferring and testing churn prediction models across Python and R in telecommunications analytics, with fully reproducible evaluation and results.
Keywords: customer churn, telecommunications, churn prediction, logistic regression, neural networks (MLP), Python, R, nested cross-validation
Published in ReVIS: 22.01.2026; Views: 128; Downloads: 1
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Abstract: This study constitutes the second part of our investigation presented at ITIS 2023, which explores the search for objective physiological biomarkers for major depressive disorder (MDD). Moving beyond the established role of Heart Rate Variability (HRV), this preliminary research focuses on Pulse-Respiratory Coupling (PRC) – the coordination between cardiac and respiratory rhythms. We hypothesize that depression, characterized by autonomic nervous system (ANS) dysregulation, disrupts this coupling. A group of 73 subjects (healthy controls, untreated depressed patients, and patients treated with tricyclic antidepressants) were submitted to simultaneous electrocardiogram (EKG) and respiratory recording. Analysis revealed a distinct degradation of PRC in the depressed group, manifesting as a loss of synchronous patterns observed in healthy subjects. Machine learning models were trained on features derived from PRC timing. The k-Nearest Neighbors algorithm achieved a promising classification accuracy of 97.3% in distinguishing depressed from healthy individuals, outperforming other classifiers like Random Forest (95.9%) and Support Vector Machine (95.9%). While these results are preliminary and require validation in larger cohorts, they strongly suggest that PRC is a sensitive, non-invasive marker of ANS dysfunction in depression. This work underscores the potential of integrating multi-system physiological analysis with artificial intelligence to create objective aids for psychiatric diagnosis.
Keywords: major depressive disorder, physiological biomarkers, pulse-respiratory coupling, heart rate variability, autonomic nervous system, machine learning
Published in ReVIS: 22.01.2026; Views: 115; Downloads: 2
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