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El Repositorio Institucional de la Universidad FinisTerrae, es gestionado por el Sistema de Bibliotecas y tiene por objetivo permitir el acceso libre a la producción académica e institucional de la Universidad, aumentando la visibilidad de sus contenidos y garantizando su conservación.
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“De forma oportuna y eficaz” : el manejo de los grupos subversivos en el gobierno de la transición chilena a la democracia. 1990-1994
(Universidad Finis Terrae (Chile) Facultad de Humanidades y Comunicaciones, 2025) Cuadro Maturana, José Manuel; Fernández Abara, Joaquín prof. guía
En la presente investigación se analizan las medidas que tomó el gobierno de Patricio Aylwin en la desarticulación de los movimientos subversivos que subsistían en el Chile de la transición a la democracia, y que nacieron al alero de la oposición más dura al Régimen Militar. Mediante una caracterización de los grupos y sus motivaciones, trataremos primero su desarrollo histórico; para luego observar las primeras visiones que tenía el gobierno al respecto, sobre todo los primeros meses de su administración. Consecutivamente, se abordará el levantamiento de información que realizan para luego dotar de herramientas administrativas a la Subsecretaría del Interior y al Consejo Coordinador de Seguridad Pública, órgano creado exclusivamente para dichos fines. Junto con ello, analizaremos las medidas de desarticulación que llevan adelante y la persecución penal con leyes que responden no solo a la necesidad de penalizar sus actos terroristas, sino también adecuar la normativa chilena a los estándares internacionales y propios de una democracia en forma.
A Digital-Twin-Enabled Resilience Framework (DTERF) for Machine-Learning-Based Anomaly Detection in High-PV Cyber–Physical Smart Grids
(MPDI, 2026-08-26) Yanine, Franco Fernando; Hidalgo, Mauricio; Frez, Jonathan; Rao, Challa Krishna; Sahoo, Sarat Kumar
The rapid integration of solar photovoltaic (PV) generation, distributed energy resources, and advanced communication infrastructures is transforming conventional power systems into highly interconnected cyber–physical smart grids. Although this transition improves sustainability and operational flexibility, it also increases grid-management complexity and introduces cyber–physical vulnerabilities, including false data injection attacks, communication failures, equipment degradation, and renewable-induced operational instabilities.
This paper presents the Digital-Twin-Enabled Resilience Framework (DTERF), a conceptual reference architecture for anomaly detection in high-PV cyber–physical smart grids. DTERF integrates heterogeneous cyber–physical data acquisition, Digital Twin-based contextual representation, machine-learning analytics, explainable decision support, adaptive operational response, continuous learning, and self-healing capabilities within a unified resilience cycle. The framework is grounded in a structured review and comparative assessment ofcontemporary machine-learning approaches and recent integrated smart-grid research. Itsarchitecture is conceptually evaluated through requirements-to-architecture traceability, examining functional coverage and internal consistency across the complete operational cycle. The analysis shows that DTERF provides explicit architectural mechanisms addressing the principal requirements identified in the literature, including contextual anomaly analysis, interpretability, cybersecurity robustness, resilience support, and operational integration. Rather than proposing a new anomaly detection algorithm or claiming empirical performance superiority, DTERF provides a technology-agnostic architectural foundationfor coordinating complementary capabilities required for resilient anomaly management.
Future work should empirically validate the framework using Digital Twin simulation environments, representative high-PV distribution systems, cyber–physical anomaly scenarios, and real or utility-derived operational data.
Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study
(John Wiley & Sons, 2026-08-19) Seo, Jieun; Ripart, Mathilde; Kaas, Helene; Kronlage, Cornelius; Sinclair, Ben; Vivash, Lucy; Courtney, Merran R.; O'Brien, Terence J.; Gopinath, Siby; Parasuram, Harilal; Kandemirli, Sedat; Alarab, Natally; Lai, Lillian; Likeman, Marcus; Zhang, Kai; Mo, Jiajie; Ciobotaru, Georgian; Galea, James; Sequeiros-Peggs, Philip; Hamandi, Khalid; Xie, Hua; Illapani, Venkata Sita Priyanka.; Gaillard, William D.; Cohen, Nathan T.; Weil, Alexander G.; Henrichon-Goulet, Florence; Lahlou, Kenza S.; Hadjinicolaou, Aristides; Ibáñez, Agustín; Rojas-Costa, Gonzalo M.; Urbach, Horst; Bücheler, Lara; Heers, Marcel; Valls Carbó, Adrián; Toledano, Rafael; Nobile, Giulia; Parodi, Costanza; Tortora, Domenico; Consales, Alessandro; Riva, Antonella; Severino, Mariasavina; Tisdall, Martin; D'Arco, Felice; Mankad, Kshitij; Chari, Aswin; Eriksson, Maria H.; Piper, Rory J.; Cross, J. Helen; Baldeweg, Torsten; González-Ortiz, Sofia; Pariente, Jose; Bargalló, Nuria; Liu, Yawu; Kälviäinen, Reetta; Barba, Carmen; Lenge, Matteo; Guerrini, Renzo; Iwasaki, Masaki; Sone, Daichi; Maki, Hiroyuki; Imokawa, Tomoki; Sato, Noriko; Jung, Julien; Sepulveda, Francisco; Mansilla, Daniel; Goycoolea, Andres; Lopez, Ingeborg; Napolitano, Antonio; De Benedictis, Alessandro; De Palma, Luca; Rossi-Espagnet, Maria Camilla; Kondylidis, Nikolaos; Gkiatis, Kostakis; Garganis, Kyriakos; Pepper, Joshua; Seri, Stefano; Duncan, John S.; Yasuda, Clarissa L.; Scárdua-Silva, Lucas; Alvim, Marina K. M.; Cendes, Fernando; Gennari, Antonio G.; O'Gorman Tuura, Ruth; Ramantani, Georgia; Josyula, Mariam; Stein, Joel; Sinha, Nishant; Davis, Kate; Hogan, R. Edward.; Maccotta, Luigi; Adler, Sophie; Wagstyl, Konrad
Objective Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative magnetic resonance imaging (MRI). Current methods for resection cavity masking are time-consuming and labor-intensive, and existing automated approaches exhibit variable segmentation accuracy, particularly on extratemporal resections. We developed MELD-PostOp, a deep learning tool trained and evaluated on a large, heterogeneous cohort to automatically segment resection cavities. Methods The study included 1.5- and 3T postoperative three-dimensional T1-weighted MRI images from the Multicentre Epilepsy Lesion Detection (MELD) project (nsubjects = 969, 27 centers) and from the EPISURG dataset (n = 133). The cohort included children and adults, alongside a range of resection locations, pathologies, and MRI characteristics. Resection cavities were individually segmented in 285 subjects and used to train an nnU-Net prototype model. The prototype model was used to generate an additional 680 resection masks, which were subsequently quality-controlled, edited, and combined with the original 285 to train the final MELD-PostOp model (n = 965). A Stratified Test Cohort (n = 50) and Independent Test Cohort (n = 87) were withheld for model evaluation. Performance was evaluated using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), number of predicted clusters, and inference runtime, and compared against established tools (Epic-CHOP, ResectVol, and RESSEG). Results MELD-PostOp achieved a median DSC of .85 and HD95 of 3.61 on the combined test cohort, outperforming Epic-CHOP (DSC .69, HD95 9.67), ResectVol (DSC .66, HD95 15.05), and RESSEG (DSC .43, HD95 32.67), with significant improvements seen in both temporal and especially extratemporal resections. The model detected 98.5% (135/137) of resection cavities. MELD-PostOp runtime was 17 s per MRI, compared to 612 s (ResectVol), 3205 s (Epic-CHOP), and 4 s (RESSEG). MELD-PostOp performance remained high across clinical and imaging subgroups (median DSC > .8). Significance MELD-PostOp is an open-source research tool that provides an accurate, efficient, and generalizable solution for postoperative resection cavity segmentation using only postoperative MRI scans.
Mapping intensive care across Ibero-America: The FEPIMCTI multinational survey of bed capacity, workforce, and pandemic response
(Elsevier, 0022-07-26) Matos, Alfredo; Giglio, Andrés; Pérez-Fernandez, Javier; Nates, Joseph; Cárdenas, Yenny; Raimondi, Nestor; Sánchez, Jorge; Hidalgo, Jorge; Rezende, Ederlon; Borges-Sa, Marcio
Background: Intensive care resources are unequally distributed across Ibero-America, and reliable comparable data are scarce, hindering cross-national comparison and health-system planning. Methods: We conducted a cross-sectional survey of the 25 national critical care societies affiliated with FEPIMCTI. Presidents or designated representatives reported country-level data on ICU beds, intensivist and nursing workforce, organizational models, training pathways, and surge capacity before and during the COVID-19 pandemic. Resource density was summarized as the country-level median with IQR and as the population-weighted regional rate. Results: Twenty-one countries, including Spain and Portugal, participated, representing 693.6 million inhabitants. A total of 78,723 ICU beds were reported (country-level median 6.1 per 100,000, IQR 3.3–12.4; population-weighted rate 11.3), with public ICUs accounting for 51.6%. A total of 23,803 intensivists were identified (median 2.6 per 100,000, IQR 1.0–5.0), with five countries below 1 per 100,000. Nurse-to-patient ratios ranged from 1:1 to 1:7, and universal 24/7 intensivist coverage was reported in only a subset of countries. During the pandemic, ICU bed capacity rose 69%; among the 20 countries with data for both periods, physician involvement rose 32%, largely through redeployment of non-intensivists rather than growth of the certified workforce. Conclusions: Critical care capacity, workforce, and organization vary widely across Ibero-America, with many countries below high-income benchmarks and pandemic surges met largely through temporary redeployment. These findings provide the first coordinated regional benchmark to guide workforce development, standardized training, and organizational strengthening.
Silencio performativo y mediación de gracia: el silencio como acción lingüístico-teológica en la predicación cristiana
(Universidad Católica del Maule, 2026-08-17) Dantas Freitas Estrela, Kênio Angelo
Lejos de ser una mera ausencia, el silencio en la predicación cristiana puede entenderse como una forma de lenguaje: un acto expresivo, performativo y mediador de gracia. El problema que aborda este artículo consiste en determinar en qué sentido el silencio puede operar como acción comunicativa dentro del discurso homilético. Este artículo desarrolla esa tesis desde una perspectiva interdisciplinaria que articula la teología espiritual y el análisis lingüístico formal. A partir del libro La fuerza del silencio, del cardenal Robert Sarah, y de varias homilías de Amigos de Dios, de San Josemaría Escrivá, consideradas aquí como un corpus discursivo, se analiza cómo ciertas elipsis, pausas y fragmentos implícitos intensifican la densidad comunicativa del discurso homilético. Utilizando herramientas como la teoría de los actos de habla, la teoría de la relevancia y la semántica formal, se sostiene que, bajo condiciones pragmáticas específicas, el silencio puede funcionar como un acto performativo con efectos interpretativos y espirituales en el oyente. Este estudio prolonga una línea de investigación previa sobre la unidad de vida y la predicación como acto comunicativo integral, proponiendo ahora una caracterización explícita del silencio como operador interpretativo de carácter teológico-lingüístico.