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This Author published in this journals
All Journal Journal of Moeslim Research Technik Research of Scientia Naturalis Journal of Social Entrepreneurship and Creative Technology
Giovanni Rossi
University of Bologna
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Physics

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STRUCTURAL OPTIMIZATION UNDER EXTREME CONDITIONS: ENGINEERING DESIGN FOR CLIMATE-INDUCED HAZARDS Saripuddin M; Ethan Tan; Giovanni Rossi
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.4004

Abstract

Structural infrastructure worldwide faces increasing exposure to climate-induced hazards, including extreme flooding, high-intensity wind events, prolonged heat waves, and compound environmental stressors that challenge conventional engineering design standards. Growing uncertainty associated with climate change necessitates innovative approaches capable of enhancing resilience while maintaining structural efficiency and economic feasibility. This study aims to examine the effectiveness of structural optimization strategies in improving infrastructure performance under extreme environmental conditions. A quantitative engineering research design was employed using finite element modeling, climate hazard simulations, probabilistic risk assessment, and multi-objective optimization techniques. Structural systems were evaluated across multiple hazard scenarios to assess resilience, reliability, material efficiency, failure probability, and lifecycle cost performance. Results indicate that optimized structures achieved significantly higher resilience scores, improved structural reliability, reduced stress concentrations, lower failure probabilities, and greater material efficiency compared with conventional designs. Optimization-based configurations demonstrated superior adaptability to future climate scenarios and maintained operational performance under severe loading conditions. Case-study simulations further revealed substantial reductions in displacement and maintenance requirements while improving long-term infrastructure sustainability. Findings suggest that integrating climate projections with advanced optimization frameworks can substantially strengthen engineering resilience and support more effective adaptation strategies. Structural optimization therefore represents a promising pathway for developing safer, more sustainable, and climate-responsive infrastructure systems capable of addressing emerging environmental risks.
MACHINE LEARNING ALGORITHMS FOR REAL-TIME DETECTION AND PREDICTION OF SEISMIC ACTIVITIES TO ENHANCE DISASTER RISK MITIGATION STRATEGIES Nofirman Nofirman; Daiki Nishida; Giovanni Rossi
Research of Scientia Naturalis Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i2.4170

Abstract

Earthquakes pose significant threats to human safety, critical infrastructure, and socioeconomic stability because their occurrence is highly complex and difficult to predict accurately in real time. Although conventional seismic monitoring systems have improved earthquake detection, they remain limited by computational constraints, delayed event recognition, and inadequate identification of nonlinear seismic patterns. This study evaluated the effectiveness of machine learning algorithms for real-time seismic detection and prediction and their contribution to disaster risk mitigation. A mixed-methods sequential explanatory design was employed using approximately 1.8 million seismic waveform segments representing 48,000 earthquake events collected from 320 monitoring stations across eight tectonically active regions. Quantitative analyses included comparative evaluation of supervised, ensemble, and deep learning algorithms using multivariate statistics, structural equation modeling, hierarchical regression, mediation, and moderation analyses, while qualitative evidence was examined through thematic analysis. Findings showed that deep learning and hybrid ensemble models consistently achieved higher prediction accuracy, computational efficiency, early warning reliability, and lower false alarm rates than conventional approaches. Improved prediction accuracy strengthened disaster response readiness, while dense sensor networks and institutional coordination enhanced operational effectiveness, supporting resilient earthquake risk mitigation and evidence-based emergency decision-making.
REVITALIZING CULTURAL HERITAGE: AN AR-BASED DIGITAL-PRENEURSHIP START-UP FOR SUSTAINABLE TOURISM AND COMMUNITY EMPOWERMENT Rit Som; Ton Kiat; Giovanni Rossi
Journal of Social Entrepreneurship and Creative Technology Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jseact.v2i6.2989

Abstract

Cultural heritage is a vital aspect of community identity and history, yet many regions face challenges in preserving and promoting their heritage in the face of modern economic pressures. Traditional tourism practices often lead to the commercialization and degradation of cultural sites, while communities struggle to benefit economically from their heritage. Augmented Reality (AR) technology offers a promising solution by providing immersive, interactive experiences that can both preserve and promote cultural heritage while supporting sustainable tourism. This study explores the implementation of an AR-based digital-preneurship start-up model designed to revitalize cultural heritage through tourism while empowering local communities. The research employs a mixed-methods approach, combining quantitative surveys and qualitative interviews with both local stakeholders and tourists. The findings reveal that the AR platform significantly enhanced both tourist engagement and local economic outcomes, increasing community participation in tourism-related activities and boosting income for local businesses. The study concludes that AR-based digital-preneurship offers a scalable, sustainable model for cultural heritage revitalization, providing communities with a new avenue for economic development and cultural preservation. This research contributes to the growing body of knowledge on the intersection of technology, entrepreneurship, and sustainable tourism.
Co-Authors Daiki Nishida Ethan Tan Nofirman, Nofirman Rit Som Saripuddin M Ton Kiat