Rafael E. Vasquez

This study addresses the critical challenge of sediment-driven degradation in aging hydropower infrastructure by implementing a novel Digital Operational Technology modernization framework at the AES Chivor Hydropower Plant in Colombia. While conventional sediment monitoring relies on sporadic manual campaigns, this research introduces a continuous, real-time sensing architecture that integrates hybrid acoustic–optical sensors, covering a range of 10 to 6000 mg/L, directly into the plant’s SCADA (Supervisory Control and Data Acquisition) system. The novelty of this approach lies in the seamless coupling of high-frequency physical data (15 min sampling) with an Operational Decision Support Module, enabling adaptive turbine management. Statistical validation against laboratory gravimetric standards yielded a robust correlation of 0.93, confirming the system’s precision in quantifying suspended sediment concentrations. By identifying critical fine particle fractions in real time, the proposed model enables a precision-based maintenance strategy that significantly reduces unscheduled production downtime and mitigates accelerated wear in Pelton turbines. These findings provide a scalable benchmark for extending the operational life of large-scale hydropower facilities facing advanced sedimentation risks through digital transformation.

The rehabilitation of critical water-conveyance infrastructure plays a fundamental role in the water–energy nexus and constitutes a key strategy for extending the operational lifetime of hydropower facilities. These interventions are aligned to the United Nations’ 2030 Agenda, which declare that ensuring access to affordable, reliable, sustainable, and modern energy systems is essential for long-term energy security. This paper presents a field-validated, non-thermal repair methodology developed for the Chivor II hydropower penstock, a critical water conduction tunnel used for energy production in Colombia, that has been affected by a circumferential fatigue crack. Due to the geometric confinement of the penstock within the rock mass, conventional thermal or stress-relief treatments were unfeasible. Therefore, the proposed methodology uses controlled material removal with a welding sequence designed to release stored elastic energy and induce compressive stresses through the Poisson effect. Its main contribution is demonstrated through pilot-scale validation and full-scale implementation under real operating conditions, achieving 50% reduction in tensile stresses and left 99% of the examined surface under compression, which represents effective residual-stress stabilization, structural recovery, and hydraulic reliability. The methodology ensures reliable water conveyance for hydropower generation and can be applied to other pressurized conduits and pipelines where accessibility and heat treatment are constrained, strengthening SDGs 7 and 9 on clean energy, water sustainability, and resilient infrastructure.

Sustainable energy management systems are part of the global energy transition by digitally transforming utility companies throughout the past decade. Industry 4.0 (4IR) has been adopted to improve competitiveness and align its processes with trends that require changes in the producer‐consumer relationship. This study examines how AES Colombia uses Industry 4.0 to improve efficiency and promote a sustainable energy transition. It focuses on automating key processes within the company's hydropower operations, representing 6% of Colombia's energy market. The research shows that robotic‐process‐automation technologies reduce pricing simulation time from 27 to 5 h and tax declaration manual labour from 1560 to 584 h. These advancements enhance operational efficiency and significantly improve decision‐making capabilities, thereby boosting the reliability of information utilized across various operational domains, including financial, market, and regulatory processes. The findings underscore the critical role of flexibility, cultural change, and continuous training in facilitating successful digital transformation initiatives. This work contributes to understanding how intelligent systems can optimize energy companies’ processes while promoting a human‐centred approach to Industry 4.0 technologies. The results underscore the importance of aligning technological advancements with sustainability goals to drive significant improvements in the energy sector, paving the way for a more resilient and adaptable energy landscape.

Sliding mode control (SMC) has been widely used to control linear and nonlinear dynamics systems because of its robustness against parametric uncertainties and matched disturbances. Although SMC design has traditionally addressed process model-based approaches, the rapid advancements in instrumentation and control systems driven by Industry 4.0, coupled with the increased complexity of the controlled processes, have led to the growing acceptance of controllers based on data-driven techniques. This review article aims to explore the landscape of SMC, focusing specifically on data-driven techniques through a comprehensive systematic literature review that includes a bibliometric analysis of relevant documents and a cumulative production model to estimate the deceleration point of the scientific production of this topic. The most used SMC schemes and their integration with data-driven techniques and intelligent algorithms, including identifying the leading applications, are presented.

This study assesses Hurricane Iota’s impact on Providencia island’s reef environments, using Google Earth Engine, Satellite Derived Bathymetry, and machine learning to calculate a supervised classification process to delineate six geomorphic reef units. Results reveal dynamic changes, including erosion in the Lagoon unit (4.47% pre-Iota, 2.27% post-Iota), loss on the Back Reef (38.14%), and Rock Terrace (6.15%). Reef Ridge showed minimal change, acting as an effective wave barrier. Back Reef and the deep Rock Terrace experienced significant erosion (−3 to −14 m) to the northeast, with sedimentary dynamics observed in deeper units (up to 22 m). The high thematic accuracies found (Kappa 99%) illustrate the effectiveness of the assessment to (i) map the reef rapidly, (ii) provide tools for long-term monitoring of changes over time and (iii) improve management strategies and decision-making.

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

This paper presents a novel integration of Systems Engineering (SE) methodologies and Industry 4.0 (I4.0) technologies in the design of robotic systems, focusing on enhancing underwater robotic missions. Using the conceptual design of an underwater exploration vehicle as a case study, we demonstrate how SE can systematically incorporate I4.0 tools to improve mission performance and meet stakeholder expectations. The study begins with an overview of the SE approach, emphasizing the conceptual design stage and aligning it with the application and case study of design theories. We then explore various I4.0 technologies, highlighting their functional benefits rather than technical specifics and addressing design methods for I4.0. Remotely Operated Vehicles (ROVs) are examined in terms of classification, components, and tasks, showcasing their evolution driven by technological advancements, thus tackling the complexity and design of complex systems. The core of our study involves defining stakeholder expectations, using quality function deployment for requirements definition, and performing a functional and logical decomposition of the ROV system. To deal with design fixation within the design team, we developed a tool to help integrate new technologies by also empathizing with their functional capabilities rather than the technology itself. Our approach underscores the importance of understanding and incorporating new technologies functionally, aligning with the transition towards Industry/Society 5.0. This work not only illustrates the synergy between SE and I4.0, but also offers a structured methodology for advancing the design and functionality of complex systems, setting a blueprint for future developments in this field.

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

The overarching vision of Society 5.0 seeks to integrate technology to enhance quality of life and address social issues, with the primary goal of creating human-centered communities, which nowadays represent the inhabitants of smart cities. In this context, this work addresses the design of a modular strategic platform for the smart supervision of public expenditure, to be used by the Directorate of Information, Analysis, and Immediate Reaction (DIARI) of the General Comptroller of the Republic (CGR) of Colombia as a significant contribution towards the country’s transition into Society 5.0. The design was performed by conducting a detailed literature review on fiscal control; performing a comprehensive analysis of the legal, organizational, and technological aspects of the country and the CGR; and developing six functional modules focused on topics such as continuous learning, strategic decision making, distinctive value, strategic achievements, capacity building, and organization, within a three-year implementation plan, through a participatory approach. This plan aims to integrate the platform to enable real-time monitoring, early detection of irregularities, and increased transparency in the management of data and public resources; since the start of the operation of the strategic platform in 2024, the DIARI increased the number of alerts generated by 29% over a three-month period with respect to the year 2023. The strategic platform for the DIARI of the CGR is useful for smart cities and the transition into Society 5.0 since it ensures efficient public expenditure management, enhancing transparency and citizen empowerment through modern technologies, data analytics, and active participation in governance processes.