Rafael Augusto Núñez Rodríguez

We present ESNformer, a hybrid architecture that couples an Echo State Network (ESN) reservoir with a Transformer encoder for classification of structured, multi-indicator assessment data: a fixed-order vector of complementary indicators per assessment instance rather than a repeated-measures time series. The reservoir acts as a fixed nonlinear feature map over the indicator vector, while self-attention, made position-aware over the fixed column order, learns how each indicator’s evidence contributes to the final decision, so the two components, together, capture local, indicator-level detail and global, cross-indicator interactions within a single, end-to-end trainable model. Interpretability is treated as a first-class design requirement rather than an afterthought: the architecture is paired with an explainability layer combining SHAP feature attribution (reported both globally and per class), the model’s own attention weights, a deletion/insertion faithfulness test that quantitatively verifies which inputs the model actually relies on, and counterfactual maps that translate a prediction into an actionable, inspectable recommendation. We evaluate the architecture on a concrete case study, classifying Braille-literacy instructional recommendations from 15 pedagogical indicators grouped into three categories (Mangold’s, ABKL, and Progresar), using a benchmark of 900 real assessment instances (630 used, together with a class-conditional augmentation procedure, to build a 2100-instance training set) with validation and test partitions (135 instances each) kept exclusively real. On this benchmark, the tuned model reached 85.33% accuracy, 85.90% macro-precision, 85.33% macro-recall, an F1 score of 85.25%, and an AUC of 0.95 on the real test set. SHAP attribution, attention weights, and the faithfulness test converge on the same two dominant indicators (response time and error count): removing them alone collapses accuracy to chance, while retaining only them recovers most of the model’s accuracy. We report this transparently alongside a comparison against ESN-only, Transformer-only, and tabular baselines (logistic regression, decision tree, random forest, XGBoost, and an MLP) on the same data and discuss what the hybrid architecture and its explainability pipeline add beyond what the two dominant indicators already explain and how the approach generalizes to other tabular and mixed-granularity assessment settings that require both predictive accuracy and a verifiable account of what drove each decision.

This study offers a comprehensive overview of the scientific landscape surrounding computer-aided drug design (CADD) for triplex-forming oligonucleotides (TFOs) within antisense and antigene therapeutic strategies. A dual-method approach was used, combining bibliometric mapping of 6154 Scopus-indexed articles (1980–2023) to identify publication trends and intellectual networks, with a PRISMA 2020-guided systematic review of 62 experimental studies (2015–2024) from Scopus and Web of Science, after removing duplicates using AteneaSIRES. Results show the strong dominance and clinical maturity of antisense strategies, supported by 18 FDA/EMA/MHLW-approved drugs, whereas antigene approaches remain technically limited and underdeveloped. Antigene research has focused on triplex stability modeling and biophysical feasibility but faces challenges with poor biochemical stability, limited in vivo validation, and outdated methods. Meanwhile, antisense design benefits advanced CADD pipelines, including molecular dynamics and docking modeling. Based on these insights, we propose a practical, narrative roadmap as a methodological guide: integrating proven antisense design practices and providing actionable strategies to enhance antigene research, ultimately increasing the translational potential of therapeutic TFOs with solid mechanistic and translational support.

This paper presents the design and implementation of a control algorithm for power converters in a microgrid, with the main objective of providing the flexibility to adjust the system inertia. The increasing integration of renewable energy sources in microgrids has driven the development of advanced control techniques to ensure stability and power quality. The proposed algorithm combines droop control, synchronverter dynamics, and virtual impedance to achieve a robust and efficient control strategy. Simulations were conducted to validate the algorithm’s performance, demonstrating its capability to maintain voltage within acceptable limits and improve the inertial response of the microgrid. The results contribute to the advancement of intelligent and resilient microgrid development, which is essential for the transition towards a more sustainable energy system.

This article presents a computational model for transmission and generation expansion planning considering the impact of virtual power lines, which consists of the investment in energy storage in the transmission system as well as being able to determine the reduction and postponement of investments in transmission lines. The flexibility from the TSO-DSO interconnection is also modeled, analyzing its impact on system expansion investments. Flexibility is provided to the AC power flow transmission network model by distribution systems connected at the transmission system nodes. The transmission system flexibility requirements are provided by expansion planning performed by the connected DSOs. The objective of the model is to minimize the overall cost of system operation and investments in transmission, generation and flexibility requirements. A data-driven distributionally robust optimization-DDDRO approach is proposed to consider uncertainties of demand and variable renewable energy generation. The column and constraint generation algorithm and duality-free decomposition method are adopted. Case studies using a Garver 6-node system and the IEEE RTS-GMLC were carried out to validate the model and evaluate the values and impacts of local flexibility on transmission system expansion. The results obtained demonstrate a reduction in total costs, an improvement in the efficient use of the transmission system and an improvement in the locational marginal price indicator of the transmission system.

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model’s efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter’s flexibility for optimal energy management under uncertain conditions.

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHM) are a feasible alternative to increase the performance of conventional microgrids (MG) and to increase the penetration of Distributed Energy Resource (DER) at the same time, active and reactive power dispatch on the system. Despite this, MHMs present challenges in managing resources under uncertainties and controlling electronic converters associated with the ST and DER, making it complex to achieve optimal system performance. In this paper, a Data-Driven Distributionally Robust Optimization (DDDRO) for Day-Ahead Operation Planning of an ST-based MHM considering the reactive power dispatch of the DER that minimizes network losses, voltage deviations, and operation cost simultaneously, considering uncertainties of Photovoltaic Generators (PVG) and demand, is proposed. The Column and Constraint Generation (C&CG) algorithm and Duality-Free Decomposition (DFD) method are adopted. Besides, the original mixed-integer non-linear planning problem is converted into a Mixed-Integer (MI) Second-Order Cone Programming (SOCP) problem through second-order cone relaxation and positive octagonal constraint method. Simulation experiments and results with a connected MHM system demonstrate the effectiveness and performance of the proposed model. Finally, we observe the effect of the meshed structure of MG and the positive impact of integrating the ST to form MHM, taking advantage of the degrees of freedom of this multi-stage converter for optimal energy management under uncertain conditions.

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST’s equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink^® environment of Matlab^{® 9.12} is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT^® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model’s accuracy and the MHM’s accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach.

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHM) present advantages concerning the performance of conventional AC/DC microgrids (MG). Despite this, MHMs present challenges in managing and controlling electronic converters associated with the ST and Distributed Energy Resources (DER), a scenario that makes it complex to achieve optimal system performance. Additionally, the high penetration of DER would impact the voltage profile, reduce the reactive compensation, and increase the losses in the distribution lines at a high cost in operation. This work proposes an optimal power management computational model for the day-ahead optimal operation planning of ST-based MHM. The management algorithm delivers the optimal operating points of each of the converters of the MG so that it allows the control of active power on both the AC and DC sides; likewise, it will enable reactive power injection control according to the availability of the solar photovoltaic resource and Battery Energy Storage Systems (BESS). According to the tests performed under different operation scenarios with varying degrees of penetration of Photovoltaic Generator (PVG), the benefits of implementing the ST as an energy router according to the operating set points by the optimization algorithm are evidenced. Integrating the ST in MHM and optimization algorithms is a suitable alternative for managing microgrids with high penetration of distributed energy resources. This allows for improving the voltage profile in the MHM, reducing losses in the lines and energy exchange costs with the distribution network.

Microgrids are considered an adequate alternative to overcome the challenges involving integrating distributed energy resources in distribution systems to contribute to the ’Three D’ trend in the electricity sector, i.e., decentralize, decarbonize, and digitize electricity. This paper reviews the most relevant works to establish a baseline for the advancement and development of smart transformer-based meshed hybrid microgrids and energy management systems. First, the structure of the solid-state transformers as smart transformers and their potential application as an energy router in a microgrid is discussed. Then, the principle of conformation of meshed hybrid microgrids based on a smart transformer and the topologies reported in the literature are reviewed. Finally, power management systems integrated into smart transformers-based meshed hybrid microgrids are studied. According to the findings and conclusions, it is considered that smart transformers-based meshed hybrid microgrids operated by an optimal energy management system under uncertainty are a potentially feasible technological alternative for the adequate penetration of distributed energy resources into local distribution systems.