A Neural Network-Based Distributional Constraint Learning Methodology for Mixed-Integer Stochastic Optimization

The use of machine learning methods helps to improve decision making in different fields. In particular, the idea of bridging predictions (machine learning models) and prescriptions (optimization problems) is gaining attention within the scientific community. One of the main ideas to address this trade-off is the so-called Constraint Learning (CL) methodology, where the structures of the machine learning model can be treated as a set of constraints to be embedded within the optimization problem, establishing therelationship between a direct decision variable x and a response variable y. However, most CL approaches have focused on making point predictions for a certain variable, not taking into account the statistical and external uncertainty faced in the modeling process. In this paper, we extend the CL methodology to deal with uncertainty in the response variable y. The novel Distributional Constraint Learning (DCL) methodology makes use of a piece-wise linearizable neural network-based model to estimate the parametersof the conditional distribution of y (dependent on decisions x and contextualinformation), which can be embedded within mixed-integer optimization problems. In particular, we formulate a stochastic optimization problem by sampling random values from the estimated distribution by using a linear set of constraints. In this sense, DCL combines both the high predictive performance of the neural network method and the possibility of generating scenarios to account for uncertainty within a tractable optimization model. The behavior of the proposed methodology is tested in a real-worldproblem in the context of electricity systems, where a Virtual Power Plant seeks to optimize its operation, subject to different forms of uncertainty, and with price-responsive consumers

Deep neural networks for the quantile estimation of regional renewable energy production

Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage

Pareto optimal prediction intervals with hypernetworks

As the relevance of probabilistic forecasting grows, the need of estimating multiple high-quality prediction intervals (PI) also increases. In the current state of the art, most deep neural network gradient descent-based methods take into account interval width and coverage into a single loss function, focusing on a unique nominal coverage target, and adding additional parameters to control the coverage-width trade-off. The Pareto Optimal Prediction Interval Hypernetwork (POPI-HN) approach developed in this work has been derived to treat this coverage-width trade-off as a multi-objective problem, obtaining a complete set of Pareto Optimal solutions (Pareto front). POPI-HN are able to be trained through gradient descent with no need to add extra parameters to control the width-coverage trade-off of PIs. Once the Pareto set has been obtained, users can extract the PI with the required coverage. Comparative results with recently introduced Quality-Driven loss show similar behavior in coverage while improving interval width for the majority of the studied domains, making POPI-HN a competing alternative for estimating uncertainty in regression tasks where PIs with multiple coverages are needed.This publication is part of the I+D+i project PID2019-107455RB-C22, funded by MCIN /AEI/10.13039/501100011033. This work was also supported by the Comunidad de Madrid Excellence Program. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022

Los contratos sobre el buque en Derecho Español. Análisis práctico

Prólogo / José Luis Gabaldón García (pp. 9-13). -- Introducción (pp. 15-18). -- El contrato de construcción naval: aspectos prácticos / Gonzalo Alvar Ezquerra (pp. 19-37). -- El contrato de compraventa / Carlos López-Quiroga, Luz Martínez de Azcoitia y José Sánchez-Fayos Martín-Peña (pp. 39-58). -- El contrato de arrendamiento de buque / Rodolfo González Lebrero (pp. 59-75). -- El contrato de fletamento por tiempo / José María Alcántara González (pp. 77-102). -- El contrato de fletamento por viaje: contenido obligacional / Juan Pablo Rodríguez Delgado (pp. 103-144). -- El contrato de transporte marítimo en régimen de conocimiento de embarque / Javier del Corte (pp. 145-186). -- Los documentos de transporte / Carlos Llorente (pp. 187-205). -- Contratos de utilización del buque para fines distintos del transporte de mercancías / José Manuel G. Pellicer (pp. 207-221). -- El contrato de arrendamiento náutico / León von Ondarza (pp. 223-244). -- El contrato de pasaje marítimo / Hannah de Bustos, Antonio Quirós de Sas y Julio López Quiroga (pp. 245-260). -- Los contratos de gestión naval para la dotación del buque / Bernardo Ruiz Lima (pp. 261-279). -- El contrato de gestión naval / Víctor Mata Garrido (pp. 281-302). -- El contrato de consignación de buques /Jesús Barbadillo Eyzaguirre (pp. 303-323). -- El contrato de manipulación portuaria / Carlos Pérez (pp. 325-338). -- El contrato de practicaje / Alicia Velasco Nates (pp. 339-356). -- Los contratos de mediación en la explotación del buque / Carmen Codes Cid y Martín Prieto Sulleiro (pp. 357-372). -- El contrato de remolque / Ana Sánchez Horneros (pp. 373- 392). -- El contrato de remolque / Jaime de Castro (pp. 393-412). -- El contrato de salvamento / Luis Souto (pp. 413-430). -- El contrato de remoción de restos / Verónica Meana (pp. 431-446). -- El contrato de clasificación del buque / Jaime Rodrigo de Larrucea (pp. 447-463). -- El seguro de casco y máquina / Carlos Cerdá Donat y Diego de San Simón Palacios (pp. 465-491). -- Los clubes de protección e indemnización (P&I) / Miguel Caballero (pp. 493-504). -- El seguro de protección e indemnización (P&I) / Jaime Albors (pp. 505-524). -- El seguro del acreedor hipotecario / Luis F. Gómez de Mariaca Fernández (pp. 525-540)

Comprehensive analysis and insights gained from long-term experience of the Spanish DILI Registry

Altres ajuts: Fondo Europeo de Desarrollo Regional (FEDER); Agencia Española del Medicamento; Consejería de Salud de Andalucía.Background & Aims: Prospective drug-induced liver injury (DILI) registries are important sources of information on idiosyncratic DILI. We aimed to present a comprehensive analysis of 843 patients with DILI enrolled into the Spanish DILI Registry over a 20-year time period. Methods: Cases were identified, diagnosed and followed prospectively. Clinical features, drug information and outcome data were collected. Results: A total of 843 patients, with a mean age of 54 years (48% females), were enrolled up to 2018. Hepatocellular injury was associated with younger age (adjusted odds ratio [aOR] per year 0.983; 95% CI 0.974-0.991) and lower platelet count (aOR per unit 0.996; 95% CI 0.994-0.998). Anti-infectives were the most common causative drug class (40%). Liver-related mortality was more frequent in patients with hepatocellular damage aged ≥65 years (p = 0.0083) and in patients with underlying liver disease (p = 0.0221). Independent predictors of liver-related death/transplantation included nR-based hepatocellular injury, female sex, higher onset aspartate aminotransferase (AST) and bilirubin values. nR-based hepatocellular injury was not associated with 6-month overall mortality, for which comorbidity burden played a more important role. The prognostic capacity of Hy's law varied between causative agents. Empirical therapy (corticosteroids, ursodeoxycholic acid and MARS) was prescribed to 20% of patients. Drug-induced autoimmune hepatitis patients (26 cases) were mainly females (62%) with hepatocellular damage (92%), who more frequently received immunosuppressive therapy (58%). Conclusions: AST elevation at onset is a strong predictor of poor outcome and should be routinely assessed in DILI evaluation. Mortality is higher in older patients with hepatocellular damage and patients with underlying hepatic conditions. The Spanish DILI Registry is a valuable tool in the identification of causative drugs, clinical signatures and prognostic risk factors in DILI and can aid physicians in DILI characterisation and management. Lay summary: Clinical information on drug-induced liver injury (DILI) collected from enrolled patients in the Spanish DILI Registry can guide physicians in the decision-making process. We have found that older patients with hepatocellular type liver injury and patients with additional liver conditions are at a higher risk of mortality. The type of liver injury, patient sex and analytical values of aspartate aminotransferase and total bilirubin can also help predict clinical outcomes