Accurate classification of water quality and precise prediction of the Water Quality Index (WQI) are essential for safeguarding aquatic ecosystems and mitigating disease risks in aquaculture. This study systematically evaluates multiple machine learning models including LightGBM, XGBoost, Random Forest, and Support Vector Machines using grid search optimization to enhance performance. Results show that ensemble models, particularly LightGBM and XGBoost, achieved superior accuracy in water quality classification (up to 99.65 %). For WQI prediction, XGBoost regression delivered the highest performance with an R² of 0.9685. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions and quantify feature contributions. Dissolved oxygen and BOD emerged as the most influential predictors, followed by turbidity, nitrate, and electrical conductivity, aligning with known risk factors for aquatic disease outbreaks. These findings underscore the potential of combining advanced machine learning with explainable AI techniques and real-time water quality data to enable proactive monitoring and early warning systems for sustainable aquatic health management.

Biochar has emerged as a promising, low-cost, and eco-friendly material for wastewater treatment due to its high surface area, porosity, and functional group diversity, which enable efficient adsorption of a wide range of contaminants. While several studies have explored the use of biochar in pollutant removal, a comprehensive synthesis that integrates its multifunctional roles, recent technological advancements, and future scalability challenges remains limited. This review uniquely addresses this gap by providing a critical and up-to-date assessment of biochar’s application for removing pharmaceuticals, microplastics, heavy metals, organic pollutants, and nutrients from aquatic systems. In contrast to previous reviews, this study emphasizes the innovative modification techniques—such as activation, functionalization, and composite synthesis—that enhances biochar's performance, and evaluates its integration with emerging technologies, including nanomaterials and hybrid filtration systems. Furthermore, we incorporate a bibliometric analysis of Scopus-indexed literature (2010–2025) to identify evolving research trends, leading contributors, and existing knowledge gaps in the field. Current findings indicate that biochar can achieve pollutant removal efficiencies of up to 80%, with notable performance in nutrient removal (71% for ammonium-nitrogen and 57% for phosphate-phosphorus) and pathogen reduction (2.4 log₁₀ units). However, its large-scale application is still constrained by limitations in regeneration efficiency, cost-effectiveness, and scalability. By offering an integrated review of scientific innovations, field applicability, and sustainability considerations, this paper provides new insights and a forward-looking perspective on transforming biochar into a practical, scalable solution for wastewater remediation. Graphical abstract

Contemporary energy research is becoming more interdisciplinary through the involvement of technical, economic, and social aspects that must be addressed simultaneously. Within such interdisciplinary energy research, the novel domain of energy informatics plays an important role, as it involves different disciplines addressing the socio-techno-economic challenges of sustainable energy and power systems in a holistic manner. The objective of this paper is to draw an overview of the novel domain of energy informatics by addressing the educational opportunities as well as related challenges in light of current trends and the future direction of research and industrial innovation. In this study we discuss the energy informatics domain in a way that goes beyond a purely scientific research perspective. This paper widens the analyses by including reflections on current and future didactic approaches with industrial innovation and research as a background. This paper provides key recommendations for the content of a foundational introductory energy informatics course, as well as suggestions on distinguishing features to be addressed through more specialized courses in the field. The importance of this work is based on the need for better guidelines for a more appropriate education of a new generation of experts who can take on the novel interdisciplinary challenges present in future integrated, sustainable energy systems. Article highlights: Didactic approaches in the energy informatics domain are discussed based on research and industrial trends. Research trends and industrial innovation driven by energy informatics are investigated. A fundamental framework for an energy informatics course is defined together with specialized distinguishing features.

The increasing demand for Electric Vehicle (EV) charging is putting pressure on the power grids and capacities of charging stations. This work focuses on how to use indirect control through price signals to level out the load curve in order to avoid the power consumption from exceeding these capacities. We propose mathematical programming models for the indirect control of EV charging that aim at finding an optimal set of price signals to be sent to the drivers based on price elasticities. The objective is to satisfy the demand for a given price structure, or minimize the curtailment of loads, when there is a shortage of capacity. The key contribution is the use of elasticity matrices through which it is possible to estimate the EV drivers’ reactions to the price signals. As real-world data on relating the elasticity values to the EV driver’s behaviour are currently non-existent, we concentrate on sensitivity analysis to test how different assumptions on elasticities affect the optimal price structure. In particular, we study how market segments of drivers with different elasticities may affect the ability of the operator to both handle a capacity problem and properly satisfy the charging needs.