During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O₃), and nitrogen dioxide (NO₂). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O₃ gases and a similar accuracy for the NO₂ gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.

The current global energy crisis is one of the most pressing challenges of the 21st century, it highlights the fragility of an old power system based on fossil fuels, geopolitical dependencies and often the precariousness and age of equipment and installations, affecting the economy, security and social stability on a national, regional and world scale. The risk of blackout thus becomes not only a technological threat, but a symbol of the need for a paradigm shift. The energy future must be sustainable, collaborative and adaptable—to guarantee not only the continuity of services with electricity, but also the stability of modern society. This paper provides an intrinsic interdisciplinary analysis on the causes, implications and possible solutions related to major imbalances in contemporary power systems, emphasizing the growing risk of blackout (large power outages). The main causes of crises are analyzed interdisciplinary, such as: insecurity in the functioning of the National Power System, terrorist attack on the National Power System, extreme weather condition, natural calamity, energy insecurity and political/military insecurity. The paper highlights the interdependence between energy infrastructure and energy security, as well as the vulnerability of power grids to cyberattacks, natural disasters and consumer pressures. In addition, socio-economic, technological and political issues are addressed, providing an integrated view of the phenomenon. Finally, national, regional and bilateral mitigation, limitation and restoration (resilience) procedures and measures are proposed in the event of an electricity crisis—blackout.

The paper presents the structure of an active parallel filter for reducing harmonic pollution and reactive power. The active power filter control is based on instantaneous reactive power theory. The authors present the modelling of parallel active filter based on this theory and the simulation results in MATLAB-SIMULINK. The method is based on instantaneous reactive power (TPRI_P).

In an era of connectivity, the Internet of Things introduces smart solutions for smart and sustainable agriculture, bringing alternatives to overcome the food crisis. Among these solutions, smart greenhouses support crop and vegetable agriculture regardless of season and cultivated area by carefully controlling and managing parameters like temperature, air and soil humidity, and light. Smart technologies have proven to be successful tools for increasing agricultural production at both the macro and micro levels, which is an important step in streamlining small-scale agriculture. This paper presents an experimental Internet of Things-based small-scale greenhouse prototype as a proof of concept for the benefits of merging smart sensing, connectivity, IoT, and mobile-based applications, for growing cultures. Our proposed solution is cost-friendly and includes a photovoltaic panel and a buffer battery for reducing energy consumption costs, while also assuring functionality during night and cloudy weather and a mobile application for easy data visualization and monitoring of the greenhouse.