The transition to the circular economy is a basic principle of the policy of the European Union, it is the chance for a cleaner world, an ideal world with “zero waste”. This document analyzes the implementation degree of circular economy in Romania through 10 monitoring indicators defined by European Commission in ”Measuring progress towards the circular economy in the European Union—Key indicators for a monitoring framework”, released in January 2018. In order to understand the paper, it was considered necessary to define the indicators with reference to the targets proposed by the European Union. The analysis of the stage of implementation of the circular economy in Romania was made by using the data published by Eurostat. Methodologically, a comparative study was carried out between the level of implementation of circular economy indicators in Romania and the EU and the position occupied by Romania among the member states was identified for each of these 10 circular economy monitoring criteria. Each indicator is analyzed and the causes and motivations that led to the obtaining of the mentioned values are identified. The results of the study show that Romania is in the upper part of the hierarchy for certain indicators, contributing to the achievement of EU targets. It also identifies the causes that delay performance in others, indicating future solutions, necessary to be adopted to remedy the situation. Implementing the circular economy requires long-term vision, political determination and consistency, and, last but not least, funding. In these directions, governmental measures must be taken to increase performance at the national level. In conclusion with an economy that is not sufficiently strong, and affected as it is by the current crises, Romania is moving forward on the path of transitioning to a circular economy.

The development of renewable energy is one of the aspirations of the European Union energy policy, being generated by the struggle against climatic changes and by the intention of achieving a high rate of energy autonomy. In this context, geothermal energy is a viable solution which has been little exploited so far. Analysing the EU’s dependence on imported energy, the paper provides a short review of the utilization of geothermal energy, of the advantages and of the problems raised by the exploitation of this resource. It also analyzes the availability of this resource in Romania and the contribution it can have towards the energy autonomy of local communities. The paper presents a particular methodology for calculating the energy autonomy. Using this methodology, one can obtain an energetic overview of the community or the area analyzed, can calculate the degree of energy autonomy and, based on the results achieved, a sustainable development strategy can be designed. The low enthalpy case study used emphasizes the way this methodology can be deployed and, moreover, allows an analysis of the contribution of geothermal energy to the degree of autonomy of the Romanian community.

Solar photovoltaic (PV) generation suffers from multiple serious issues, for example shading effects directly affect the performance of installed PV arrays. By studying these issues, it can play a key role in improving the limitations and performance of PV panels. The study of PV arrays under partial shading conditions is mentioned in many papers as a part of other studies and as an independent phenomenon. Therefore, it is essential to improve these studies and update them with new research and study this problem in detail to know how to limit the effects of partial shading irradiance conditions on PV arrays performances, including economical aspects. This paper proposes to study the effects of different partial shading irradiance conditions for a PV array in various configurations. The analysis is proved in detail through the simulation of the proposed PV array in the Matlab/Simulink environment. The obtained results show the performances of interconnected PV arrays under different partial shading irradiance and temperature conditions, including the solar array I-V and P-V characteristics. In addition, different PV array configurations with and without bypass and blocking protection diodes improve and minimize the effect of partial shading conditions and avoid the reductions in output power.

The accurate characterization of low-power near-infrared LEDs typically requires costly radiometric equipment, limiting broader accessibility. This study proposes a low-cost indirect method for comparative NIR LED characterization based on the thermal response of black-coated aluminum absorbing targets monitored by a commercial MLX90614 contactless temperature sensor integrated with an ESP32 acquisition system. The absorbed optical power was estimated from a steady-state energy-balance model combining convective and radiative heat transfer, with geometry-dependent effective coefficients derived for 10 mm and 15 mm diameter targets. Experiments were conducted using 850 nm and 940 nm LEDs at drive currents between 30 mA and 100 mA. The absorbed power increased linearly with the drive current and electrical input power across all configurations, with R² values of 0.995–0.997 and 0.996–0.999, respectively. The 15 mm targets exhibited higher capture ratios (10.4–11.9%) compared to the 10 mm targets (8.4–9.4%). The combined measurement uncertainty ranged from 13% at high drive currents to nearly 70% at low drive currents, with the temperature-rise sensitivity being the dominant factor; within the recommended operating range (≥70 mA for 10 mm and ≥80 mA for 15 mm targets), the uncertainty remained below 25%. The proposed platform enables reliable comparative characterization of low-power NIR emitters using exclusively off-the-shelf components.

Accurate characterization of low-power near-infrared LEDs typically requires costly radiometric equipment, limiting broader accessibility. This study proposes a low-cost indirect method for comparative NIR LED characterization based on the thermal response of black-coated aluminum absorbing targets monitored by a commercial MLX90614 contactless temperature sensor integrated with an ESP32 acquisition system. Absorbed optical power was estimated from a steady-state energy-balance model combining convective and radiative heat transfer, with geometry-dependent effective coefficients derived for 10 mm and 15 mm diameter targets. Experiments were conducted using 850 nm and 940 nm LEDs at drive currents between 30 mA and 100 mA. Absorbed power increased linearly with drive current and electrical input power across all configurations, with R² values of 0.995–0.997 and 0.996–0.999, respectively. The 15 mm targets exhibited higher capture ratios (10.4–11.9%) compared to the 10 mm targets (8.4–9.4%). The combined measurement uncertainty ranged from 13% at high drive currents to nearly 70% at low drive currents, with the temperature-rise sensitivity being the dominant factor; within the recommended operating range (≥70 mA for 10 mm, ≥80 mA for 15 mm targets) uncertainty remained below 25%. The proposed platform enables reliable comparative characterization of low-power NIR emitters using exclusively off-the-shelf components.

Due to their vast potential, Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various applications. To prevent in-flight failures and loss of control, implementing Internet of Things (IoT)-based Predictive Maintenance (PdM) systems is crucial. However, data collected from PdM systems often contains outliers, which can significantly degrade the accuracy and performance of predictive models. In this paper, we present a comparative performance analysis of several outlier detection methods, namely K-Nearest Neighbors (KNN), Autoencoder (AE), and Isolation Forest (IForest). The datasets used to evaluate these methods were acquired from a UAV predictive maintenance system designed to estimate the Remaining Useful Life (RUL) of Li-ion batteries and detect faults in Brushless DC (BLDC) motors. Ultimately, this study aims to determine the most effective outlier detection method for UAV predictive maintenance datasets.