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New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines. [ABSTRACT FROM AUTHOR]

Buildings, energy, and the environment are key issues facing construction around the world. The energy efficiency of buildings is a key topic when it comes to reducing the world's energy consumption, releasing harmful gases, and global climate change, as they consume about 40% of the world's energy supplies. Heat losses in buildings reduce the energy performance of buildings and are basically important to them. In the paper, the authors focus on the main problems related to heat losses generated by chimney systems, which are inseparable equipment of building structures, resulting in lower energy efficiency and, at the same time, technical efficiency and durability of the building partitions themselves. Authors present thermal imaging with its contribution to the detection of heat losses, thermal bridges, insulation problems, and other performance disturbances, and then verifications using appropriate simulation models. The mathematical apparatus of artificial neural networks was implemented to predict the temperature distributions on the surfaces of prefabricated chimney solutions. In Europe, we can often find a large building substance equipped with traditional chimneys, which disrupts the current trend of striving to reduce energy consumption, especially that derived from fossil fuels. Speaking of energy-efficient buildings, one should not ignore those that, without additional security and modern installations, are constantly used in a very wide range. Therefore, the article deals with an essential problem that is not perceived in design studies and during the operation period as having a basis in incorrect architectural solutions and which can be easily eliminated. It concerns the cooling of internal partitions of buildings on their last storeys, in places where chimneys are located, regardless of their function. The authors of the paper decided to take a closer look at this phenomenon, which may allow the limiting of its effects and at the same time reduce its impact on the energy performance of technologically older buildings. [ABSTRACT FROM AUTHOR]

Water scarcity and quality deterioration, driven by rapid population growth, urbanization, and intensive industrial and agricultural activities, emphasize the urgency for effective water management. This study aims to develop a model to comprehensively monitor various water quality parameters (WQP) and evaluate the feasibility of implementing this model in real-world scenarios, addressing the limitations of conventional in-situ sampling. Thus, a comprehensive model for monitoring WQP was developed using a 38-year dataset of Landsat imagery and in-situ data from the Water Information System of Europe (WISE), employing Back-Propagated Artificial Neural Networks (ANN). Correlation analyses revealed strong associations between remote sensing data and various WQPs, including Total Suspended Solids (TSS), chlorophyll-a (chl-a), Dissolved Oxygen (DO), Total Nitrogen (TN), and Total Phosphorus (TP). Optimal band combinations for each parameter were identified, enhancing the accuracy of the WQP estimation. The ANN-based model exhibited very high accuracy, particularly for chl-a and TSS (R2 > 0.90, NRMSE < 0.79%), surpassing previous studies. The independent validation showcased accurate classification for TSS and TN, while DO estimation faced challenges during high variation periods, highlighting the complexity of DO dynamics. The usability of the developed model was successfully tested in a real-case scenario, proving to be an operational tool for water management. Future research avenues include exploring additional data sources for improved model accuracy, potentially enhancing predictions and expanding the model's utility in diverse environmental contexts. [ABSTRACT FROM AUTHOR]

The widespread tree mortality caused by the European spruce bark beetle (Ips typographus L.) is a significant concern for Norway spruce-dominated (Picea abies H. Karst) forests in Europe and there is evidence of increases in the affected areas due to climate warming. Effective forest monitoring methods are urgently needed for providing timely data on tree health status for conducting forest management operations that aim to prepare and mitigate the damage caused by the beetle. Unoccupied aircraft systems (UASs) in combination with machine learning image analysis have emerged as a powerful tool for the fast-response monitoring of forest health. This research aims to assess the effectiveness of deep neural networks (DNNs) in identifying bark beetle infestations at the individual tree level from UAS images. The study compares the efficacy of RGB, multispectral (MS), and hyperspectral (HS) imaging, and evaluates various neural network structures for each image type. The findings reveal that MS and HS images perform better than RGB images. A 2D-3D-CNN model trained on HS images proves to be the best for detecting infested trees, with an F1-score of 0.759, while for dead and healthy trees, the F1-scores are 0.880 and 0.928, respectively. The study also demonstrates that the tested classifier networks outperform the state-of-the-art You Only Look Once (YOLO) classifier module, and that an effective analyzer can be implemented by integrating YOLO and the DNN classifier model. The current research provides a foundation for the further exploration of MS and HS imaging in detecting bark beetle disturbances in time, which can play a crucial role in forest management efforts to combat large-scale outbreaks. The study highlights the potential of remote sensing and machine learning in monitoring forest health and mitigating the impacts of biotic stresses. It also offers valuable insights into the effectiveness of DNNs in detecting bark beetle infestations using UAS-based remote sensing technology. [ABSTRACT FROM AUTHOR]

Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the European Green Deal and the Fit for 55 package. Furthermore, these systems are associated with a lower short circuit power as compared with distribution grid traditional power plants. The power quality parameters (PQPs) of such small-scale off-grid systems are largely determined by the inverter's ability to handle the impact of a device; however, this makes it difficult to accurately forecast the PQPs. To address this issue, this work compared prediction models for the PQPs as a function of the meteorological conditions regarding the off-grid systems for small-scale households in Central Europe. To this end, seven models—the artificial neural network (ANN), linear regression (LR), interaction linear regression (ILR), quadratic linear regression (QLR), pure quadratic linear regression (PQLR), the bagging decision tree (DT), and the boosting DT—were considered for forecasting four PQPs: frequency, the amplitude of the voltage, total harmonic distortion of the voltage ( T H D u ), and current ( T H D i ). The computation times of these forecasting models and their accuracies were also compared. Each forecasting model was used to forecast the PQPs for three sunny days in August. As a result of the study, the most accurate methods for forecasting are DTs. The ANN requires the longest computational time, and conversely, the LR takes the shortest computational time. Notably, this work aimed to predict poor PQPs that could cause all the equipment in off-grid systems to respond in advance to disturbances. This study is expected to be beneficial for the off-grid systems of small households and the substations included in existing smart grids. [ABSTRACT FROM AUTHOR]

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst. [ABSTRACT FROM AUTHOR]

Taper functions are important tools for forest description, modelling, assessment, and management. A large number of studies have been conducted to develop and improve taper functions; however, few review studies have been dedicated to addressing their development and parameters. This review summarises the development of taper functions by considering their parameterisation, geographic and species-specific limitations, and applications. This study showed that there has been an increase in the number of studies of taper function and contemporary methods have been developed for the establishment of these functions. The reviewed studies also show that taper functions have been developed from simple equations in the early 1900s to complex functions in modern times. Early taper functions included polynomial, sigmoid, principal component analysis (PCA), and linear mixed functions, while contemporary machine learning (ML) approaches include artificial neural network (ANN) and random forest (RF). Further analysis of the published literature also shows that most of the studies of taper functions have been carried out in Europe and the Americas, meaning most taper equations are not specifically applicable to tropical tree species. Developing well-conditioned taper functions requires reducing the variation due to species, measurement techniques, and climatic conditions, among other factors. The information presented in this study is important for understanding and developing taper functions. Future studies can focus on developing better taper functions by incorporating emerging remote sensing and geospatial datasets, and using contemporary statistical approaches such as ANN and RF. [ABSTRACT FROM AUTHOR]

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively. [ABSTRACT FROM AUTHOR]

Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk assessment process and therefore significantly lower the costs associated with the risk assessment process. Future research may be oriented towards developing a comprehensive risk management system, which will enable dynamic risk assessment taking into account various combinations of risk mitigation actions. [ABSTRACT FROM AUTHOR]

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid. [ABSTRACT FROM AUTHOR]