Positive energy districts (PEDs) are seen as a promising pathway to facilitating energy transition. PEDs are urban areas composed of different buildings and public spaces with local energy production, where the total annual energy balance must be positive. Urban areas consist of a mix of different buildings, such as households and service sector consumers (offices, restaurants, shops, cafes, supermarkets), which have a different annual energy demand and production, as well as a different consumption profile. This paper presents a data modeling approach to estimating the annual energy balance of different types of consumer categories in urban areas and proposes a methodology to extrapolate energy demands from specific building types to the aggregated level of an urban area and vice versa. By dividing an urban area into clusters of different consumer categories, depending on parameters such as surface area, building type and energy interventions, energy demands are estimated. The presented modeling approach is used to model and calculate the energy balance and CO2 emissions in two PED areas of the City of Groningen (The Netherlands) proposed in the Smart City H2020 MAKING CITY project.

The paper proposes a novel computing and net-working framework that can be implemented for the realization of different disaster management applications or real-time surveillance. The framework is based on networks of unmanned aerial vehicles (UAVs) equipped with different sensors including cameras. The framework represents a holistic approach that exploits the distributed architecture of clusters of UAVs and cloud computing resources located on the ground. The proposed framework is characterized by the hierarchical organization among framework elements. In such a framework, each UAV is assumed to be fully autonomous and locally implements a state-of-the-art deep learning algorithms for real-time route planning, obstacle avoidance and object detection on aerial images. The main operating modules of the proposed framework have been presented, with the emphasis on the improvements which the proposed framework can bring in terms of event detection time and accuracy, energy consumption and reliability of application in disaster management systems. The proposed framework can serve as the foundation for the development of more reliable, faster in terms of disaster event detection and energy-efficient disaster management systems based on UAV networks.

This paper considers the application of machine learning models to electric field intensity and magnetic flux density estimation in the proximity of the overhead transmission lines. The machine learning models are applied on two horizontal overhead transmission line configurations at different rated voltages, at height 1 m above ground surface. The obtained results are compared with the results obtained by charge simulation method and Biot-Savart law based method as well as with the field measurement results.

In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramér-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.

One of the most critical factors for a successful road trip is a high degree of alertness while driving. Even a split second of inattention or sleepiness in a crucial moment, will make the difference between life and death. Several prestigious car manufacturers are currently pursuing the aim of automated drowsiness identification to resolve this problem. The path between neuro-scientific research in connection with artificial intelligence and the preservation of the dignity of human individual’s and its inviolability, is very narrow. The key contribution of this work is a system of data analysis for EEGs during a driving session, which draws on previous studies analyzing heart rate (ECG), brain waves (EEG), and eye function (EOG). The gathered data is hereby treated as sensitive as possible, taking ethical regulations into consideration. Obtaining evaluable signs of evolving exhaustion includes techniques that obtain sleeping stage frequencies, problematic are hereby the correlated interference’s in the signal. This research focuses on a processing chain for EEG band splitting that involves band-pass filtering, principal component analysis (PCA), independent component analysis (ICA) with automatic artefact severance, and fast fourier transformation (FFT). The classification is based on a step-by-step adaptive deep learning analysis that detects theta rhythms as a drowsiness predictor in the pre-processed data. It was possible to obtain an offline detection rate of 89% and an online detection rate of 73%. The method is linked to the simulated driving scenario for which it was developed. This leaves space for more optimization on laboratory methods and data collection during wakefulness-dependent operations.

Pineapple (Ananas comosus (L.) Merril), one of the major fruit crops, is mainly used for raw consumption and for industrial juice production, which creates large amounts of residues. The United Nations Food and Agriculture Organization (FAO) has estimated that pineapple waste accounts for between 50 to 65 % of the total weight of the fruit. Industrial pineapple waste is a major source of pollution as important quantities of primary residues are not further processed. Pineapple waste contains bioactive compounds such as carotenoids, polyphenols, fibers, vitamins, enzymes, and essential oils. These phytochemicals can be used in the food industry, medicine and pharmacy, textile, and others. This review highlights essential oil and other bioactive compounds extracted from pineapple waste and the composition of pineapple essential oil. Pineapple peels are the potential raw material for essential oil extraction through various methods. Modern spectrometric methods have shown that essential oil extracted from pineapple waste comprises esters, alcohols, aldehydes, and ketones. From this overview, it can be concluded that there is an important need for further research into pineapple waste as a potential source of valuable byproducts, as well as new techniques to studying industrial organic residuals to achieve higher recovery rates of valuable bioactive compounds used in pharmaceuticals, cosmetic and chemical industries as well as for developing new functional foods.

One way to improve a structure’s total load-bearing capacity during an earthquake is to apply fiber-reinforced polymers (FRP) to unreinforced walls. The study discusses the use of FRP to strengthen unreinforced masonry (URM) structures. Although, many studies were conducted on the FRP strengthening of URM buildings, most of them were experiments to investigate the success of retrofitting approaches, rather than developing a successful design model. A database of 120 FRP-reinforced wall samples was created based on the current literature. Various approaches for calculating the bearing capacity of FRP-reinforced masonry are presented and detailed. The findings of the experiments, which were compiled into a database, were compared to those derived using formulas from the literature and/or building codes, and the model’s limitations are discussed.

UDK: 577.212:574]:007.5(497.6) DNA barcoding is a method designed to provide rapid and precise species identifications by using one or more of short gene sequences called barcodes. In most plant and fungi studies, the standard barcodes of choice are three plastid (rbcL, matK and trnH-psbA) and one nuclear (ITS) gene regions. The relatively high, but comparatively conserved rate of sequence evolution of mtDNA has made COI the marker of choice in animals. BOLD is a freely available cloud-based data storage and analysis platform developed with the aim to advance biodiversity science through DNA barcoding species identification. To date, over 6 million barcodes have been deposited in BOLD with 196,000 animal species, 68,000 plant species and 22,000 species of fungi and other organism entries. In this database, there are currently 447 entries for organisms from Bosnia and Herzegovina, which makes 0.0067% of the total number of BOLD entries. According to BOLD statistics, only 1.11% of all organism entries from B&H were submitted by B&H institutions. Despite the fact that Bosnia and Herzegovina has valuable natural resources with a high percentage of endemic and autochthonous species, BOLD statistics elucidated the lack of coordinated and systematic DNA barcoding research so far. It is necessary to establish continuous progress of molecular-genetic characterization of these resources in the future. It is up to B&H institutions to decide if they want to continue the practice of submitting the data sporadically or if they will animate the research community to actively participate in this global project.