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.

INTRODUCTION A challenge in the treatment of patients with head and neck cancer is the management of occult cervical lymph node (LN) metastases. Single-fiber reflectance (SFR) spectroscopy has the potential to detect physiological tissue changes that occur in a positive LN. This pilot study aimed to investigate whether SFR spectroscopy could serve as an alternative or additional technique to detect cervical lymph node metastases. MATERIALS AND METHODS We performed intraoperative SFR spectroscopy measurements of LNs with and without malignancies. We analyzed if physiological and scattering parameters were significantly altered in positive LNs. RESULTS Nine patients with a total of nineteen LNs were included. Three parameters, blood volume fraction (BVF), microvascular saturation (StO2), and Rayleigh amplitude, were significantly lower in positive LNs. They were combined into one optical parameter 'delta', using discriminant analysis. Delta was significantly decreased in positive LNs, p = 0,0006. It had a high diagnostic accuracy where the sensitivity, specificity, PPV, and NPV were 90,0%, 88.9%, 90,0%, and 88.9%, respectively. The area under the ROC curve was 96.7% (95% confidence interval 89.7-100.0%). CONCLUSION This proof of principle study is a first step in the development of an SFR spectroscopy technique to detect LN metastases in real time. A next step towards this goal is replicating these results in LNs with smaller metastases and in a larger cohort of patients. This future study will combine SFR spectroscopy with fine-needle aspiration, using the same needle, to perform preoperative in vivo measurements.

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.

Electrochemical reduction of thin graphene-oxide films in aqueous solutions: restoration of conductivity Dalibor Karačić1, Sanjin J. Gutić2, Borislav Vasić3, Vladimir M. Mirsky4, Natalia V. Skorodumova5, Slavko V. Mentus1,6, Igor A. Pašti1,5* 1 University of Belgrade – Faculty of Physical Chemistry, Belgrade, Serbia 2 University of Sarajevo, Faculty of Science, Department of Chemistry, Sarajevo, Bosnia and Herzegovina 3 Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia 4 Institute of Biotechnology, Department of Nanobiotechnology, Brandenburgische Technische Universität Cottbus-Senftenberg, Germany 5 Department of Materials Science and Engineering, School of Industrial Engineering and Management, KTH – Royal Institute of Technology, Stockholm, Sweden 6 Serbian Academy of Sciences and Arts, Belgrade, Serbia