Wearable devices and smartphone applications have allowed for the utilization of different at-home treatments. Biofeedback is a mind-body technique that enables users to self-regulate the responses of the autonomic nervous system. This paper has conducted a proof-of-concept study to test multimodal biofeedback treatment with smartphone application and custom-made wearable sensor. While contact-based measurements included skin temperature and skin conductance from the sensor, the smartphone's front camera recorded the patient's face to estimate cardiovascular parameters such as heart rate and heart rate variability. The tested individual completed five biofeedback treatments at home, with activation stress exercises before and after a 5-day experiment. The obtained results show increased finger's skin temperature and heart rate variability during biofeedback sessions, indicating the successful biofeedback treatment.

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.

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.

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.

Aim of the study Lateral episiotomy is a widely used procedure, although it is rarely mentioned in the literature and its effects on the pelvic floor are largely unexplored. The purpose of this study is to evaluate the impact of lateral episiotomy on the incidence of urinary incontinence (UI) after vaginal delivery in primiparas. Material and methods The study design is a prospective cohort study. The primiparas were divided into two groups. The first group consisted of women who gave birth with lateral episiotomy, while the second group included women who gave birth with an intact perineum or with perineal tears of first and second degree. Assessments of UI were performed at 5 and 8 months after childbirth using the International Consultation on Incontinence Questionnaire – Short Form (ICIQ-SF) questionnaire followed by the stress test. Results The results revealed no significant differences (p > 0.05) in emergence of stress urinary incontinence (SUI) between the groups at the two time points. There were no statistically significant differences in overall rate of UI, urge urinary incontinence (UUI), or mixed urinary incontinence according to the ICIQ-SF questionnaire. The overall incontinence rate on the first examination was 24% in the episiotomy group and 36% in the perineal laceration group, although the difference was not statistically significant (p = 0.064). On the second examination, rates were similar and without a statistically significant difference. Conclusions Lateral episiotomy has a neutral effect on the onset of UI in primiparous women in the first year after delivery.

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 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.

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.