Abstract
The volatile organic compounds benzene, toluene, ethylbenzene, and xylene (BTEX) are emitted into the atmosphere at gas stations (GS) leading to chronic exposure of nearby residents, which raises public health concerns. This study aimes at determining the contribution of GS emissions to BTEX exposure in nearby residents. Three Control and Exposed areas to BTEX emissions from GS were defined in a medium-sized European city (Porto, Portugal). BTEX atmospheric levels were determined in Control and Exposed areas using passive samplers deployed outdoors (n = 48) and indoors (n = 36), and human exposure was estimated for 119 non-smoking residents using the first urine of the day. Results showed that median BTEX outdoor and indoor concentrations were significantly higher for Exposed than Control areas, with exception of ethylbenzene and xylene indoor concentrations, where no marked differences were found. Comparison of urinary concentrations between Exposed and Control residents demonstrated no significant differences for benzene and ethylbenzene, whereas levels of toluene and xylene were significantly higher in Exposed residents. No marked correlation was obtained between atmospheric BTEX concentrations and urinary concentrations. Data indicate the potential impact on air quality of BTEX emissions from GS, which confirms the importance of these findings in urban planning in order to minimize the impact on health and well-being of surrounding populations.

Abstract
The integration of micro-generation (µG) in distribution networks faces new challenges concerning the technical as well as commercial management. The µG integration in the Low and medium voltage distribution networks has many advantages for the grid operation, such as voltage profiles improvement, power losses reduction, and branches congestion levels reduction. This paper presents a method for guiding continuation power flow simulation of integrating µG on distribution feeders. A base model is designed with variable capacitor bank, µG unit such as PV and Wind generation are integrated. A control method is used to improve the voltage level of each node as well as improving power factor of the systems. The electricity consumption of a university's substation area where commercial, residential and municipal load are presented are modeled using actual data collected from each single residential hall and commercial buildings. This model allows analyzing the power flow and voltage profile along each distribution feeders on continuing fashion for a 24- hour period at hour-by-hour formulation. By dividing the feeder into load zones based on distance from each load node to distribution feeder head, the impact of integration of different µG operation in different condition has been discussed. © 2019 IEEE.

Abstract

Abstract
Industrial and commercial electricity customers have significant potential in providing flexibility for power systems through diverse demand response (DR) programs. However, the industrial and commercial potential of DR is not yet completely understood, especially regarding the emerging and advanced technologies associated with the smart grid. Advances in smart meter technology that allow monitoring and controlling responsive loads in real time will also be key enablers of DR potential. It can be more complex to implement DR for industrial loads if compared to residential loads mainly due to the reliability management that is more vital for industrial plants. Hence, this paper aims at providing a comprehensive review of the most recent advances on industrial and commercial DR. On this basis, this survey first presents the potential and technologies of DR in industrial and commercial sectors. Then, the existing models of DR in the mentioned sectors are presented. The presence of industrial and commercial DR in electricity markets is also investigated. Finally, the main positive and beneficial aspects, as well as challenges and barriers of industrial and commercial DR, are investigated.

Abstract
This paper describes a group of robots for cleaning a simulated environment and proposes an efficient algorithm for navigation based on Pathfinding A *. No need for vision sensors. As a result it was observed that the robots can work cooperatively to clear the ground and that the navigation algorithm is effective in cleaning. In order to test its efficiency it was compared the combination of the Pathfinding A* algorithm and the decision algorithm proposed in this paper with Pathfinding A* and Euclidean distance, resulted in an improvement in time and distance traveled. © CLAWAR Association.

Abstract
This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. Different temporal modeling schemes are applied to the output of the proposed neural network, which induce the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). In particular, convolutional neural networks are used in conjunction with underlying hidden Markov models and hidden semi-Markov models to infer emission distributions. The proposed approaches are tested on heart sound signals from the publicly available PhysioNet dataset, and they are shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.9 and an average positive predictive value of 94 in detecting S1 and S2 sounds.