Abstract:The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

Today it is obvious that the existing linear model of the economy does not correlate with the principles of sustainable development. The circular economy model can replace the current linear economy whilst addressing the issues of environmental deterioration, social equity and long-term economic growth. In the context of effectively implementing circular economy objectives, particular importance should be attributed to wastewater treatment sludge management, due to the possibility of recovering valuable raw materials and using its energy potential. Anaerobic digestion is one of the methods of recovering energy from sewage sludge. The main goal of this study is to make a preliminary evaluation of possible sewage sludge biogas and biomethane solutions using a computation model called MCBioCH4 and compare its results with laboratory tests of sewage sludge fermentation from the northern wastewater treatment plant (WWTP) of Ekaterinburg (Russian Federation). Laboratory experiments were conducted to determine the volume and qualitative composition of biogas produced throughout anaerobic fermentation of raw materials coming from the WWTP. The specific productivity of samples ranged between 308.46 Nm3/tvs and 583.08 Nm3/tvs depending if mesophilic or thermophilic conditions were analyzed, or if the experiment was conducted with or without sludge pre-treatment. Output values from the laboratory were used as input for MCBioCH4 to calculate the flow of biogas or biomethane produced. For the case study of Ekaterinburg two possible energy conversion options were selected: B-H (biogas combustion with cogeneration of electrical and thermal energy) and M-T (biomethane to be used in transports). The results of the energy module showed a net energy content of the biogas between 6575 MWh/year and 7200 MWh/year. Both options yielded a favorable greenhouse gas (GHG) balance, meaning that avoided emissions are higher than produced emissions. The results discussion also showed that, in this case, the B-H option is preferable to the M-T option. The implementation of the biogas/biomethane energy conversion system in Ekaterinburg WWTP necessitates further investigations to clarify the remaining technical and economic aspects

This article reports on the EU project ExCITE with specific focus on the technical development of the telepresence platform over a period of 42 months. The aim of the project was to assess the robustness and validity of the mobile robotic telepresence (MRP) system Giraff as a means to support elderly people and to foster their social interaction and participation. Embracing the idea of user-centered product refinement, the robot was tested over long periods of time in real homes. As such, the system development was driven by a strong involvement of elderly people and their caregivers but also by technical challenges associated with deploying the robot in real-world contexts. The results of the 42-months’ long evaluation is a system suitable for use in homes rather than a generic system suitable, for example, in office environments.

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Climate change and anthropogenic pollution have put limited water resources under pressure. Lack of basic sanitation services as well as the discharge of improperly treated effluent from wastewater treatment plants (WWTP) result in the deposition of large amounts of organic matter and nutrients, which have major detrimental effects on health. Wastewater treatment (WWT) can reduce water pollution but at the cost of increasing energy consumption and the corresponding atmosphere and climate problems. Sustainable WWT management is a global challenge to preserve fresh water and decrease energy consumption. Nowadays it becomes obvious that existing WWTP operation model, based on the linear "take-make-dispose" pattern, is no longer sustainable. Furthermore, disposal of a product in landfill means that all residual energy is lost. The adoption of circular economy (CE) practices with its 3R principles of reducing, reusing and recycling material appears as a timely, relevant and practical option to meet the goals of sustainable development. WWTP is a critical element in CE implementation policy and to measure the degree of "circularity" there is a need for indicators. This study considers the holistic overview of measuring the progress of CE implementation at WWTP under 3R principles using life cycle analysis (LCA) and material flow analysis (MFA) frameworks. The paper presents the principles of CE indicators set construction using managerial approach. The proposed set of indicators and integral circularity index are studied under three scenarios, based on real performance of northern and southern WWTP in Ekaterinburg, Russia. This study provides an efficient assessment tool of CE progress, which is rather simple for calculation and interpretation and suitable for the use of wide range of stakeholders.

BackgroundEvidence from preclinical studies suggests that probiotics affect brain function via the microbiome-gut-brain axis, but evidence in humans remains limited.ObjectiveThe present proof-of-concept study investigated if a probiotic product containing a mixture of Bifidobacterium longum R0175, Lactobacillus helveticus R0052 and Lactiplantibacillus plantarum R1012 (in total 3 × 109 CFU/day) affected functional brain responses in healthy subjects during an emotional attention task.DesignIn this double-blinded, randomized, placebo-controlled crossover study (Clinicaltrials.gov, NCT03615651), 22 healthy subjects (24.2 ± 3.4 years, 6 males/16 females) were exposed to a probiotic intervention and a placebo for 4 weeks each, separated by a 4-week washout period. Subjects underwent functional magnetic resonance imaging while performing an emotional attention task after each intervention period. Differential brain activity and functional connectivity were assessed.ResultsAltered brain responses were observed in brain regions implicated in emotional, cognitive and face processing. Increased activation in the orbitofrontal cortex, a region that receives extensive sensory input and in turn projects to regions implicated in emotional processing, was found after probiotic intervention compared to placebo using a cluster-based analysis of functionally defined areas. Significantly reduced task-related functional connectivity was observed after the probiotic intervention compared to placebo. Fecal microbiota composition was not majorly affected by probiotic intervention.ConclusionThe probiotic intervention resulted in subtly altered brain activity and functional connectivity in healthy subjects performing an emotional task without major effects on the fecal microbiota composition. This indicates that the probiotic effects occurred via microbe-host interactions on other levels. Further analysis of signaling molecules could give possible insights into the modes of action of the probiotic intervention on the gut-brain axis in general and brain function specifically. The presented findings further support the growing consensus that probiotic supplementation influences brain function and emotional regulation, even in healthy subjects. Future studies including patients with altered emotional processing, such as anxiety or depression symptoms are of great interest.Clinical Trial Registration[http://clinicaltrials.gov/], identifier [NCT03615651].

F-formations are a set of possible patterns in which groups of people tend to spatially organize themselves while engaging in social interactions. In this paper, we study the behavior of teleoperators of mobile robotic telepresence systems to determine whether they adhere to spatial formations when navigating to groups. This work uses a simulated environment in which teleoperators are requested to navigate to different groups of virtual agents. The simulated environment represents a conference lobby scenario where multiple groups of Virtual Agents with varying group sizes are placed in different spatial formations. The task requires teleoperators to navigate a robot to join each group using an egocentric-perspective camera. In a second phase, teleoperators are allowed to evaluate their own performance by reviewing how they navigated the robot from an exocentric perspective. The two important outcomes from this study are, firstly, teleoperators inherently respect F-formations even when operating a mobile robotic telepresence system. Secondly, teleoperators prefer additional support in order to correctly navigate the robot into a preferred position that adheres to F-formations.