Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study

In this work we theoretically and experimentally analyze Multi-Agent Advantage Actor-Critic (MA2C) and Independent Advantage Actor-Critic (IA2C), two recently proposed multi-agent reinforcement learning methods that can be applied to control traffic signals in urban areas. The two methods differ in their use of a reward calculated locally or globally and in the management of agents' communication. We analyze the methods theoretically with the framework provided by non-Markov decision processes, which provides useful insights in the analysis of the algorithms. Moreover, we analyze the efficacy and the robustness of the methods experimentally by testing them in two traffic areas in the Bologna (Italy) area, simulated by SUMO, a software tool. The experimental results indicate that MA2C achieves the best performance in the majority of cases, outperforms the alternative method considered, and displays sufficient stability during the learning process.Comment: 41 pages, 16 figure

EVALUATION OF FUGITIVE DUST FROM CONSTRUCTION SITES IN THE CITY OF SHANGHAI

China’s cities are growing faster, more than in other countries. The presence of conspicuous number of construction yards can affect seriously the air quality of the cities, moreover the PM10 emissions from these sources are still underestimate. A monitoring campaign and model simulation results are presented in this paper. The aim of the project, conduct in the city of Shanghai, was to evaluate a dust emission factor from the constructions sites. A first assessment activity was developed from October to November 2006, in the Peng Xin Mansion construction site, where 8 PM10 sequential samplers and 2 meteorological towers, were deployed. The data collected were used to improve a new simplify methodology, also a Gaussian plume model AERMOD was rund. Results from the air dispersion model comes by different emission factors calculated from two procedures, one from the AP-42 Environmental Protection Agency (EPA-USA) and the second from an empirical areal emission factor. The first procedure assigns different sources depending on construction activities and in this case no good results were achieved. The reason was identify in the lack of a knowledge regarding the source locations depending on schedule time, the specific employed machinery and the detailed construction operations. The second procedure, based on the determination, from the measured data, of an areal emission factor, gave, for 6 selected days, good results regarding the trends and the values obtained comparing with the measured data. Considering the results obtained, we found for the construction site, one seasonal emission factor: the value is 1.8 g/(m2*sec) of PM10 emitted. At the end to better understand the role of the construction yards in the air quality budget in a city of Shanghai we use the estimated emission factor as input in the AERMOD model

Carbon Dioxide Removal with Tuff: Experimental Measurement of Adsorption Properties and Breakthrough Modeling Using CFD Approach

Abstract This work presents the study of tuff as an alternative material for CO 2 capturing and removal by pressure swing adsorption techniques. Tuff represents an economic and environmentally sustainable alternative to commonly-used synthetic zeolites. The proposed methodology includes a laboratory characterization of the CO 2 adsorption process under different operative conditions and experimental layouts. Measured data are also used to setup computational fluid dynamics simulations of the fixed-bed adsorption column. Results can be used to define optimal design parameters needed to implement and to improve different applications for biogas upgrading (CO 2 /CH 4 ratio adjustment) or carbon capture and storage

Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination

The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two approaches differing only by the first layer machinery, which, in one case, is instantiated as a fully-connected layer and, in the other case, results in a ConvNet equipped with kernels covering the whole input (wide-kernel ConvNet). A patchwise approach, tessellating training reference data in square patches, was adopted. Besides assessing the effectiveness of ConvNets with patched multispectral data, we analyzed how the information needed for classification spreads to patterns over convex sets of pixels. Our results show that: (a) with an F1-score of around 97% (5 x 5 patch size), ConvNets provides an excellent tool for patch-based pattern recognition with multispectral input data without requiring special feature extraction; (b) the information spreads over the limit of a single pixel: the performance of the network increases until 5 x 5 patch sizes are used and then ConvNet performance starts decreasing

Trends in social acceptance of renewable energy across Europe. A literature review

Social acceptance has proven to be a significant barrier in the implementation of renewable energy systems (hereinafter "RES"). While a general acceptance of RES is high, low local acceptance has hindered the development of renewable energy projects (hereinafter "REP"). This study assesses the determinants of local and general social acceptance of REP across Europe through a qualitative analysis from 25 case studies of the most significant social drivers and barriers that include all European countries. These case studies contain qualitative and quantitative analyses of the main factors for social acceptance of many representative groups including residents, stakeholders, and experts. Understanding the influences of social acceptance enables us to create strategies that will promote the development of REP by mitigating any public opposition

Assessment of energy, mobility, waste, and water management on Italian small islands

Small islands are recognized for their vulnerability to climate change. In this context, mitigation and adaptation policies are needed, but the ecological transition must be based on data. This study aims to assess the level of sustainability reached by 26 of the inhabited Italian small islands; it collects and analyzes the data and initiatives on the energy, mobility, waste, and water sectors and discusses the islands’ steps toward sustainability. The findings show that 18 of the 26 islands are not interconnected with the national grid and that the renewable sources cover less than 5% of the energy demand on 25 of the 26 islands. The number per capita of private vehicles reaches 90 cars per 100 inhabitants on three islands. The average of the separate collection of waste on the islands is 52%, which is far from the minimum recommended threshold of 65%. Pipelines or tankers on 17 of the 26 islands guarantee the water supply, and desalination plants are still not the rule, while the presence of wastewater treatment has been detected on 12 islands, and it often provides only partial treatment. An ambitious multi-stakeholder sustainability plan for each island should be developed to overcome the typical barriers of the island and to increase the building capacity in order to use economic incentives for that goal

The Bortoluzzi Mud Volcano (Ionian Sea, Italy) and its potential for tracking the seismic cycle of active faults

The Ionian Sea in southern Italy is at the center of active interaction and convergence between the Eurasian and African–Adriatic plates in the Mediterranean. This area is seismically active with instrumentally and/or historically recorded Mw > 7:0 earthquakes, and it is affected by recently discovered long strike-slip faults across the active Calabrian accretionary wedge. Many mud volcanoes occur on top of the wedge. A recently discovered one (called the Bortoluzzi Mud Volcano or BMV) was surveyed during the Seismofaults 2017 cruise (May 2017). Bathymetric backscatter surveys, seismic reflection profiles, geochemical and earthquake data, and a gravity core are used here to geologically, geochemically, and geophysically characterize this structure. The BMV is a circular feature ' 22m high and ' 1100m in diameter with steep slopes (up to a dip of 22 ). It sits atop the Calabrian accretionary wedge and a system of flowerlike oblique-slip faults that are probably seismically active as demonstrated by earthquake hypocentral and focal data. Geochemistry of water samples from the seawater column on top of the BMV shows a significant contamination of the bottom waters from saline (evaporite-type) CH4-dominated crustalderived fluids similar to the fluids collected from a mud volcano located on the Calabria mainland over the same accretionary wedge. These results attest to the occurrence of open crustal pathways for fluids through the BMV down to at least the Messinian evaporites at about 3000 m. This evidence is also substantiated by helium isotope ratios and by comparison and contrast with different geochemical data from three seawater columns located over other active faults in the Ionian Sea area. One conclusion is that the BMV may be useful for tracking the seismic cycle of active faults through geochemical monitoring. Due to the widespread diffusion of mud volcanoes in seismically active settings, this study contributes to indicating a future path for the use of mud volcanoes in the monitoring and mitigation of natural hazards.Published1-233SR TERREMOTI - Attività dei CentriJCR Journa

RETRACE-3D PROJECT, a multidisciplinary approach for the construction of a 3D crustal model: first results and seismotectonic implications

The RETRACE-3D (centRal italy EarThquakes integRAted Crustal modEl) Project has been launched with the ambitious goal to build, as first result, a new, robust, 3D geological model of broad consensus of the area struck by the 2016-2018 Central Italy seismic sequencePublishedBologna3T. Sorgente sismica4T. Sismicità dell'Itali