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The direct-on-line start-up of a synchronous motor results in alternating torques which can excite the lowest torsional natural frequency of compressor trains used in the oil & gas industry. Considering a slip-dependent variation of the rotor parameters, an integrated electrical and mechanical simulation model is developed, providing an accurate estimation of the air-gap torque. The proposed approach is validated by an accurate testing campaign carried out during moto-compressor field test activities.

Climate change and environmental degradation present significant threats globally. The various sources report on different climate records. The European Green Deal aims to counteract these challenges by achieving zero emissions by 2050, with a significant focus on citizen involvement. Citizen science initiatives play a critical role in engaging the public in climate action through data collection and monitoring of environmental impacts. Technological tools, such as dashboards, facilitate this involvement by visualizing complex climate data, raising awareness, and bridging the knowledge-action gap. This paper discusses a dashboard (developed within the Horizon 2020 project I-CHANGE) designed to engage citizens in environmental data collection and analysis. The Dashboard, co-designed with scientists and stakeholders, supports citizens in viewing, understanding, and interpreting data collected with low-cost sensors and through crowdsourcing activities. The paper gives an overview of the Dashboard's design, implementation, data integration, and its role in fostering public engagement and environmental consciousness.

Abstract From the sustainable perspective, the optimum thickness calculations of the buildings envelope insulation materials published in scientific journals suffer a number of notable shortcomings. The most relevant are the short amortization time periods and the prevalence of economic criterion. The work presented shows that an increase from 20 to 50 years in the amortization time period involves, in some cases, to double the value of the optimum thickness. Moreover, the thicknesses calculated applying energetic or environmental criteria for the optimization give, in some cases, results 10 times higher than the ones obtained using the economic argument. The type of insulation materials (especially their different characteristics at the manufacturing stage) and the calculation conditions (e.g. Degree-Days zone) also affect optimum thicknesses determination.

In the last years, the concept of “smart city” has been the subject of increasing attention in urban planning and governance due to the sustainability challenges and great technological advancements. Although the relevant literature highlights those smart cities can create a fertile environment to drive innovation from a technological, managerial and organizational, and policy point of view how smart cities function, in terms of their value chain and in light of sustainability and circularity is still an open question. Based on these considerations, this work aims to investigate how smart cities deliver value by combining environmental, societal, and financial priorities to re-imagine their core business models and shift the boundaries of urban competition in light of circular economy principles. To reach this goal, the tool Sustainable Business Model Innovation Canvas has been applied in the urban context with the aim to create and leverage an environmental and societal surplus in light of circularity in the smart city framework. Preliminary results shed light on new vistas on the future and new challenges of smart cities particularly relevant for facing the current complexity of the economy, social, and environmental changes.

In order to make adequate projections on the consequences of climate change stressors on marine organisms, it is important to know how impacts of these stressors are affected by the presence of other species. Here we assessed the direct effects of ocean warming (OW) and acidification (OA) along with non-consumptive effects (NCEs) of a predatory crab and/or a predatory snail on the habitat-forming mussel Perumytilus purpuratus. Mussels were exposed for 10-14 weeks to contrasting pCO2 (500 and 1400 μatm) and temperature (15 and 20 °C) levels, in the presence/absence of cues from one or two predator species. We compared mussel traits at sub-organismal (nutritional status, metabolic capacity-ATP production-, cell stress condition via HSP70 expression) and organismal (survival, oxygen consumption, growth, byssus biogenesis, clearance rates, aggregation) levels. OA increased the mussels' oxygen consumption; and OA combined with OW increased ATP demand and the use of carbohydrate reserves. Mussels at present-day pCO2 levels had the highest protein content. Under OW the predatory snail cues induced the highest cell stress condition on the mussels. Temperature, predator cues and the interaction between them affected mussel growth. Mussels grew larger at the control temperature (15 °C) when crab and snail cues were present. Mussel wet mass and calcification were affected by predator cues; with highest values recorded in crab cue presence (isolated or combined with snail cues). In the absence of predator cues in the trails, byssus biogenesis was affected by OA, OW and the OA × OW and OA × predator cues interactions. At present-day pCO2 levels, more byssus was recorded with snail than with crab cues. Clearance rates were affected by temperature, pCO2 and the interaction between them. The investigated stressors had no effects on mussel aggregation. We conclude that OA, OW and the NCEs may lead to neutral, positive or negative consequences for mussels.

Abstract This work reports a detailed experimental study that is aimed at investigating the Thermal Energy Storage (TES) performance of cementitious systems containing Microencapsulated Phase Change Materials (MPCMs). New spherical-shaped test specimens for TES measurements were produced following an innovative casting technique, developed at the Institut fur Werkstoffe im Bauwesen – TU Darmstadt. Three water-to-cement ratios and three MPCMs volume fractions, leading to a total of nine different mixtures, were investigated. The thermal experiments were accompanied by mechanical tests to observe the effect MPCMs have on the resulting strengths in both compression and bending. The analysis and discussion of the TES results are employed for calibrating an enthalpy-based model. The experimental data have been applied to evaluate the corresponding temperature-based material parameters like specific heat, conductivity, or more in general, the energy storage capacity of a system under transient heat conduction conditions.

Classification of time series is a fundamental problem in energy distribution, especially to extract information about events that occurred during the observation period. In this paper, we propose a solution to the problem of identifying energy thefts by introducing a classification method based on convolutional neural networks. The input structure to the model is based on real data that have been certified by external authorities and regards thefts operated by the final user with physical intervention. The training of the neural network is done by means of yearly time series of monthly data, which pertain to different physical quantities relevant to the user profile. The proposed method has been experimentally tested and verified against acceptable test results in different conditions, even giving an indication on where in the sequence the theft has occurred.

Solar Energy Materials and Solar Cells, 116 + (2013) 176-181. doi:10.1016/j.solmat.2013.04.019