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Historical exploitation of the Mediterranean Sea and the absence of rigorous baselines makes it difficult to evaluate the current health of the marine ecosystems and the efficacy of conservation actions at the ecosystem level. Here we establish the first current baseline and gradient of ecosystem structure of nearshore rocky reefs at the Mediterranean scale. We conducted underwater surveys in 14 marine protected areas and 18 open access sites across the Mediterranean, and across a 31-fold range of fish biomass (from 3.8 to 118 g m(-2)). Our data showed remarkable variation in the structure of rocky reef ecosystems. Multivariate analysis showed three alternative community states: (1) large fish biomass and reefs dominated by non-canopy algae, (2) lower fish biomass but abundant native algal canopies and suspension feeders, and (3) low fish biomass and extensive barrens, with areas covered by turf algae. Our results suggest that the healthiest shallow rocky reef ecosystems in the Mediterranean have both large fish and algal biomass. Protection level and primary production were the only variables significantly correlated to community biomass structure. Fish biomass was significantly larger in well-enforced no-take marine reserves, but there were no significant differences between multi-use marine protected areas (which allow some fishing) and open access areas at the regional scale. The gradients reported here represent a trajectory of degradation that can be used to assess the health of any similar habitat in the Mediterranean, and to evaluate the efficacy of marine protected areas.

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.

Environment preservation, energy, and the growing economy are becoming strongly interconnected themes requiring new solutions to be exploited. An example of this interconnection is the demand for the development of almost zero-energy buildings, i.e. buildings capable to be almost autonomous from external energy supply or at least not dependent on the energy supply from utilities. The actual conception of a zero energy building is a very complex system formed by several subsystems, with the consequence that costs are very high and reliability relatively low. The aim of this research program is to deepen the possibility to employ the Stirling engine and cooler technology to lower the number of components required in a near zero-energy building, increase the efficiency, and contemporary raise the reliability of the overall system. Stirling cooler could be used to convert mechanical work into heating and cooling effects and produce the temperature difference by the expanding and compressing the working fluid. A similar concept of the Stirling cooler could also be adopted to develop a heat pump. Compared to the traditional vaporcompression refrigeration systems, the Stirling coolers are of higher efficiency and with no components like compressor, expansion valve, evaporator, or condensers. Therefore, they are considered to be clean cooling devices. On the other hand, the Stirling engine is an external combustion engine, which is compatible with a variety of thermal sources, such as solar radiation, waste heat, geothermal energy, combustion, and so on. With the heat input to the hot end of the engine, the Stirling engine could be operated to produce mechanical work/electricity at high thermal efficiency. In principle, the Stirling machines are capable to provide all the forms of energy (heat, cool, and electricity) that form the almost total energy load of a building.

Abstract Building energy performance requirements aiming at the nZEB target have been recently established by the national legislation. In Italy, the requirements are verified through the notional reference building, whose U-values are reduced in two steps: up to 2018 and since 2019 for public buildings, and up to 2020 and since 2021 for all other buildings. This might cause a reduction of the heating need but an increase of the cooling need. The objective of the study is to investigate in which conditions and extent a significant imbalance of the energy needs occurs. Different building types and climatic zones are considered.

European physical journal special topics 228(2), 261-623 (2019). doi:10.1140/epjst/e2019-900045-4 Published by Springer, Berlin ; Heidelberg