A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

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

Quantum Cosmology and Open Universes

Quantum creation of Universes with compact spacelike sections that have curvature $k$ either closed, flat or open, i.e. $k=\pm1,0$ are studied. In the flat and open cases, the superpotential of the Wheeler De Witt equation is significantly modified, and as a result the qualitative behaviour of a typical wavefunction differs from the traditional closed case. Using regularity arguments, it is shown that the only consistent state for the wavefunction is the Tunneling one. By computing the quantum probabilities for the curvature of the sections, it is shown that quantum cosmology actually favours that the Universe be open, $k=-1$. In all cases sufficient inflation $\sim 60$ e-foldings is predicted: this is an improvement over classical measures that generally are ambiguous as to whether inflation is certain to occur.Comment: 11 pages, Revtex, 7 figures. Accepted for publication in PRD. New material and important corrections added in response to referee's repor

The impact of COVID-19 on the management of European protected areas and policy implications

The COVID-19 pandemic led to many European countries imposing lockdown measures and limiting people’s movement during spring 2020. During the summer 2020, these strict lockdown measures were gradually lifted while in autumn 2020, local restrictions started to be re-introduced as a second wave emerged. After initial restrictions on visitors accessing many Nature Protected Areas (PAs) in Europe, management authorities have had to introduce measures so that all users can safely visit these protected landscapes. In this paper, we examine the challenges that emerged due to COVID-19 for PAs and their deeper causes. By considering the impact on and response of 14 popular European National and Nature Parks, we propose tentative longer-term solutions going beyond the current short-term measures that have been implemented. The most important challenges identified in our study were overcrowding, a new profile of visitors, problematic behavior, and conflicts between different user groups. A number of new measures have been introduced to tackle these challenges including information campaigns, traffic management, and establishing one-way systems on trail paths. However, measures to safeguard public health are often in conflict with other PA management measures aiming to minimize disturbance of wildlife and ecosystems. We highlight three areas in which management of PAs can learn from the experience of this pandemic: managing visitor numbers in order to avoid overcrowding through careful spatial planning, introducing educational campaigns, particularly targeting a new profile of visitors, and promoting sustainable tourism models, which do not rely on large visitor numbers.European Research Council (ERC) under the European Union’s Horizon 2020 research programme (Project FIDELIO, grant agreement no. 802605)

Biophilic architecture: a review of the rationale and outcomes

Contemporary cities have high stress levels, mental health issues, high crime levels and ill health, while the built environment shows increasing problems with urban heat island effects and air and water pollution. Emerging from these concerns is a new set of design principles and practices where nature needs to play a bigger part called “biophilic architecture”. This design approach asserts that humans have an innate connection with nature that can assist to make buildings and cities more effective human abodes. This paper examines the evidence for this innate human psychological and physiological link to nature and then assesses the emerging research supporting the multiple social, environmental and economic benefits of biophilic architecture

Loop Quantum Cosmology

Quantum gravity is expected to be necessary in order to understand situations where classical general relativity breaks down. In particular in cosmology one has to deal with initial singularities, i.e. the fact that the backward evolution of a classical space-time inevitably comes to an end after a finite amount of proper time. This presents a breakdown of the classical picture and requires an extended theory for a meaningful description. Since small length scales and high curvatures are involved, quantum effects must play a role. Not only the singularity itself but also the surrounding space-time is then modified. One particular realization is loop quantum cosmology, an application of loop quantum gravity to homogeneous systems, which removes classical singularities. Its implications can be studied at different levels. Main effects are introduced into effective classical equations which allow to avoid interpretational problems of quantum theory. They give rise to new kinds of early universe phenomenology with applications to inflation and cyclic models. To resolve classical singularities and to understand the structure of geometry around them, the quantum description is necessary. Classical evolution is then replaced by a difference equation for a wave function which allows to extend space-time beyond classical singularities. One main question is how these homogeneous scenarios are related to full loop quantum gravity, which can be dealt with at the level of distributional symmetric states. Finally, the new structure of space-time arising in loop quantum gravity and its application to cosmology sheds new light on more general issues such as time.Comment: 104 pages, 10 figures; online version, containing 6 movies, available at http://relativity.livingreviews.org/Articles/lrr-2005-11

Meta-analysis of nature conservation values in Asia & Oceania: Data heterogeneity and benefit transfer issues

We conduct a meta-analysis (MA) of around 100 studies valuing nature conservation in Asia and Oceania. Dividing our dataset into two levels of heterogeneity in terms of good characteristics (endangered species vs. nature conservation more generally) and valuation methods, we show that the degree of regularity and conformity with theory and empirical expectations is higher for the more homogenous dataset of contingent valuation of endangered species. For example, we find that willingness to pay (WTP) for preservation of mammals tends to be higher than other species and that WTP for species preservation increases with income. Increasing the degree of heterogeneity in the valuation data, however, preserves much of the regularity, and the explanatory power of some of our models is in the range of other MA studies of goods typically assumed to be more homogenous (such as water quality). Subjecting our best MA models to a simple test forecasting values for out-of-sample observations, shows median (mean) forecasting errors of 24 (46) percent for endangered species and 46 (89) percent for nature conservation more generally, approaching levels that may be acceptable in benefit transfer for policy use. We recommend that the most prudent MA practice is to control for heterogeneity in regressions and sensitivity analysis, rather than to limit datasets by non-transparent criteria to a level of heterogeneity deemed acceptable to the individual analyst. However, the trade-off will always be present and the issue of acceptable level of heterogeneity in MA is far from settle