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Forest trees dominate many Alpine landscapes that are currently exposed to changing climate. Norway spruce is one of the most important conifer species of the Italian Alps, and natural populations are found across steep environmental gradients with large differences in temperature and moisture availability. This study seeks to determine and quantify patterns of genetic diversity in natural populations toward understanding adaptive responses to changing climate. Across the Italian species range, 24 natural stands were sampled with a major focus on the Eastern Italian Alps. Sampled trees were genotyped for 384 selected single nucleotide polymorphisms (SNPs) from 285 genes. A wide array of potential candidate genes was tested for correlation with climatic parameters. To minimize false-positive association between genotype and climate, population structure was investigated. Pairwise F ST estimates between sampled populations ranged between 0.000 and 0.075, with the highest values involving the two disjoint populations, Valdieri, on the western Italian Alps, and Campolino, the most southern population on the Apennines. Despite considerable genetic admixture among populations, both Bayesian and multivariate approach identified four genetic clusters. Selection scans revealed five F ST outliers, and the environmental association analysis detected ten SNPs associated to one or more climatic variables. Overall, 13 potentially adaptive loci were identified, three of which have been reported in a previous study on the same species conducted on a broader geographical scale. In our study, precipitation, more than temperature, was often associated with genotype; therefore, it appears as the most important environmental variable associated with the high sensitivity of Norway spruce to soil water supply. These findings provide relevant information for understanding and quantifying climate change effects on this species and its ability to genetically adapt.

Reduced graphene oxide (rGO) has been prepared from commercial graphene oxide by a thermal reduction method, and NiO/rGO photocathodes were obtained by a mixed NiCl2/rGO sol–gel process by using di...

Synchronous electrostatic harvesters are able to deliver a higher amount of power to electronic devices compared to the asynchronous electrostatic harvesters. However, synchronous generators need a control circuit for synchronising the electrical switching events with the energy source frequency. Asynchronous electrostatic generators have the advantage of omitting this control circuit resulting in a simplified implementation and eliminates the power consumption related to this part. In this paper, the performance of a typical circuit based on each structure is explored and the optimal parameters for the asynchronous circuit are presented. It is shown mathematically and verified in simulation that the maximum generated power in the asynchronous circuit is equal to 50% of the generated power in the synchronous circuit considering the conduction losses.

Abstract The rapid development of distributed generation in different forms and capacities is transforming the conventional planning of distribution networks. Despite the benefits offered by renewable distributed generation technologies, several economic and technical challenges can result from the inappropriate integration of distributed generation in existing distribution networks. Therefore, the optimal planning of distributed generation is of paramount importance to ensure that the performance of distribution network can meet the expected power quality, voltage stability, power loss reduction, reliability and profitability. In this paper, we firstly discuss several conventional and metaheuristic methodologies to address the optimal distributed generation planning problem. Metaheuristic algorithms are often used as they offer more flexibility, particularly for multi-objective planning problems without the pursuit of globally optimized solution. Analytical techniques are considered suitable for modeling power system mechanisms and validating numerical methods. Then, this paper conducts a comprehensive review and critical discussion of state-of-the-art analytical techniques for optimal planning of renewable distributed generation. The analytical techniques are discussed in detail in six categories, i.e. exact loss formula, loss sensitivity factor, branch current loss formula, branch power flow loss formula, equivalent current injection and phasor feeder current injection. In addition, a comparative analysis of analytical techniques is presented to show their suitability for distributed generation planning in terms of various optimization criteria. Finally, we present conclusive remarks along with a set of recommendations and future challenges for optimal planning of distributed generation in modern power distribution networks.

Sorption to "hard carbon" (black carbon, coal, kerogen) in soils and sediments is of major importance for risk assessment of organic pollutants. We argue that activated carbon (AC) may be considered a model sorbent for hard carbon. Here, we evaluate six sorption models on a literature dataset for sorption of 12 compounds onto 12 ACs and one charcoal, at different temperatures (79 isotherms in total). A statistical analysis, accounting for differences in the number of fitting parameters, demonstrates that the dual Langmuir equation is in general superior and/or preferable to the single and triple Langmuir equation, the Freundlich equation, a Polanyi-Dubinin-Manes equation, and the Toth equation. Consequently, the analysis suggests the presence of two types of adsorption sites: a high-energy (HE) type of site and a low-energy (LE) type of site. Maximum adsorption capacities for the HE domain decreased with temperature while those for the LE domain increased. Average Gibbs free energies for adsorption from the hypothetical pure liquid state at 298 K were fairly constant at -15+/-4 and -5+/-4 kJ mol(-1) for the HE and LE domain, respectively.

Energy efficiency has long been considered a key component of an industrial company's competitive repertoire. However, despite the potential benefits of adopting so-called energy efficiency measures, their uptake in such companies remains low. In response, this study proposes a framework aimed at supporting key decision-makers in undertaking a thorough assessment of energy efficiency measures. This involves, on the one hand, providing a complete characterization of a general industrial energy efficiency measure and, on the other, identifying the multiple impacts stemming from its adoption based on a novel performance measurement system that encompasses sustainability features and is defined at the shop floor level. Once theoretically validated through literature, the framework is empirically tested with a heterogeneous sample of Italian companies. The preliminary results demonstrate the framework's ability to thoroughly assess energy efficiency measures, highlighting characteristics and impacts that are sometimes considered more critical than energy saving by industrial decision-makers and therefore able to guide the outcome of the adoption decision.

Over the past several years, online disinformation and misinformation concerning climate change have gained substantive attention within the scientific community. However, while the dynamics that drive the circulation of false online information have been analysed extensively, it remains unclear whether (and how) this phenomenon can be counteracted. This research project analyses the emerging role of bottom-up mobilisations as a form of noise-reduction, thereby examining how social movements may deploy peer-produced communication narra- tives to counteract the circulation of online disinformation and misinformation relating to climate change. To investigate this communication dynamic, this research applies techniques from computational social sciences to an original dataset of ≈ 250k Facebook posts produced by two movements that best embody this novel and innovative generation of radical envi- ronmental activism: Extinction Rebellion and Fridays for Future. The central thesis of this project forwards two original contributions to the fields of climate change communication and social movement studies. First, it analyses the emergence of a new generation of radical climate change movements and the significance of this new development in climate activism (Chapter II). Second, it offers interdisciplinary empirical evidence on how radical climate movements can act as a bottom-up force for what I term ‘epistemic activism’. It presents a theoretical framework where activist-led, peer-produced communication can provide a coun- tering force to both vertical disinformation and horizontal misinformation. It quantitatively analyses two channels through which these forms of false information can be opposed. For reducing vertical disinformation, this work assesses the use of naming and shaming against information polluters (Chapter III), while for horizontal misinformation, it evaluates the dissemination of scientific counter-narratives (Chapter IV). Ultimately, this thesis shows that the two movements under analysis engage extensively in epistemic activism, with great potential to influence the online climate change debate positively.

Electrical power systems have been traditionally designed to be reliable during normal conditions and abnormal but foreseeable contingencies. However, withstanding unexpected and less frequent severe situations still remains a significant challenge. As a critical infrastructure and in the face of climate change, power systems are more and more expected to be resilient to highimpact low-probability events determined by extreme weather phenomena. However, resilience is an emerging concept, and, as such, it has not yet been adequately explored in spite of its growing interest. On these bases, this paper provides a conceptual framework for gaining insights into the resilience of power systems, with focus on the impact of severe weather events. As quantifying the effect of weather requires a stochastic approach for capturing its random nature and impact on the different system components, a novel sequential Monte-Carlo-based time-series simulation model is introduced to assess power system resilience. The concept of fragility curves is used for applying weather-and time-dependent failure probabilities to system's components. The resilience of the critical power infrastructure is modeled and assessed within a context of system-of-systems that also include human response as a key dimension. This is illustrated using the IEEE 6-bus test system.

The increasing demand for temporary housing in many developing countries necessitate the use of sustainable and affordable construction options. Earthbag units have the potential to be integrated into such housings as they are inexpensive, sustainable, and straightforward material options for building structures. Nevertheless, due to their thermal characteristics, earthbag units cannot provide a thermally comfortable environment. Thus, the present study focuses on developing an environmentally and sustainable earthbag unit integrated with phase change materials (PCM) to convert severely harsh indoor spaces to moderately harsh ones. For the design and development of earthbag blocks, several units are developed with varying amounts of PCM encapsulated in expanded perlite (EP) and expanded graphite (EG) within each unit, including block A (reference), Block B (PCM 2.2% of sample weight), C (4.3%), and D (6.5%). An experimental study is then conducted to understand the microstructural properties of the embedded PCM composite in soil. Following this initial study, practical differential techniques, including differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), scanning electron microscope (SEM), thermal conductivity, and Oozing circle test, have been employed over the developed units to measure their thermal characteristics. Test results from DSC and TGA show good thermal stability of PCM and PCM composites, while SEM results indicated that PCM is well distributed within the pores of EP at 50%EP of the PCM weight. The study found the average indoor surface temperatures by block B, block C, and block D to drop compared to the reference block about 1.2 °C, 3.3 °C, and 4.1 °C, respectively. This clearly shows the benefit of integrating phase change materials in an earthbag unit.