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Wind farms through the lens of sustainability and circularity: Integrating environmental, economic, and social dimensions
No full textThe development of a sustainable energy transition is one of the strategic objectives in Europe, and wind energy plays a key role, as its production capacity has increased significantly in recent decades. However, to verify that wind farm projects are indeed sustainable, it is necessary to apply appropriate methodologies to assess the three pillars of sustainable development: environmental, social and economic. In addition, a comparison with traditional energy resources of fossil origin is necessary, seeking to identify the benefits and challenges associated with these renewable energy alternatives, as well as the study of how wind farms adhere circular economy principles. The idea of this analysis is to avoid past mistakes, such as the depletion of essential resources, for example the depletion of rare elements, used for the construction of renewable energy facilities. It is in this framework that this comprehensive and critical review is developed, with the aim of providing information on the actual production of wind energy in the European context, its potential environmental benefits and effects, the socio-economic constraints and benefits that wind farm projects could bring, as well as the gaps and challenges identified in the value chain. It is hoped that this critical review can be considered as a guide for policy makers, researchers and stakeholders on the main constraints that could slow down wind energy technologies, on the environmental footprint of wind farms and its comparison with fossil energy, on the potentialities of wind projects to increase employment opportunities and economic growth, and on the main concerns of social communities
Woulda shoulda coulda?: The impact of predictive, prescriptive, and prospective expectations on stakeholder reactions
No full textWhen and why might stakeholders react to firm activities in ways that might be different than, or even contradictory to, what we might expect based on the extant research? We draw on expectancy violation theory (EVT) and bring in the notion of heuristics and future-oriented expectations to examine this question, using a sample of investor reactions to earnings surprises from 2013 to 2019. We find that, in addition to comparing earnings to consensus earnings estimates, investors appear to compare the earnings surprises to the firm’s past performance and to its peers. Importantly, their expectations regarding future interactions with the firm appear to shape their decisions and generate anticipatory reactions despite a lack of full certainty about the future, a point notably absent from the EVT literature so far, which has tended to be reactive. Numerous robustness checks and post hoc analyses indicate that this behavior is not necessarily driven by unsophisticated investors, as initially predicted, but seemingly by institutional investors who rely on these multiple expectations, even though that may not be entirely rational. Our theorizing and findings make several contributions to the EVT literature and offer practical insights for managers and investors
Explainable prediction of the mechanical properties of composites with CNNs
No full textComposites are amongst the most important materials
manufactured today, as evidenced by their use in countless applications. In order to establish the suitability of composites in specific applications, finite element (FE) modelling, a numerical method based on partial differential equations, is the industry standard for assessing their mechanical properties. However, FE modelling is exceptionally costly from a computational viewpoint, a limitation which has led to efforts towards applying AI models to this task. However, in these approaches: the chosen model architectures were rudimentary, feed-
forward neural networks giving limited accuracy; the studies focus on predicting elastic mechanical properties, without considering material strength limits; and the models lacked transparency, hindering trustworthiness by users. In this paper, we show that convolutional neural networks (CNNs) equipped with methods from explainable AI (XAI) can be successfully deployed to solve this problem. Our
approach uses customised CNNs trained on a dataset we generate using transverse tension tests in FE modelling to predict composites’ mechanical properties, i.e., Young’s modulus and yield strength. We show empirically that our approach achieves high accuracy, outperforming a baseline, ResNet-34, in estimating the mechanical properties. We then use SHAP and Integrated Gradients, two post-hoc
XAI methods, to explain the predictions, showing that the CNNs use the critical geometrical features that influence the composites’ behaviour, thus allowing engineers to verify that the models are trust-worthy by representing the science of composites
In-situ investigation of discontinuous precipitation in Sn-Bi low temperature solders
No full textThe mechanisms of precipitation in near-eutectic Sn-Bi alloys have been studied with in-situ electron backscatter diffraction (EBSD) and compared with microstructures in solder balls. Time-lapse SEM imaging revealed the occurrence of discontinuous precipitation (DP) with a two-phase front advancing into supersaturated β-Sn as either columnar or equiaxed cells. In-situ EBSD provided evidence that the migrating front changes the orientation of the tin phase and establishes a specific orientation relationship (OR) between the (Bi) lamellae and advancing β-Sn. In solder balls that solidified with multiple tin dendrites, DP initiated at tin grain boundaries and grew into the dendrites, changing part of their orientation. In solder balls with a single tin dendrite, the tin orientation change involved a change in OR or orientation variant. Thus, DP can increase the number of tin orientations and create tin grain boundaries which are known sites of stress localisation during thermal cycling of electronic components
Argumentatively coherent judgmental forecasting
No full textJudgmental forecasting employs human opinions to make predictions about future events, rather than exclusively historical data as in quantitative forecasting. When these opinions form an argumentative structure around forecasts, it is useful to study the properties of the forecasts from an argumentative perspective. In this paper, we advocate and formally define a property of argumentative coherence, which, in essence, requires that a forecaster’s reasoning is
coherent with their forecast. We then conduct three evaluations with our notion of coherence. First, we assess the impact of enforcing coherence on human forecasters as well as on Large Language Model (LLM)-based forecasters, given that they have recently shown to be competitive with human forecasters. In both cases, we show that filtering out incoherent predictions improves forecasting accuracy consistently, supporting the practical value of coherence in both human and LLM-based forecasting. Then, via crowd-sourced user experiments, we show that, despite its apparent intuitiveness and usefulness, users do not generally align with this coherence property. This points to the need to integrate, within argumentation-based judgmental forecasting, mechanisms to filter out incoherent opinions before obtaining group forecasting predictions
Quantifying the influence of soiling on PV module energy production based on a coupled opto-thermal-electrical model: an international assessment in different climatic conditions
No full textAccording to the IEA, soiling on a global scale has been identified as the second most significant factor affecting the energy production of photovoltaic (PV) systems after solar irradiance. Soiling directly affects the transmittance of PV glass and also disrupts the thermal balance (heat flows) within PV modules, thus impacting both the amount of solar irradiance received by and the temperature of the cells. In this work, we develop and validate a coupled opto-thermal-electrical model capable of assessing the impact of soiling on PV module performance. The model goes beyond other models, commercial and in the literature, by accounting explicitly for the effects of soiling on the optical transmission and the thermal balance through PV modules, while also being capable of accounting for electrical output limitations caused by non-uniform soiling. The validated model, which is shown to predict experimentally obtained losses due to soiling with a RMSE of 6%, is then used to investigate the impact of natural soiling on yearly PV system energy production while considering the local weather conditions at different geographical locations (Oman, Nigeria, Iran, Indonesia, Australia and Spain), as well as the module tilt angle and module cleaning frequency. For a monthly cleaning frequency, and thus exposure period, maximum yearly soiling losses (relative to modules kept continuously clean) of between 5% in Nigeria and 18% in Oman are obtained for horizontally oriented modules. For a seasonal cleaning frequency, the yearly losses can reach values up to 27% in Nigeria and 32% in Oman, with the lowest predicted loss being 12% in Australia. Based on the results, it is concluded that soiling losses can be significant (>30% for horizontal, but also >20% for optimally-tilted modules, in the worst case), and that the cleaning frequency can have a significant effect on yearly PV energy production, motivating the development of improved soiling mitigation practices
Short communication: Learning how landscapes evolve with neural operators
No full textThe use of Fourier Neural Operators (FNOs) to learn how landscapes evolve is introduced. The approach makes use of recent developments in deep learning to learn the processes involved in evolving landscapes (e.g., erosion). An example is provided in which FNOs are developed using input–output pairs (elevations at different times) in synthetic landscapes generated using the stream power model (SPM). The SPM takes the form of a non-linear partial differential equation that advects slopes headwards. The results indicate that the learned operators can reliably and very rapidly predict subsequent landscape evolution at large scales. These results suggest that FNOs could be used to rapidly predict landscape evolution without recourse to the (slow) computation of flow routing and time stepping needed when generating numerical solutions to the SPM. More broadly, they suggest that neural operators could be used to learn the processes that evolve actual and analogue landscapes. Interesting future work could involve assessment of whether learned operators can be applied to other settings or model parametrizations
Archaea produce peptidoglycan hydrolases that kill bacteria
No full textThe social life of archaea is poorly understood. In particular, even though competition and conflict are common themes in microbial communities, there is scant evidence documenting antagonistic interactions between archaea and their abundant prokaryotic brethren: bacteria. Do archaea specifically target bacteria for destruction? If so, what molecular weaponry do they use? Here, we present an approach to infer antagonistic interactions between archaea and bacteria from genome sequence. We show that a large and diverse set of archaea encode peptidoglycan hydrolases, enzymes that recognize and cleave a structure—peptidoglycan—that is a ubiquitous component of bacterial cell walls but absent from archaea. We predict the bacterial targets of archaeal peptidoglycan hydrolases using a structural homology approach and demonstrate that the predicted target bacteria tend to inhabit a similar niche to the archaeal producer, indicative of ecologically relevant interactions. Using a heterologous expression system, we demonstrate that two peptidoglycan hydrolases from the halophilic archaeaon Halogranum salarium B-1 kill the halophilic bacterium Halalkalibacterium halodurans, a predicted target, and do so in a manner consistent with peptidoglycan hydrolase activity. Our results suggest that, even though the tools and rules of engagement remain largely unknown, archaeal-bacterial conflicts are likely common, and we present a roadmap for the discovery of additional antagonistic interactions between these two domains of life. Our work has implications for understanding mixed microbial communities that include archaea and suggests that archaea might represent a large untapped reservoir of novel antibacterials
On gradual semantics for assumption-based argumentation
No full textIn computational argumentation, gradual semantics are fine-
grained alternatives to extension-based and labelling-based
semantics. They ascribe a dialectical strength to (components of) arguments sanctioning their degree of acceptability. Several gradual semantics have been studied for abstract, bipolar and quantitative bipolar argumentation frameworks (QBAFs), as well as, to a lesser extent, for some forms of structured argumentation. However, this has not been the case for assumption-based argumentation (ABA), despite it being a popular form of structured argumentation with several applications where gradual semantics could be useful. In this paper, we fill this gap and propose a family of novel gradual semantics for equipping assumptions, which are the core components in ABA frameworks, with dialectical strengths. To do so, we use bipolar set-based argumentation frame-works as an abstraction of (potentially non-flat) ABA frame-works and generalise state-of-the-art modular gradual semantics for QBAFs. We show that our gradual ABA semantics satisfy suitable adaptations of desirable properties of gradual QBAF semantics, such as balance and monotonicity. We also explore an argument-based approach that leverages established QBAF modular semantics directly, and use it as base-line. Finally, we conduct experiments with synthetic ABA frameworks to compare our gradual ABA semantics with its argument-based counterpart and assess convergence
