AI News, Advanced Topics on DTM: Empirical Studies on AI artificial intelligence
Reflecting trends in the academic landscape of sustainable energy using probabilistic topic modeling
The selected LDA model with 300 topics provides indications on the strongest topic trends, inter-topic distances or general thematic areas, and topic communities in the research field dedicated to sustainable energy.
The patterns recognizable in the results are starting points for various interpretations that emerge when relating these patterns to or underlining them with selected literature, including but not limited to some of the articles reviewed in the “Background” section.
For example, for electricity generation, the discourse clearly focuses on photovoltaic and wind energy, whereas highly cited reviews dedicated to sustainable energy still discuss conventional options [32,33,34,35].
Interestingly, regarding fuel cells, the discourse tends to associate them with biofuels rather than hydrogen, which indicates that a comparably low realization potential is ascribed to the vision of a hydrogen economy [33, 57,58,59,60,61,62].
Regarding blind spots, this study validates the concerns that have been anticipated already, e.g., regarding the availability of material resources for energy transitions [81, 149,150,151], and points out a lack of attention to the role and structure of different end-use sectors.
Acknowledging the various conceptualizations of sustainability [13, 14, 155,156,157,158,159], this study focuses on selected elements: justice between generations, societal sub-systems, levels or scales, and the operational principles of strong sustainability.
If the trends identified via topic modeling became reality, the future energy system would be highly electrified using photovoltaic and wind energy but would also intensively make use of bioenergy (compare: cluster 4 in Fig.
Furthermore, topics on nuclear energy or fossil power plants, e.g., in combination with CCSU, which are part of several reviews addressed in the “Background” section [32,33,34,35], did not emerge from this study as prominent topics (compare: cold topics 225, 10 in Table 3).
The discourse seems to perceive the basic renewable energy conversion as a mastered task and now traverses on the learning curve to a phase focusing on optimization (compare: topic 90 in Table 1, community C in Fig.
While advancing conversion technologies with low direct risks, long-term or latent risks connected to the upstream or downstream energy system stages might require greater attention and accompanying strategies.
This study shows that current research already deals with advancing material properties in the production phase and, to a certain extent, considers life cycle assessment in connection with biomass from microalgae and building materials (compare: community A and community C in Fig.
The research on battery or supercapacitor technologies advances crucial elements of future electric grids, which need to be capable of integrating and balancing fluctuating renewable energy generation at large scale.
Concerning the assumption that batteries might precede fuel cells in the near future [35, 39], the results of this study point in the same direction (compare: rank of hot topics 57 and 13 in Table 1).
In this field, digitalization is expected to have a net positive effect regarding climate change mitigation  and promises further improvements for planning, operating, and managing energy systems including the various end-use sectors [162,163,164,165,166,167,168,169,170].
However, potential negative effects need to be taken into account such as rebound effects, e.g., in the transportation sector [167, 171], or socio-economic concerns regarding the replacement of human labor by machines .
Further, in the context of increasing decentralization, the declining attention to international trade, e.g., in global energy markets, appears logical (compare: cold topics 104, 185 in Table 3).
However, considering the uneven global distribution of material resources and possible political or economic tensions, the international resource markets might become a decisive factor for developing the future energy infrastructure and, as this and other studies highlight, require increased attention [175, 176].
This and other studies show that systemic or behavioral energy-saving potentials that do not primarily stem from advancing individual technologies have received comparably low attention, e.g., traffic planning, improving public transport systems, or increasing vehicle occupancy [34, 97, 161] (compare: no corresponding links in community D in Fig.
The majority of energy-intensive industries receive comparably low attention, except the cement industry and, to a certain extent, the manufacturing industry (compare: hot topic 125 in Table 1, community D in Fig.
4 (compare: top of cluster 2) directly refers to another industrial branch, i.e., the manufacturing industry, but is rather connected to the micro-level of energy-efficient machines than to a meso- or macro-perspective as applied, e.g., in the field of industrial ecology [181, 182] or circular economy [183, 184].
These observations emphasize that future research will have to understand better the different industrial sectors and their interactions with the energy system for leveraging decarbonization potentials and for supporting the industrial transition towards sustainability.
This proposal is in line with other studies calling for a more integrated perspective on sustainable product-service systems that consider the interplay of consumers, i.e., the users of energy-consuming products, with the phases of product design, manufacturing, and recycling .
This study indicates that research on sustainable energy is navigating towards a technology-oriented perspective (compare: hot topics in Table 1) and is moving away from the normative concepts connected to sustainability and sustainable development that have initially motivated this research field (compare: cold topics in Table 3).
The physical availability of raw materials might not be the major bottleneck in the near future for establishing a low-carbon economy, whereas environmental, social, and economic issues of resource extraction seem to be more relevant .
The consideration of operational aspects of transition processes involving these systems indicates an action-oriented research agenda, which matches the scholarly tradition of transition management for socio-technical systems (STS) [186, 187].
Considering the increasing urbanization trend [191, 192], the abundance of topics dealing with urban areas seems reasonable for addressing critical sustainability problems that affect a high share of the global population.
In research on sustainable energy, this kind of global perspective seems to fade and might be reinforced by connecting the various available local perspectives in order to support solutions to sustainability problems, e.g., via intergovernmental cooperation and regulation.
In contrast, the presence of the principle of sufficiency, which favors foregoing consumption instead of only optimizing it, is not as clear, although the literature has highlighted it as a necessary companion of efficiency [41, 42, 172, 193].
In summary, this indicates that research on sustainable energy has not yet integrated all the principles in a balanced way that are necessary for heading towards a steady-state-economy that is not governed by a paradigm of growth but of (sustainable) development .
CS+: CompSci Projects Beyond the Classroom
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The naturally occurring drift of the biological sample during the exposure -caused by beam-induced motion- is known to negatively impact image resolution when a simple average of the frames is calculated.
Motivated by recent advances in deep neural network approaches for natural image super-resolution and burst photography techniques that harness natural hand tremor on smartphone cameras, we seek to apply these methods to improve the resolution of cryo-EM images of proteins.
Outcomes: After completing this project, participants will have acquired experience applying modern machine learning and image processing techniques to an alluring and challenging research area in computational structural biology.